1var e={strategy:{slug:`strategy-practice`,name:`pronix.ai Strategy Practice`,role:`Enterprise AI & CX advisory`,practice:`Strategy`,bio:`Our Strategy Practice is a small team of former CIOs, transformation partners and product leaders who help executive teams turn AI ambition into a delivery plan. We write for the boardroom, sit in the war room, and stay through go-live.`,expertise:[`AI operating model & governance`,`Buy-vs-build frameworks`,`CCaaS platform selection`,`Executive readiness workshops`],based:`New York · Hyderabad`},cx:{slug:`cx-engineering`,name:`pronix.ai CX Engineering`,role:`Contact Center AI architects`,practice:`CX Engineering`,bio:`The CX Engineering practice designs and ships voice AI, agent assist, conversational AI and analytics on Amazon Connect, Genesys, NICE, Five9, Salesforce Agentforce, Google CCAI and Dynamics 365. We measure ourselves on cutover risk and post-go-live CSAT, not deck pages.`,expertise:[`Voice AI & IVR modernization`,`Agent assist rollouts`,`Automated quality management`,`CCaaS migration & cutover`],based:`New York · Hyderabad`},finops:{slug:`finops-practice`,name:`pronix.ai FinOps Practice`,role:`AI cost & governance`,practice:`FinOps`,bio:`Our FinOps practice keeps enterprise AI spend defensible â unit economics, cost attribution, cheap-first routing, guardrails and monthly governance rhythms. We work alongside CIOs and CFOs to keep the AI portfolio in the black.`,expertise:[`LLM cost attribution`,`Cheap-first cascade routing`,`Budget guardrails & alerting`,`Vendor & model portfolio review`],based:`New York · Hyderabad`}},t=e,n=Object.values(e);function r(e){return n.find(t=>t.slug===e)}var i=[{slug:`large-us-health-insurer-agentic-claims`,capabilities:[`Agent Assist`,`Conversational AI`,`RAG / Knowledge AI`,`AI Ops / Analytics`],title:`Cutting claim-triage cycle time by 62% at a large US health insurer`,seoTitle:`Case Study â Agentic Claims Triage at a Large US Health Insurer â pronix.ai`,metaDescription:`How pronix.ai cut claim-triage cycle time 62% and tripled throughput for a large US health insurer â delivered on Amazon Bedrock and Kore.ai.`,client:`Large US health insurer`,industry:`Health Payers`,platform:`Amazon Bedrock · Kore.ai · Salesforce Health Cloud`,eyebrow:`Case study · Health Payers · Agentic AI`,summary:`A large US health insurer was drowning in claim-triage backlog during open enrollment. pronix.ai designed and shipped an agentic triage workflow that took cycle time from 11 minutes to under 4, tripled throughput, and stayed inside healthcare and CMS auditability guardrails throughout.`,readTime:`9 min read`,updated:`Q4 2025`,audience:[`VP Claims`,`Chief AI Officer`,`CIO`,`Head of Digital Ops`],outcomes:[{metric:`-62%`,label:`Cycle time`},{metric:`3.4x`,label:`Triage throughput`},{metric:`$14.2M`,label:`Annualized savings`},{metric:`0`,label:`healthcare / CMS audit findings`}],related:[`national-retailer-amazon-connect-cutover-90-days`,`global-bpo-agent-assist`,`large-national-insurer-llm-finops`],__author:`strategy`},{slug:`national-retailer-amazon-connect-cutover-90-days`,capabilities:[`Agent Assist`,`Conversational AI`,`Contact Center Migration`],title:`90-day cutover from legacy IVR to Amazon Connect at a national retailer`,seoTitle:`Case Study â 90-Day Amazon Connect Cutover for a National Retailer â pronix.ai`,metaDescription:`How pronix.ai cut over a national retailer from legacy IVR in 90 days, held CSAT flat and cut AHT 28% â delivered on Amazon Connect.`,client:`National specialty retailer`,industry:`Retail & Ecommerce`,platform:`Amazon Connect · Amazon Q · Salesforce Service Cloud`,eyebrow:`Case study · Retail · CCaaS migration`,summary:`A national specialty retailer had 90 days to get off a decade-old Avaya IVR before their peak holiday window â and wanted agent assist live on day one. pronix.ai ran the cutover on Amazon Connect with Amazon Q for agent assist, held CSAT flat through peak, and cut AHT by 28%.`,readTime:`8 min read`,updated:`Q4 2025`,audience:[`VP Contact Center`,`CX Platform Owner`,`CIO`],outcomes:[{metric:`90 days`,label:`Legacy IVR to live on Amazon Connect`},{metric:`-28%`,label:`AHT`},{metric:`+1.4pt`,label:`CSAT through peak`},{metric:`0`,label:`Missed peak days`}],related:[`large-us-health-insurer-agentic-claims`,`global-bpo-agent-assist`,`large-national-insurer-llm-finops`],__author:`cx`},{slug:`global-bpo-agent-assist`,capabilities:[`Agent Assist`,`RAG / Knowledge AI`,`AI Ops / Analytics`],title:`Agent assist across 1,200 BPO seats â a co-sell success`,seoTitle:`Case Study â Agent Assist at 1,200 BPO Seats â pronix.ai`,metaDescription:`How pronix.ai cut AHT 22% and lifted QA 14% across 1,200 BPO seats â delivered on NICE CXone, Kore.ai and Azure AI Foundry.`,client:`Global BPO`,industry:`BPO`,platform:`NICE CXone · Kore.ai · Azure AI Foundry`,eyebrow:`Case study · BPO · Agent assist`,summary:`A global BPO wanted to introduce agent assist across three of their largest client programs without disturbing the existing SLA structure. pronix.ai delivered the platform, the change program and the client co-sell motion â 22% AHT reduction, 14% QA lift, and a repeatable playbook the BPO now sells into their own book.`,readTime:`7 min read`,updated:`Q3 2025`,audience:[`BPO COO`,`VP Delivery`,`Client Partner`],outcomes:[{metric:`-22%`,label:`AHT`},{metric:`+14%`,label:`QA scores`},{metric:`3`,label:`New client wins attributed to the capability`},{metric:`6 wk`,label:`Time to first live program`}],implementationSummary:{href:`/downloads/case-studies/global-bpo-agent-assist-implementation-summary.pdf`,pages:2,sizeKb:6},related:[`large-us-health-insurer-agentic-claims`,`national-retailer-amazon-connect-cutover-90-days`,`large-national-insurer-llm-finops`],__author:`cx`},{slug:`large-national-insurer-llm-finops`,capabilities:[`AI Ops / Analytics`],title:`Cutting LLM spend 41% at a large national insurer`,seoTitle:`Case Study â LLM FinOps at a Large National Insurer â pronix.ai`,metaDescription:`How pronix.ai cut LLM spend 41% across 40+ workloads at a large national insurer with no quality regression â delivered on Azure and Bedrock.`,client:`Large national insurer`,industry:`Insurance`,platform:`Azure AI Foundry · Amazon Bedrock · Kore.ai`,eyebrow:`Case study · Insurance · FinOps`,summary:`A large national insurer had 40+ AI workloads across Azure and Bedrock with no unified cost view, no routing controls and a spend curve that was going to cross eight figures inside 18 months. pronix.ai stood up an LLM FinOps program that cut spend 41% with
1no measurable quality regression.`,readTime:`8 min read`,updated:`Q4 2025`,audience:[`Head of FinOps`,`CIO`,`Chief AI Officer`,`VP AI Platform`],outcomes:[{metric:`-41%`,label:`Total LLM spend`},{metric:`0`,label:`Quality regressions on golden set`},{metric:`100%`,label:`Workloads with attributed cost`},{metric:`3 wk`,label:`Time to first defensible spend report`}],related:[`large-us-health-insurer-agentic-claims`,`national-retailer-amazon-connect-cutover-90-days`,`global-bpo-agent-assist`],__author:`finops`},{slug:`healthcare-provider-kore-ai-agent-platform`,capabilities:[`Conversational AI`,`AI Ops / Analytics`],title:`38% call containment in 90 days for a regional healthcare provider â healthcare-safe conversational AI`,seoTitle:`Case Study â Kore.ai Agent Platform for a Healthcare Provider â pronix.ai`,metaDescription:`How pronix.ai achieved 38% call containment in 90 days for a regional healthcare provider â delivered on Kore.ai with Epic integration.`,client:`Regional healthcare provider`,industry:`Healthcare Providers`,platform:`Kore.ai Agent Platform · Epic · Amazon Connect`,eyebrow:`Case study · Healthcare · Conversational AI`,summary:`A regional healthcare provider needed to contain rising call volume for scheduling, refills and pre-visit intake without adding agents. pronix.ai deployed Kore.ai Agent Platform virtual agents on voice and chat with Epic integration, PHI guardrails and HITL escalation â 38% containment in 90 days with CSAT flat.`,readTime:`8 min read`,updated:`Q1 2026`,audience:[`VP Patient Access`,`CIO`,`Chief Digital Officer`],outcomes:[{metric:`38%`,label:`Tier-1 call containment`},{metric:`-24%`,label:`Average handle time on transferred calls`},{metric:`+0.3pt`,label:`Patient CSAT`},{metric:`0`,label:`healthcare audit findings`}],related:[`large-us-health-insurer-agentic-claims`,`omnichannel-conversational-ai-large-healthcare`,`kore-ai-contact-center-ai-healthcare`],__author:`cx`},{slug:`pc-insurer-amazon-connect-migration`,capabilities:[`Agent Assist`,`Conversational AI`,`Contact Center Migration`],title:`1,800-agent contact center cutover in six months at a P&C insurer â zero claims disruption`,seoTitle:`Case Study â Amazon Connect Migration for a P&C Insurer â pronix.ai`,metaDescription:`How pronix.ai cut over a large agent team in six months with zero CAT-season disruption â delivered on Amazon Connect and Guidewire.`,client:`Mid-market P&C insurer`,industry:`Insurance`,platform:`Amazon Connect · Amazon Q · Guidewire ClaimCenter`,eyebrow:`Case study · Insurance · CCaaS migration`,summary:`A P&C insurer needed off legacy Avaya before CAT season and wanted agent assist, screen pop from Guidewire and callback orchestration on day one. pronix.ai cut over the full agent team to Amazon Connect in six months with zero major CAT-season disruption.`,readTime:`9 min read`,updated:`Q1 2026`,audience:[`VP Claims`,`Contact Center Director`,`CIO`],outcomes:[{metric:`6 months`,label:`Legacy Avaya to Amazon Connect, multiple sites`},{metric:`-31%`,label:`AHT on FNOL calls`},{metric:`0`,label:`Missed CAT-season SLA days`},{metric:`$4.1M`,label:`Annualized run-rate savings`}],related:[`national-retailer-amazon-connect-cutover-90-days`,`large-national-insurer-llm-finops`,`large-us-health-insurer-agentic-claims`],__author:`cx`},{slug:`large-retailer-kore-ai-agent-platform`,capabilities:[`Conversational AI`,`RAG / Knowledge AI`,`AI Ops / Analytics`],title:`41% self-service containment for a national retailer â order status and returns on conversational AI`,seoTitle:`Case Study â Kore.ai Agent Platform at a Large Retailer â pronix.ai`,metaDescription:`How pronix.ai delivered 41% self-service containment for a national retailer â delivered on Kore.ai, Genesys Cloud and Salesforce.`,client:`National retailer`,industry:`Retail & Ecommerce`,platform:`Kore.ai Agent Platform · Salesforce Commerce Cloud · Genesys Cloud`,eyebrow:`Case study · Retail · Conversational AI`,summary:`A national retailer wanted to contain repetitive order-status and returns calls without hurting the high-touch loyalty experience. pronix.ai deployed Kore.ai Agent Platform on voice and chat over Genesys Cloud with Salesforce Commerce integration â 41% containment and CSAT up half a point.`,readTime:`8 min read`,updated:`Q1 2026`,audience:[`VP Contact Center`,`Head of Digital`,`CX Platform Owner`],outcomes:[{metric:`41%`,label:`Containment on top-3 intents`},{metric:`+0.5pt`,label:`CSAT`},{metric:`-19%`,label:`AHT on escalated calls`},{metric:`12 wk`,label:`First intent live to peak-ready`}],related:[`national-retailer-amazon-connect-cutover-90-days`,`healthcare-provider-kore-ai-agent-platform`,`omnichannel-conversational-ai-large-healthcare`],__author:`cx`},{slug:`life-sciences-genesys-cloud-implementation`,capabilities:[`Agent Assist`,`Contact Center Migration`,`RAG / Knowledge AI`],title:`GxP-safe medical information contact center for a global life-sciences company â modernized in one quarter`,seoTitle:`Case Study â Genesys Cloud CX for a Life Sciences Company â pronix.ai`,metaDescription:`How pronix.ai modernized a medical information contact center in one quarter with 27% AHT reduction â delivered on Genesys Cloud and Veeva.`,client:`Global life-sciences company`,industry:`Life Sciences`,platform:`Genesys Cloud CX · Veeva Vault · Salesforce Health Cloud`,eyebrow:`Case study · Life Sciences · CCaaS implementation`,summary:`A global life-sciences company needed to modernize its medical information contact center without breaking GxP audit posture or adverse-event reporting SLAs. pronix.ai implemented Genesys Cloud CX with Veeva Vault integration and a validated deployment pipeline â 27% AHT reduction and clean audits.`,readTime:`9 min read`,updated:`Q1 2026`,audience:[`Head of Medical Information`,`CIO`,`Head of GxP Compliance`],outcomes:[{metric:`-27%`,label:`AHT on MedInfo calls`},{metric:`100%`,label:`AE reports within regulator SLA`},{metric:`0`,label:`GxP audit findings post go-live`},{metric:`5 mo`,label:`Discovery to global cutover`}],related:[`large-us-health-insurer-agentic-claims`,`pc-insurer-amazon-connect-migration`,`healthcare-provider-kore-ai-agent-platform`],__author:`strategy`},{slug:`investment-mgmt-five9-to-kore-ai-migration`,capabilities:[`Contact Center Migration`,`RAG / Knowledge AI`],title:`92 dialog flows migrated with zero client-facing outage at an investment management firm`,seoTitle:`Case Study â Five9 to K
1ore.ai Migration at an Investment Manager â pronix.ai`,metaDescription:`How pronix.ai migrated 92 dialog flows with zero outage and cut auth time 45s â delivered on Kore.ai and Genesys Cloud.`,client:`Investment management firm`,industry:`Financial Services`,platform:`Kore.ai Agent Platform · Genesys Cloud · Salesforce Financial Services Cloud`,eyebrow:`Case study · Financial Services · Platform migration`,summary:`An investment management firm needed off Five9 IVA before a licensing cliff and wanted its 92 dialog flows on Kore.ai Agent Platform without disturbing SEC/FINRA-audited call recording or advisor workflows. pronix.ai migrated in 14 weeks with zero client-facing outage.`,readTime:`8 min read`,updated:`Q1 2026`,audience:[`Head of Advisor Services`,`CIO`,`Head of Compliance`],outcomes:[{metric:`14 wk`,label:`Full cutover, 61 consolidated flows`},{metric:`0`,label:`Client-facing outage minutes`},{metric:`-45s`,label:`Advisor authentication AHT`},{metric:`100%`,label:`Recording continuity through cutover`}],related:[`pc-insurer-amazon-connect-migration`,`kore-ai-banking-conversational-ai`,`large-retailer-kore-ai-agent-platform`],__author:`strategy`},{slug:`kore-ai-contact-center-ai-healthcare`,capabilities:[`Agent Assist`,`RAG / Knowledge AI`,`AI Ops / Analytics`],title:`Agent assist at a large healthcare provider â 22% AHT reduction with healthcare guardrails`,seoTitle:`Case Study â Kore.ai Contact Center AI at a Healthcare Provider â pronix.ai`,metaDescription:`How pronix.ai reduced AHT 21% and after-call work 63% across the patient-access seat base â delivered with Kore.ai on Genesys Cloud.`,client:`Integrated healthcare provider`,industry:`Healthcare Providers`,platform:`Kore.ai Contact Center AI · Genesys Cloud · Epic`,eyebrow:`Case study · Healthcare · Agent assist`,summary:`A large healthcare provider wanted real-time agent coaching, next-best-action and auto-summary across the patient-access seat base â without asking agents to learn a new console. pronix.ai deployed Kore.ai Contact Center AI as an overlay on Genesys Cloud with Epic-aware summaries.`,readTime:`7 min read`,updated:`Q1 2026`,audience:[`VP Patient Access`,`Contact Center Director`,`CMIO`],outcomes:[{metric:`-21%`,label:`AHT`},{metric:`+18%`,label:`First-contact resolution`},{metric:`-63%`,label:`After-call work`},{metric:`900`,label:`Seats live in 10 weeks`}],related:[`healthcare-provider-kore-ai-agent-platform`,`omnichannel-conversational-ai-large-healthcare`,`global-bpo-agent-assist`],__author:`cx`},{slug:`omnichannel-conversational-ai-large-healthcare`,capabilities:[`Conversational AI`,`RAG / Knowledge AI`],title:`Omnichannel conversational AI at a large healthcare provider â one bot across voice, web, SMS and app`,seoTitle:`Case Study â Omnichannel Conversational AI at a Large Healthcare Provider â pronix.ai`,metaDescription:`How pronix.ai consolidated four bot stacks into one omnichannel AI with 44% containment â delivered on Kore.ai across voice, web, SMS and app.`,client:`Large integrated healthcare provider`,industry:`Healthcare Providers`,platform:`Kore.ai Agent Platform · Amazon Connect · Twilio · MyChart`,eyebrow:`Case study · Healthcare · Omnichannel CAI`,summary:`A large healthcare provider was maintaining four separate bot stacks across voice, web, SMS and the MyChart app â with drifting answers and duplicated compliance reviews. pronix.ai consolidated to a single omnichannel conversational AI on Kore.ai with 44% containment and one review cycle.`,readTime:`9 min read`,updated:`Q1 2026`,audience:[`Chief Digital Officer`,`CIO`,`VP Patient Experience`],outcomes:[{metric:`44%`,label:`Cross-channel containment`},{metric:`-70%`,label:`Compliance review cycles`},{metric:`1`,label:`Bot stack instead of four`},{metric:`+0.4pt`,label:`Patient digital CSAT`}],related:[`healthcare-provider-kore-ai-agent-platform`,`kore-ai-contact-center-ai-healthcare`,`large-retailer-kore-ai-agent-platform`],__author:`strategy`},{slug:`kore-ai-banking-conversational-ai`,capabilities:[`Conversational AI`,`RAG / Knowledge AI`,`AI Ops / Analytics`],title:`Retail banking self
1-service at a regional bank â conversational AI across servicing journeys`,seoTitle:`Case Study â Conversational AI for Banking on Kore.ai â pronix.ai`,metaDescription:`How pronix.ai moved 36% of retail banking calls to self-service â delivered on Kore.ai Agent Platform with Amazon Connect and Fiserv.`,client:`Regional bank`,industry:`Financial Services`,platform:`Kore.ai Agent Platform · Amazon Connect · Fiserv DNA`,eyebrow:`Case study · Banking · Conversational AI`,summary:`A regional bank wanted to move balance, transfer, card-controls and dispute-intake calls to self-service without hurting NPS or falling out of FFIEC posture. pronix.ai deployed Kore.ai Agent Platform on voice and mobile â 36% containment and clean audit.`,readTime:`8 min read`,updated:`Q1 2026`,audience:[`Head of Digital Banking`,`Head of Contact Center`,`CISO`],outcomes:[{metric:`36%`,label:`Containment on top-4 servicing intents`},{metric:`-52%`,label:`Dispute intake AHT`},{metric:`0`,label:`enterprise-security / FFIEC findings`},{metric:`+8`,label:`Digital NPS`}],related:[`investment-mgmt-five9-to-kore-ai-migration`,`large-retailer-kore-ai-agent-platform`,`ai-hr-conversational-ai-implementation`],__author:`strategy`},{slug:`ai-hr-conversational-ai-implementation`,capabilities:[`Conversational AI`,`RAG / Knowledge AI`,`Employee Experience`],title:`AI for HR â conversational AI for employee self-service at a large national employer`,seoTitle:`Case Study â AI for HR Conversational AI Implementation â pronix.ai`,metaDescription:`How pronix.ai deflected 58% of HR service-desk tickets for a workforce of tens of thousands â delivered on Kore.ai, Workday, ServiceNow and Teams.`,client:`Large national employer`,industry:`Enterprise`,platform:`Kore.ai Agent Platform · Workday · ServiceNow HRSD · Microsoft Teams`,eyebrow:`Case study · HR · Conversational AI`,summary:`A large national employer wanted to give tens of thousands of employees a single HR assistant across Teams and the intranet â policy Q&A, PTO, benefits and case creation grounded on the sanctioned policy set. pronix.ai delivered 58% ticket deflection from the HR service desk.`,readTime:`8 min read`,updated:`Q1 2026`,audience:[`CHRO`,`Head of HR Operations`,`CIO`],outcomes:[{metric:`58%`,label:`HR service-desk ticket deflection`},{metric:`-49%`,label:`Time to answer policy questions`},{metric:`88k`,label:`Employees on one HR assistant`},{metric:`0`,label:`Sensitive-topic mishandling incidents`}],related:[`kore-ai-banking-conversational-ai`,`omnichannel-conversational-ai-large-healthcare`,`large-retailer-kore-ai-agent-platform`],__author:`strategy`},{slug:`regional-medicare-advantage-payer-agentforce-member-services`,capabilities:[`Conversational AI`,`RAG / Knowledge AI`,`AI Ops / Analytics`],title:`46% member-services deflection for a Medicare Advantage payer â CMS-safe agentic AI`,seoTitle:`Case Study â Agentforce Member Services at a Medicare Advantage Payer â pronix.ai`,metaDescription:`How pronix.ai deflected 46% of member-services volume during AEP â delivered on Salesforce Agentforce and Amazon Connect.`,client:`Regional Medicare Advantage payer`,industry:`Health Payers`,platform:`Salesforce Agentforce · Salesforce Health Cloud · Amazon Connect`,eyebrow:`Case study · Health Payers · Agentforce`,summary:`A regional Medicare Advantage payer was buckling under AEP call volume for benefits, PCP changes, ID cards and prior-auth status. pronix.ai deployed Salesforce Agentforce on Health Cloud with Amazon Connect voice and CMS-safe guardrails â 46% deflection and CSAT flat through AEP.`,readTime:`8 min read`,updated:`Q1 2026`,audience:[`VP Member Services`,`Chief AI Officer`,`CIO`],outcomes:[{metric:`46%`,label:`Deflection on top-5 intents`},{metric:`-11pt`,label:`Abandon rate through AEP`},{metric:`+0.2pt`,label:`Member CSAT`},{metric:`0`,label:`CMS marketing findings`}],related:[`large-us-health-insurer-agentic-claims`,`healthcare-provider-kore-ai-agent-platform`,`pc-insurer-amazon-connect-migration`],__author:`cx`},{slug:`medicaid-mco-genesys-eligibility-modernization`,capabilities:[`Agent Assist`,`Contact Center Migration`,`RAG / Knowledge AI`],title:`33% AHT reduction on eligibility calls at a Medicaid MCO â contact center modernization`,seoTitle:`Case Study â Medicaid MCO Genesys Cloud Eligibility Modernization â pronix.ai`,metaDescription:`How pronix.ai cut eligibility AHT 33% for a Medicaid MCO â delivered on Genesys Cloud with Kore.ai and Salesforce Health Cloud.`,client:`State Medicaid MCO`,industry:`Health Payers`,platform:`Genesys Cloud CX · Kore.ai · Salesforce Health Cloud`,eyebrow:`Case study · Health Payers · CCaaS modernization`,summary:`A state Medicaid MCO was running eligibility and enrollment on an aging Cisco estate with no agent assist and a queue of state audit findings. pronix.ai moved them to Genesys Cloud with Kore.ai agent assist and Health Cloud integration â 33% AHT reduction, clean audits, and a modernization path for the rest of the estate.`,readTime:`8 min read`,updated:`Q1 2026`,audience:[`VP Operations`,`Compliance Director`,`CIO`],outcomes:[{metric:`-33%`,label:`AHT on eligibility calls`},{metric:`-67%`,label:`Document-to-decision time`},{metric:`0`,label:`New state audit findings`},{metric:`9 mo`,label:`Legacy Cisco to Genesys`}],related:[`regional-medicare-advantage-payer-agentforce-member-services`,`large-us-health-insurer-agentic-claims`,`pc-insurer-amazon-connect-migration`],__author:`strategy`},{slug:`specialty-pharma-veeva-agent-assist`,capabilities:[`Agent Assist`,`RAG / Knowledge AI`,`AI Ops / Analytics`],title:`Agent assist for a specialty pharma medical information line â GxP-safe on Veeva`,seoTitle:`Case Study â Specialty Pharma Medical Information Agent Assist â pronix.ai`,metaDescription:`How pronix.ai reduced medical information AHT 29% with zero AE misses â delivered with GxP-safe agent assist on Veeva and Genesys Cloud.`,client:`Specialty pharmaceutical manufacturer`,industry:`Life Sciences`,platform:`Genesys Cloud CX · Kore.ai · Veeva Vault MedComms`,eyebrow:`Case study · Life Sciences · Agent assist`,summary:`A specialty pharma manufacturer needed agent assist for its medical information line without breaking GxP validation or slowing adverse-event reporting. pronix.ai delivered a Veeva-grounded assistant with an AE-detection classifier â 29% AHT reduction and zero AE reporting misses.`,readTime:`8 min read`,updated:`Q1 2026`,audience:[`Head of Medical Information`,`Head of Pharmacovigilance`,`CIO`],outcomes:[{metric:`-29%`,label:`AHT on medical information calls`},{metric:`+18%`,label:`AE detection rate (assisted vs. baseline)`},{metric:`0`,label:`Missed 24-hour AE reporting SLAs`},{metric:`0`,label:`GxP audit findings`}],related:[`life-sciences-genesys-cloud-implementation`,`kore-ai-contact-center-ai-healthcare`,`healthcare-provider-kore-ai-agent-platform`],__author:`cx`},{slug:`global-bpo-multilingual-voice-ai`,capabilities:[`Conversational AI`,`RAG / Knowledge AI`,`AI Ops / Analytics`],title:`Multilingual voice AI across a global BPO â six languages, four programs`,seoTitle:`Case Study â Multilingual Voice AI at a Global BPO â pronix.ai`,metaDescription:`How pronix.ai delivered 34% containment and 100% QA coverage across six languages â delivered on Amazon Connect, Lex and Azure AI.`,client:`Global BPO`,industry:`BPO`,platform:`Amazon Connect · Amazon Lex · Kore.ai · Azure AI Foundry`,eyebrow:`Case study · BPO · Voice AI`,summary:`A global BPO wanted to add multilingual voice AI across four enterprise client programs without renegotiating any master service agreement. pronix.ai deployed six-language voice AI with automated QA on Amazon Connect + Lex â 34% containment on tier-1 intents and 100% QA coverage.`,readTime:`8 min read`,updated:`Q1 2026`,audience:[`BPO COO`,`VP Delivery`,`Client Partner`],outcomes:[{metric:`34%`,label:`Containment on tier-1 intents`},{metric:`100%`,label:`QA coverage across programs`},{metric:`6`,label:`Languages live`},{metric:`0`,label:`MSA renegotiations required`}],implementationSummary:{href:`/downloads/case-studies/global-bpo-multilingual-voice-ai-implementation-summary.pdf`,pages:2,sizeKb:6},related:[`global-bpo-agent-assist`,`large-retailer-kore-ai-agent-platform`,`national-retailer-amazon-connect-cutover-90-days`],__author:`cx`},{slug:`regional-bank-agentforce-fraud-triage`,capabilities:[`AI Ops / Analytics`],title:`4-minute fraud case cycle at a regi
1onal bank â agentic triage with human-in-the-loop`,seoTitle:`Case Study â Regional Bank Agentforce Fraud Triage â pronix.ai`,metaDescription:`How pronix.ai collapsed fraud case cycle time from 22 minutes to 4 â delivered on Salesforce Agentforce and Amazon Connect.`,client:`Regional bank`,industry:`Financial Services`,platform:`Salesforce Agentforce · Salesforce Financial Services Cloud · Amazon Connect`,eyebrow:`Case study · Financial Services · Agentforce`,summary:`A regional bank's fraud triage was 22 minutes per case with a rising backlog and inconsistent customer messaging. pronix.ai shipped an Agentforce triage workflow on Financial Services Cloud with Amazon Connect voice â case cycle down to four minutes with a HITL step on every decision.`,readTime:`8 min read`,updated:`Q1 2026`,audience:[`Head of Fraud Operations`,`Chief AI Officer`,`CIO`],outcomes:[{metric:`22 â 4 min`,label:`Fraud case cycle`},{metric:`-38%`,label:`Fraud loss on triaged cases`},{metric:`+0.4pt`,label:`Customer CSAT post-verify`},{metric:`0`,label:`Regulator exam findings`}],related:[`large-national-insurer-llm-finops`,`pc-insurer-amazon-connect-migration`,`large-us-health-insurer-agentic-claims`],__author:`strategy`},{slug:`enterprise-legal-intake-copilot-studio`,capabilities:[`Agent Assist`,`Conversational AI`,`RAG / Knowledge AI`],title:`71% self-service on NDAs and vendor reviews â enterprise legal intake with copilot workflows`,seoTitle:`Case Study â Enterprise Legal Intake with Copilot Studio â pronix.ai`,metaDescription:`How pronix.ai handled 71% of legal intake without a lawyer touch â delivered with Microsoft Copilot Studio and ServiceNow Legal.`,client:`Large global enterprise`,industry:`Enterprise`,platform:`Microsoft Copilot Studio · SharePoint · ServiceNow Legal · Microsoft Teams`,eyebrow:`Case study · Legal · Copilot Studio`,summary:`A large enterprise legal team was drowning in NDA, vendor review and privacy DSAR intake tickets. pronix.ai deployed Microsoft Copilot Studio in Teams with SharePoint-grounded playbooks and ServiceNow Legal case creation â 71% of requests handled without a lawyer touching them.`,readTime:`7 min read`,updated:`Q1 2026`,audience:[`General Counsel`,`Head of Legal Ops`,`CIO`],outcomes:[{metric:`71%`,label:`Legal intake handled without lawyer touch`},{metric:`-58%`,label:`Time to signed NDA`},{metric:`-40%`,label:`Legal ops routing time`},{metric:`0`,label:`Sensitive-topic mis-routes`}],related:[`ai-hr-conversational-ai-implementation`,`kore-ai-banking-conversational-ai`,`omnichannel-conversational-ai-large-healthcare`],__author:`strategy`},{slug:`global-bpo-agentic-collections-outbound`,capabilities:[`Agent Assist`,`Conversational AI`],title:`18% lift in right-party contact for a global BPO â agentic outbound collections`,seoTitle:`Case Study â Agentic Outbound Collections at a Global BPO â pronix.ai`,metaDescription:`How pronix.ai lifted right-party contact 18% and promise-to-pay 27% â delivered on Amazon Connect and Salesforce FSC.`,client:`Global BPO`,industry:`BPO`,platform:`Amazon Connect · Salesforce Financial Services Cloud · AWS Bedrock`,eyebrow:`Case study · BPO · Voice AI`,summary:`A global BPO's largest collections program was missing quota because right-party contact rates had collapsed. pronix.ai deployed a compliance-first agentic outbound stack â smarter dial strategy, natural voice AI for verification, and evidence-grade audit trails â lifting RPC 18% and promise-to-pay 27% inside TCPA and Reg F.`,readTime:`8 min read`,updated:`Q1 2026`,audience:[`BPO COO`,`Head of Collections`,`Compliance Officer`],outcomes:[{metric:`+18%`,label:`Right-party contact`},{metric:`+27%`,label:`Promise-to-pay conversion`},{metric:`-45s`,label:`Handle time per RPC`},{metric:`0`,label:`TCPA / Reg F findings in audit`}],implementationSummary:{href:`/downloads/case-studies/global-bpo-agentic-collections-outbound-implementation-summary.pdf`,pages:2,sizeKb:6},related:[`global-bpo-agent-assist`,`global-bpo-multilingual-voice-ai`,`regional-bank-agentforce-fraud-triage`],__author:`cx`},{slug:`bpo-workforce-management-ai-forecasting`,capabilities:[`AI Ops / Analytics`],title:`32% forecast-accuracy gain for a large global BPO â AI-driven workforce management`,seoTitle:`Case Study â AI Workforce Forecasting at a Large Global BPO â pronix.ai`,metaDescription:`How pronix.ai improved forecast accuracy 32% and avoided $11M in staffing cost â delivered with AI forecasting on Snowflake and Databricks.`,client:`Large global BPO`,industry:`BPO`,platform:`NICE CXone · Snowflake · Databricks · custom ML`,eyebrow:`Case study · BPO · AI Ops`,summary:`A large global BPO's staffing model was rebuilt every Monday in spreadsheets and always wrong by Wednesday. pronix.ai shipped an AI forecasting engine with intraday re-optimization across dozens of sites â 32% forecast-accuracy improvement, 9% shrinkage reduction and $11M in avoided overstaffing.`,readTime:`7 min read`,updated:`Q4 2025`,audience:[`BPO COO`,`VP Workforce Management`,`CFO`],outcomes:[{metric:`+32%`,label:`Forecast accuracy (MAPE)`},{metric:`-9%`,label:`Shrinkage`},{metric:`$11M`,label:`Annual staffing cost avoided`},{metric:`3d â 4h`,label:`Weekly WFM cycle time`}],implementationSummary:{href:`/downloads/case-studies/bpo-workforce-management-ai-forecasting-implementation-summary.pdf`,pages:2,sizeKb:6},related:[`global-bpo-agent-assist`,`global-bpo-multilingual-voice-ai`,`large-national-insurer-llm-finops`],__author:`strategy`},{slug:`bpo-quality-assurance-100pct-coverage`,capabilities:[`AI Ops / Analytics`],title:`From 3% sampled QA to 100% coverage across a mid-market BPO â AI quality operations`,seoTitle:`Case Study â 100% AI Quality Coverage at a BPO â pronix.ai`,metaDescription:`How pronix.ai moved a BPO from 3% sampled QA to 100% AI-scored coverage â delivered on Genesys Cloud with AWS Bedrock.`,client:`Mid-market BPO`,industry:`BPO`,platform:`Genesys Cloud CX · AWS Bedrock · Snowflake`,eyebrow:`Case study · BPO · AI Ops`,summary:`A mid-market BPO's QA program sampled 3% of interactions and clients still argued the scorecards. pronix.ai stood up 100% AI-scored QA with human calibration and evidence linking â 4x more coach-worthy findings and a 22% CSAT lift across the top three programs.`,readTime:`6 min read`,updated:`Q4 2025`,audience:[`BPO COO`,`Head of Quality`,`Client Services Director`],outcomes:[{metric:`3% â 100%`,label:`QA coverage`},{metric:`4x`,label:`Coach-worthy findings surfaced`},{metric:`+22%`,label:`CSAT on covered programs`},{metric:`-83%`,label:`Client scorecard disputes`}],implementationSummary:{href:`/downloads/case-studies/bpo-quality-assurance-100pct-coverage-implementation-summary.pdf`,pages:2,sizeKb:6},related:[`global-bpo-agent-assist`,`global-bpo-agentic-collections-outboun
1d`,`kore-ai-contact-center-ai-healthcare`],__author:`cx`},{slug:`regional-bank-agentic-loan-origination`,capabilities:[`RAG / Knowledge AI`,`AI Ops / Analytics`],title:`9-day loan decision at a regional bank â agentic origination for small business`,seoTitle:`Case Study â Agentic Small Business Loan Origination at a Regional Bank â pronix.ai`,metaDescription:`How pronix.ai collapsed small-business loan decisions from 21 days to 9 and raised underwriter throughput 2.6x â delivered on Salesforce FSC and nCino.`,client:`Regional US bank`,industry:`Financial Services`,platform:`Salesforce Financial Services Cloud · AWS Bedrock · nCino`,eyebrow:`Case study · Financial Services · Agentic AI`,summary:`A regional bank was losing small business loans to fintechs because their decision cycle ran 21 days. pronix.ai deployed an agentic origination workflow with fair-lending guardrails and underwriter-in-the-loop â decisions in 9 days, 2.6x underwriter throughput and zero fair-lending findings.`,readTime:`8 min read`,updated:`Q1 2026`,audience:[`Chief Lending Officer`,`Head of Small Business`,`Chief Risk Officer`],outcomes:[{metric:`21d â 9d`,label:`Loan decision cycle`},{metric:`2.6x`,label:`Underwriter throughput`},{metric:`+22%`,label:`Application-to-fund conversion`},{metric:`0`,label:`Fair-lending findings`}],related:[`regional-bank-agentforce-fraud-triage`,`investment-mgmt-five9-to-kore-ai-migration`,`kore-ai-banking-conversational-ai`],__author:`strategy`},{slug:`wealth-mgmt-advisor-copilot-rag`,capabilities:[`Agent Assist`,`RAG / Knowledge AI`,`AI Ops / Analytics`],title:`43% advisor productivity gain at a global wealth manager â RAG copilot for client meetings`,seoTitle:`Case Study â Advisor RAG Copilot at a Global Wealth Manager â pronix.ai`,metaDescription:`How pronix.ai gave advisors 43% of prep time back and cut new-hire ramp to 5 months â delivered with a RAG copilot on Microsoft Copilot Studio.`,client:`Global wealth manager`,industry:`Financial Services`,platform:`Microsoft Copilot Studio · Azure AI Search · Salesforce Financial Services Cloud`,eyebrow:`Case study · Financial Services · RAG`,summary:`A global wealth manager's advisors spent hours prepping every client meeting across research, holdings and suitability. pronix.ai shipped a RAG copilot grounded on sanctioned research and CRM â 43% prep-time gain, faster suitability reviews and full SEC 17a-4 archival.`,readTime:`7 min read`,updated:`Q1 2026`,audience:[`Head of Wealth`,`CIO`,`Chief Compliance Officer`],outcomes:[{metric:`-43%`,label:`Meeting prep time`},{metric:`+31%`,label:`Client meetings per advisor per week`},{metric:`9mo â 5mo`,label:`New-hire time-to-productivity`},{metric:`100%`,label:`SEC 17a-4 archival coverage`}],related:[`investment-mgmt-five9-to-kore-ai-migration`,`regional-bank-agentic-loan-origination`,`kore-ai-banking-conversational-ai`],__author:`cx`},{slug:`academic-medical-center-ambient-scribe`,capabilities:[`RAG / Knowledge AI`],title:`72 minutes returned per clinician per day at an academic medical center â ambient AI scribe`,seoTitle:`Case Study â Ambient AI Scribe at an Academic Medical Center â pronix.ai`,metaDescription:`How pronix.ai returned 72 minutes per clinician per day and cut burnout 38% â delivered with an Epic-native ambient AI scribe on Azure OpenAI.`,client:`Academic medical center`,industry:`Healthcare Providers`,platform:`Epic · Azure OpenAI · Nuance DAX-class ambient scribe`,eyebrow:`Case study · Healthcare Providers · Ambient AI`,summary:`Clinician documentation was the top-cited driver of burnout at an academic medical center. pronix.ai rolled out an ambient AI scribe integrated with Epic across its clinician workforce â 72 minutes returned per clinician per day and a 38
1% drop in reported burnout scores in six months.`,readTime:`8 min read`,updated:`Q1 2026`,audience:[`CMIO`,`CMO`,`CIO`],outcomes:[{metric:`72 min`,label:`Returned per clinician per day`},{metric:`-38%`,label:`Burnout score (Maslach)`},{metric:`+11%`,label:`E/M level-appropriate coding`},{metric:`83%`,label:`Six-month clinician adoption`}],related:[`healthcare-provider-kore-ai-agent-platform`,`kore-ai-contact-center-ai-healthcare`,`omnichannel-conversational-ai-large-healthcare`],__author:`cx`},{slug:`childrens-hospital-scheduling-conversational-ai`,capabilities:[`Conversational AI`],title:`41% reduction in scheduling abandonment at a children's hospital â bilingual conversational AI`,seoTitle:`Case Study â Bilingual Scheduling AI at a Children's Hospital â pronix.ai`,metaDescription:`How pronix.ai cut scheduling abandonment 41% at a children's hospital â delivered with bilingual conversational AI on Amazon Connect and Epic MyChart.`,client:`Regional children's hospital`,industry:`Healthcare Providers`,platform:`Amazon Connect · AWS Bedrock · Epic MyChart`,eyebrow:`Case study · Healthcare Providers · Conversational AI`,summary:`A children's hospital was losing 34% of scheduling calls to abandonment during peak hours and had no way to serve Spanish-speaking parents after hours. pronix.ai deployed a bilingual conversational scheduling agent integrated with Epic MyChart â abandonment down 41% and booking cycle at 3.2 minutes.`,readTime:`6 min read`,updated:`Q4 2025`,audience:[`Chief Patient Access Officer`,`CMIO`,`COO`],outcomes:[{metric:`-41%`,label:`Scheduling abandonment`},{metric:`3.2 min`,label:`Average booking cycle`},{metric:`58%`,label:`Spanish-language self-service adoption`},{metric:`0`,label:`Clinical safety mis-routes`}],related:[`healthcare-provider-kore-ai-agent-platform`,`academic-medical-center-ambient-scribe`,`omnichannel-conversational-ai-large-healthcare`],__author:`cx`},{slug:`healthcare-provider-rcm-denial-workqueue`,capabilities:[`Conversational AI`],title:`47% faster denial resolution at a multi-hospital system â RCM workqueue automation`,seoTitle:`Case Study â RCM Denial Workqueue Automation for Healthcare Providers â pronix.ai`,metaDescription:`How pronix.ai cut denial resolution time 47% and recovered $11M in year one â delivered with an agentic RCM workqueue on Epic and Kore.ai.`,client:`Multi-hospital health system`,industry:`Healthcare Providers`,platform:`Epic · Kore.ai Agent Platform · Workday Financials`,eyebrow:`Case study · Healthcare Providers · RCM`,summary:`A multi-hospital health system had $34M in denied and underpaid claims stuck in manual workqueues, with an average 23-day resolution cycle. pronix.ai deployed an agentic RCM workqueue that classified denials, drafted appeals, gathered evidence and tracked status â cutting resolution time by 47% and recovering $11M in the first year.`,readTime:`8 min read`,updated:`Q2 2026`,audience:[`VP Revenue Cycle`,`CFO`,`CIO`,`Director of PFS`],outcomes:[{metric:`-47%`,label:`Denial resolution time`},{metric:`$11M`,label:`First-year cash recovery`},{metric:`-62%`,label:`Manual appeal drafting`},{metric:`94%`,label:`Payer SLA compliance`}],related:[`healthcare-provider-kore-ai-agent-platform`,`academic-medical-center-ambient-scribe`,`childrens-hospital-scheduling-conversational-ai`],__author:`strategy`},{slug:`healthcare-provider-compliant-collections`,capabilities:[`Contact Center Migration`,`RAG / Knowledge AI`],title:`33% reduction in bad debt placement at a regional health system â compliant patient collections`,seoTitle:`Case Study â Compliant Patient Collections AI for Healthcare Providers â pronix.ai`,metaDescription:`How pronix.ai reduced bad-debt placement 33% â delivered with compliant voice and text collections on Amazon Connect and Kore.ai.`,client:`Regional health system`,industry:`Healthcare Providers`,platform:`Amazon Connect · Kore.ai Agent Platform · Epic MyChart`,eyebrow:`Case study · Healthcare Providers · Collections`,summary:`A regional health system was placing 18% of self-pay balances to bad debt within 120 days, partly because outreach was one-channel, one-touch and often non-compliant. pronix.ai deployed a compliant, multi-channel collections agent on voice and SMS with payment-plan negotiation â bad-debt placement fell 33% and TCPA/FDCPA audit findings stayed at zero.`,readTime:`7 min read`,updated:`Q2 2026`,audience:[`VP Revenue Cycle`,`Chief Patient Financial Officer`,`Compliance Officer`,`CIO`],outcomes:[{metric:`-33%`,label:`Bad-debt placement`},{metric:`+28%`,label:`Payment-plan self-enrollment`},{metric:`0`,label:`TCPA / FDCPA findings`},{metric:`41%`,label:`Accounts resolved without human touch`}],related:[`healthcare-provider-rcm-denial-workqueue`,`healthcare-provider-kore-ai-agent-platform`,`kore-ai-contact-center-ai-healthcare`],__author:`cx`},{slug:`healthcare-provider-patient-services-concierge`,capabilities:[`Conversational AI`,`RAG / Knowledge AI`],title:`Unified patient-services concierge handles 52% of inquiries without a live agent`,seoTitle:`Case Study â Unified Patient Services Concierge Bot for Healthcare Providers â pronix.ai`,metaDescription:`How pronix.ai resolved 52% of patient inquiries without a live agent â delivered with a unified concierge on K
1ore.ai and Epic.`,client:`Large integrated healthcare provider`,industry:`Healthcare Providers`,platform:`Kore.ai Agent Platform · Epic · Salesforce Health Cloud · Amazon Connect`,eyebrow:`Case study · Healthcare Providers · Patient Services`,summary:`Patients were calling separate numbers for scheduling, billing, pharmacy and portal help â and getting different answers. pronix.ai built a unified patient-services concierge on voice and chat that authenticated once, reasoned across Epic and Health Cloud, and resolved 52% of inquiries without a live agent.`,readTime:`8 min read`,updated:`Q2 2026`,audience:[`Chief Patient Experience Officer`,`VP Patient Access`,`CIO`,`CMIO`],outcomes:[{metric:`52%`,label:`Inquiry deflection`},{metric:`-41%`,label:`Repeat calls within 24 hours`},{metric:`3.1x`,label:`Patient portal task completion`},{metric:`+0.6pt`,label:`Patient experience score`}],related:[`omnichannel-conversational-ai-large-healthcare`,`healthcare-provider-kore-ai-agent-platform`,`childrens-hospital-scheduling-conversational-ai`],__author:`strategy`},{slug:`healthcare-provider-or-infusion-scheduling-optimization`,capabilities:[],title:`Perioperative and infusion scheduling optimization â 19% OR utilization lift`,seoTitle:`Case Study â Perioperative & Infusion Scheduling Optimization for Healthcare Providers â pronix.ai`,metaDescription:`How pronix.ai lifted OR utilization 19% and cut same-day cancellations 27% â delivered with an agentic scheduler on Epic and Kore.ai.`,client:`Academic medical center`,industry:`Healthcare Providers`,platform:`Epic · Kore.ai Agent Platform · Azure AI Foundry`,eyebrow:`Case study · Healthcare Providers · Operations`,summary:`An academic medical center struggled with OR gaps, infusion chair idle time and last-minute cancellations. pronix.ai built an agentic scheduling optimizer that released unused blocks, packed infusion chairs and proactively reached out to patients â lifting OR utilization 19% and cutting same-day cancellations by 27%.`,readTime:`8 min read`,updated:`Q2 2026`,audience:[`COO`,`VP Perioperative Services`,`CIO`,`CMO`],outcomes:[{metric:`+19%`,label:`OR utilization`},{metric:`-27%`,label:`Same-day cancellations`},{metric:`-34%`,label:`Infusion chair idle time`},{metric:`+2.3M`,label:`Annual incremental capacity`}],related:[`academic-medical-center-ambient-scribe`,`childrens-hospital-scheduling-conversational-ai`,`healthcare-provider-patient-services-concierge`],__author:`cx`},{slug:`healthcare-provider-prior-auth-automation`,capabilities:[`RAG / Knowledge AI`],title:`72% straight-through prior authorizations at an multi-hospital system â agentic RCM`,seoTitle:`Case Study â Prior Authorization Automation for Healthcare Providers â pronix.ai`,metaDescription:`How pronix.ai automated 72% of prior authorizations end-to-e
1nd and cut turnaround from 6 days to under 24 hours â delivered on Epic, Kore.ai and Azure OpenAI.`,client:`Integrated delivery network`,industry:`Healthcare Providers`,platform:`Epic · Kore.ai Agent Platform · Azure OpenAI · Availity`,eyebrow:`Case study · Healthcare Providers · RCM`,summary:`A large integrated delivery network was losing scheduled procedures to prior-auth delays and paying overtime to clear a growing backlog. pronix.ai deployed a multi-agent prior-auth system that read the order, pulled clinical evidence, matched payer policy and submitted through Availity or payer portals â 72% of authorizations completed straight-through with turnaround compressed from six days to under 24 hours.`,readTime:`8 min read`,updated:`Q3 2026`,audience:[`VP Revenue Cycle`,`CFO`,`COO`,`CIO`,`Director of Prior Auth`],outcomes:[{metric:`72%`,label:`Straight-through prior authorizations`},{metric:`-83%`,label:`Prior-auth turnaround time`},{metric:`-41%`,label:`Auth-related procedure delays`},{metric:`$7.6M`,label:`Annualized revenue protected`}],related:[`healthcare-provider-rcm-denial-workqueue`,`healthcare-provider-compliant-collections`,`healthcare-provider-or-infusion-scheduling-optimization`],__author:`strategy`},{slug:`physician-group-early-out-self-pay-ai`,capabilities:[`Agent Assist`,`Conversational AI`],title:`Early-out self-pay AI lifts collections 29% at a 900-provider physician group`,seoTitle:`Case Study â Early-Out Self-Pay Collections AI for Physician Groups â pronix.ai`,metaDescription:`How pronix.ai lifted early-out self-pay collections 29% and cut cost-to-collect 38% â delivered with a compliant voice and digital AI agent on Amazon Connect and Kore.ai.`,client:`Multi-specialty physician group`,industry:`Healthcare Providers`,platform:`Amazon Connect · Kore.ai Agent Platform · Athenahealth · Salesforce Health Cloud`,eyebrow:`Case study · Healthcare Providers · Collections`,summary:`A national physician group was placing early-out self-pay accounts with agencies at 90 days because in-house outreach couldn't keep pace. pronix.ai deployed an agentic early-out program on voice, SMS and email with propensity-to-pay scoring, self-service payment plans and financial-assistance screening â collections lifted 29% and cost-to-collect dropped 38%.`,readTime:`7 min read`,updated:`Q3 2026`,audience:[`VP Revenue Cycle`,`CFO`,`Director of PFS`,`Compliance Officer`],outcomes:[{metric:`+29%`,label:`Early-out self-pay collections`},{metric:`-38%`,label:`Cost to collect`},{metric:`58%`,label:`Balances resolved without human touch`},{metric:`0`,label:`TCPA / healthcare audit findings`}],related:[`healthcare-provider-compliant-collections`,`healthcare-provider-rcm-denial-workqueue`,`healthcare-provider-patient-services-concierge`],__author:`cx`},{slug:`health-system-nurse-triage-discharge-copilot`,capabilities:[`Agent Assist`,`RAG / Knowledge AI`,`AI Ops / Analytics`],title:`Nurse triage and discharge copilot cuts length of stay 0.6 days at a regional health system`,seoTitle:`Case Study â Nurse Triage & Discharge Planning Copilot for Health Systems â pronix.ai`,metaDescription:`How pronix.ai cut length of stay 0.6 days and lifted safe discharges before noon 46% â delivered with a nurse triage and discharge planning copilot on Epic and Azure OpenAI.`,client:`Regional health system`,industry:`Healthcare Providers`,platform:`Epic · Azure OpenAI · Microsoft Copilot Studio · Kore.ai Agent Platform`,eyebrow:`Case study · Healthcare Providers · Clinical Operations`,summary:`A regional health system was holding patients an extra half-day on average due to discharge coordination gaps and after-hours triage bottlenecks. pronix.ai deployed a clinician-in-the-loop nurse triage and discharge copilot integrated with Epic â length of stay dropped 0.6 days, discharges before noon rose 46%, and nurses reclaimed 90 minutes per shift on documentation and coordination work.`,readTime:`9 min read`,updated:`Q3 2026`,audience:[`CNO`,`COO`,`CMO`,`VP Care Management`,`CMIO`],outcomes:[{metric:`-0.6 days`,label:`Average length of stay`},{metric:`+46%`,label:`Discharges before noon`},{metric:`90 min`,label:`Time returned per nurse per shift`},{metric:`0`,label:`Clinical safety escalations missed`}],related:[`academic-medical-center-ambient-scribe`,`healthcare-provider-or-infusion-scheduling-optimization`,`healthcare-provider-patient-services-concierge`],__author:`strategy`},{slug:`commercial-insurer-agentic-underwriting`,capabilities:[`RAG / Knowledge AI`],title:`34% underwriting cycle-time reduction at a commercial insurer â agentic submissions triage`,seoTitle:`Case Study â Agentic Submissions Triage at a Commercial Insurer â pronix.ai`,metaDescription:`How pronix.ai cut underwriting cycle time 34% and raised throughput 2.1x â delivered with an agentic workflow on Guidewire and AWS Bedrock.`,client:`Large US commercial insurer`,industry:`Insurance`,platform:`Guidewire PolicyCenter · AWS Bedrock · Salesforce Financial Services Cloud`,eyebrow:`Case study · Insurance · Agentic AI`,summary:`A large commercial insurer's underwriters spent 60% of their day sorting broker submissions instead of pricing risk. pronix.ai deployed an agentic submissions triage workflow with appetite-fit scoring â cycle time down 34%, underwriter throughput up 2.1x and every decision traceable to a broker-visible reason.`,readTime:`8 min read`,updated:`Q1 2026`,audience:[`Chief Underwriting Officer`,`Head of Commercial Lines`,`CIO`],outcomes:[{metric:`-34%`,label:`Underwriting cycle time`},{metric:`2.1x`,label:`Underwriter throughput`},{metric:`+19%`,label:`Broker-preferred quote rate`},{metric:`-62%`,label:`Past-SLA quotes`}],related:[`pc-insurer-amazon-connect-migration`,`large-national-insurer-llm-finops`,`large-us-health-insurer-agentic-claims`],__author:`strategy`},{slug:`life-insurer-fnol-voice-ai`,capabilities:[`Conversational AI`,`RAG / Knowledge AI`],title:`First-notice-of-loss in under 4 minutes at a life insurer â empathetic voice AI`,seoTitle:`Case Study â Empathetic FNOL Voice AI at a Life Insurer â pronix.ai`,metaDescription:`How pronix.ai reduced FNOL intake from 14 minutes to under 4 while raising NPS 41 points â delivered with empathetic voice AI on Genesys Cloud.`,client:`Large US life insurer`,industry:`Insurance`,platform:`Genesys Cloud CX · AWS Bedrock · Salesforce Financial Services Cloud`,eyebrow:`Case study · Insurance · Voice AI`,summary:`First-notice-of-loss calls at a life insurer averaged 14 minutes and often required beneficiaries to repeat traumatic details across three teams. pronix.ai launched an empathetic voice AI for FNOL intake â 4-minute c
1omplete capture, warm hand-off on any distress signal and 100% policy-verified data downstream.`,readTime:`7 min read`,updated:`Q4 2025`,audience:[`Chief Claims Officer`,`VP Contact Center`,`Chief Customer Officer`],outcomes:[{metric:`14m â 4m`,label:`FNOL intake time`},{metric:`+41`,label:`NPS on bereavement calls`},{metric:`100%`,label:`Policy-verified capture`},{metric:`0`,label:`Compliance findings`}],related:[`pc-insurer-amazon-connect-migration`,`commercial-insurer-agentic-underwriting`,`large-us-health-insurer-agentic-claims`],__author:`cx`},{slug:`specialty-insurer-genai-broker-portal`,capabilities:[`RAG / Knowledge AI`,`AI Ops / Analytics`],title:`58% broker submission-to-quote lift at a specialty insurer â GenAI broker portal`,seoTitle:`Case Study â GenAI Broker Portal at a Specialty Insurer â pronix.ai`,metaDescription:`How pronix.ai lifted broker submission-to-quote conversion 58% â delivered with a GenAI portal on Salesforce FSC and Azure OpenAI.`,client:`US specialty insurer, professional and financial lines`,industry:`Insurance`,platform:`Salesforce Financial Services Cloud · Azure OpenAI · Duck Creek`,eyebrow:`Case study · Insurance · GenAI`,summary:`A specialty insurer was losing broker mindshare because brokers couldn't tell what business the carrier wanted. pronix.ai built a GenAI broker portal with self-serve appetite guidance and instant preliminary indications â 58% lift in submission-to-quote conversion in two quarters.`,readTime:`6 min read`,updated:`Q1 2026`,audience:[`Chief Distribution Officer`,`Head of Broker Experience`,`Chief Underwriting Officer`],outcomes:[{metric:`+58%`,label:`Submission-to-quote conversion`},{metric:`-71%`,label:`Broker time-to-appetite answer`},{metric:`+34%`,label:`Broker portal MAU`},{metric:`0`,label:`Mis-quoted bindable indications`}],related:[`commercial-insurer-agentic-underwriting`,`life-insurer-fnol-voice-ai`,`pc-insurer-amazon-connect-migration`],__author:`strategy`},{slug:`national-payer-prior-auth-automation`,capabilities:[`RAG / Knowledge AI`,`AI Ops / Analytics`],title:`82% prior-authorization touchless rate at a national payer â clinical-safe AI automation`,seoTitle:`Case Study â Prior-Authorization AI Automation at a National Payer â pronix.ai`,metaDescription:`How pronix.ai achieved 82% touchless prior-auth approvals and cut turnaround to 4 hours â delivered on Salesforce Health Cloud and FHIR APIs.`,client:`National health payer`,industry:`Health Payers`,platform:`Salesforce Health Cloud · AWS Bedrock · FHIR APIs`,eyebrow:`Case study · Health Payers · Agentic AI`,summary:`Prior authorization was a 6-day process that cost the payer $92M annually and drove providers to public complaint. pronix.ai automated the clinical-review workflow with clinician-in-the-loop â 82% touchless approvals, 4-hour turnaround and full CMS interoperability rule alignment.`,readTime:`8 min read`,updated:`Q1 2026`,audience:[`Chief Medical Officer`,`VP Utilization Management`,`Chief Digital Officer`],outcomes:[{metric:`82%`,label:`Touchless approval rate`},{metric:`6d â 4h`,label:`Average PA turnaround`},{metric:`$62M`,label:`Annualized administrative savings`},{metric:`0`,label:`CMS interoperability findings`}],related:[`large-us-health-insurer-agentic-claims`,`regional-medicare-advantage-payer-agentforce-member-services`,`medicaid-mco-genesys-eligibility-modernization`],__author:`strategy`},{slug:`regional-payer-appeals-genai-triage`,capabilities:[`AI Ops / Analytics`],title:`44% faster appeals turnaround at a regional payer â GenAI evidence triage`,seoTitle:`Case Study â GenAI Appeals Triage at a Regional Payer â pronix.ai`,metaDescription:`How pronix.ai cut appeals turnaround 44% and lifted first-pass overturn accuracy 18% â delivered on Salesforce Health Cloud and Azure OpenAI.`,client:`Regional health payer`,industry:`Health Payers`,platform:`Salesforce Health Cloud · Azure OpenAI · document AI`,eyebrow:`Case study · Health Payers · GenAI`,summary:`Member and provider appeals were a paperwork mountain that missed regulatory turnaround windows. pronix.ai built a GenAI evidence-triage workflow with reviewer-in-the-loop â 44% faster turnaround, cleaner first-pass overturn decisions and NCQA-audit-ready evidence trails.`,readTime:`7 min read`,updated:`Q4 2025`,audience:[`VP Grievance & Appeals`,`Chief Medical Officer`,`Chief Compliance Officer`],outcomes:[{metric:`-44%`,label:`Appeals turnaround time`},{metric:`+18%`,label:`First-pass overturn accuracy`},{metric:`0`,label:`CMS turnaround misses in period`},{metric:`-71%`,label:`Evidence assembly time per case`}],related:[`national-payer-prior-auth-automation`,`medicaid-mco-genesys-eligibility-modernization`,`regional-medicare-advantage-payer-agentforce-member-services`],__author:`cx`},{slug:`specialty-retailer-agentic-post-purchase`,capabilities:[`Conversational AI`],title:`63% self-service on post-purchase support at a specialty retailer â agentic returns and orders`,seoTitle:`Case Study â Agentic Post-Purchase Support at a Specialty Retailer â pronix.ai`,metaDescription:`How pronix.ai moved 63% of post-purchase support to self-service and lifted NPS 14 points â delivered on Salesforce Commerce Cloud and Amazon Connect.`,client:`US specialty retailer`,industry:`Retail & Ecommerce`,platform:`Salesforce Commerce Cloud · Amazon Connect · AWS Bedrock`,eyebrow:`Case study · Retail & Ecommerce · Agentic AI`,summary:`A specialty retailer's post-purchase support was 68% of contact center volume and mostly about the same six intents. pronix.ai deployed an agentic post-purchase workflow across chat and voice â 63% self-service, 22-second policy-clear refund resolution and 14-point NPS lift.`,readTime:`7 min read`,updated:`Q1 2026`,audience:[`Chief Customer Officer`,`VP Digital`,`Head of Contact Center`],outcomes:[{metric:`63%`,label:`Post-purchase self-service`},{metric:`22s`,label:`Average refund policy resolution`},{metric:`+14`,label:`NPS on post-purchase contacts`},{metric:`-38%`,label:`Cost to serve per post-purchase contact`}],related:[`national-retailer-amazon-connect-cutover-90-days`,`large-retailer-kore-ai-agent-platform`,`global-bpo-agent-assist`],__author:`cx`},{slug:`grocery-retailer-conversational-ordering`,capabilities:[`Conversational AI`,`RAG / Knowledge AI`],title:`$47M incremental online order revenue at a grocery retailer â conversational commerce`,seoTitle:`Case Study â Conversational Ordering at a Grocery Retailer â pronix.ai`,metaDescription:`How pronix.ai generated $47M in incremental online revenue and cut basket time to 4.1 minutes â delivered with conversational commerce on Google Cloud.`,client:`National US grocery retailer`,industry:`Retail & Ecommerce`,platform:`Google Cloud · Salesforce Commerce Cloud · custom voice UX`,eyebrow:`Case study · Retail & Ecommerce · Conversational AI`,summary:`A national grocer's online basket-building experience took 18 minutes and lost half its shoppers before checkout. pronix.ai launched a conversational commerce experience with personalized substitutions â 4.1-minute basket time and $47M in incremental online revenue in one year.`,readTime:`7 min read`,updated:`Q4 2025`,audience:[`Chief Digital Officer`,`VP Ecommerce`,`Head of Loyalty`],outcomes:[{metric:`$47M`,label:`Incremental online revenue (year 1)`},{metric:`18m â 4.1m`,label:`Basket-building time`},{metric:`+3x`,label:`Accepted substitution rate`},{metric:`+22%`,label:`Repeat-order rate`}],related:[`large-retailer-kore-ai-agent-platform`,`national-retailer-amazon-connect-cutover-90-days`,`specialty-retailer-agentic-post-purchase`],__author:`cx`},{slug:`luxury-retailer-clienteling-copilot`,capabilities:[`Agent Assist`],title:`$118M in assisted sales at a luxury retailer â clienteling copilot for store associates`,seoTitle:`Case Study â Clienteling Copilot at a Luxury Retailer â pronix.ai`,metaDescription:`How pronix.ai attributed $118M in assisted sales in year one â delivered with a clienteling copilot on Salesforce Commerce Cloud and Azure OpenAI.`,client:`Global luxury retailer`,industry:`Retail & Ecommerce`,platform:`Salesforce Commerce Cloud · Salesforce Marketing Cloud · Azure OpenAI`,eyebrow:`Case study · Retail & Ecommerce · GenAI`,summary:`A global luxury retailer's clienteling program depended on a handful of top associates and vanished when they left. pronix.ai built a clienteling copilot that captured client memory and drafted brand-safe outreach â $118M in assisted sales attributed in year one.`,readTime:`7 min read`,updated:`Q1 2026`,audience:[`Chief Client Officer`,`VP Retail`,`CMO`],outcomes:[{metric:`$118M`,label:`Attributed assisted sales (year 1)`},{metric:`3.4x`,label:`Outreach per associate per week`},{metric:`-58%`,label:`New-associate time to first assisted sale`},{metric:`0`,label:`Brand-tone or consent violations`}],related:[`specialty-retailer-agentic-post-purchase`,`grocery-retailer-conversational-ordering`,`wealth-mgmt-advisor-copilot-rag`],__author:`cx`},{slug:`global-manufacturer-ai-it-service-desk`,capabilities:[`Conversational AI`,`RAG / Knowledge AI`,`AI Ops / Analytics`,`Employee Experience`],title:`58% ticket deflection on the IT service desk at a global manufacturer`,seoTitle:`Case Study â AI for IT Service Desk on ServiceNow & Kore.ai â pronix.ai`,metaDescription:`How pronix.ai deflected 58% of IT tickets and cut Tier-1 MTTR to 4 minutes â delivered on Kore.ai, ServiceNow and Microsoft Teams.`,client:`Global industrial manufacturer`,industry:`Enterprise`,platform:`Kore.ai Agent Platform · ServiceNow · Microsoft Entra ID · Microsoft Teams`,eyebrow:`Case study · Enterprise IT · AI for IT`,summary:`A global manufacturer's IT service desk was buried under repetitive password, access and how-do-I tickets across dozens of countries. pronix.ai launched an agentic AI service desk on Kore.ai integrated
1with ServiceNow and Entra ID â 58% of tickets resolved without a human, MTTR on Tier-1 issues down from 42 minutes to under 4, and a 24/7 multilingual experience inside Microsoft Teams.`,readTime:`8 min read`,updated:`Q1 2026`,audience:[`CIO`,`Head of IT Operations`,`VP Digital Workplace`,`Head of Employee Experience`],outcomes:[{metric:`58%`,label:`Ticket deflection (Tier-1)`},{metric:`42m â 4m`,label:`MTTR on password / access`},{metric:`$6.8M`,label:`Annualized service-desk savings`},{metric:`24/7`,label:`Multilingual coverage across dozens of countries`}],related:[`enterprise-agentic-access-provisioning`,`global-tech-firm-aiops-incident-response`,`large-national-insurer-llm-finops`],__author:`strategy`},{slug:`enterprise-agentic-access-provisioning`,capabilities:[],title:`Access provisioning from 3 days to 11 minutes at a large diversified holdings company`,seoTitle:`Case Study â Agentic Access Provisioning on SailPoint & Okta â pronix.ai`,metaDescription:`How pronix.ai collapsed access provisioning from 3 days to 11 minutes and cut standing entitlements 71% â delivered on SailPoint, Okta and ServiceNow.`,client:`Large diversified holdings company`,industry:`Enterprise`,platform:`Kore.ai Agent Platform · SailPoint · Okta · ServiceNow · Microsoft Entra ID`,eyebrow:`Case study · Enterprise IT · Identity & Access`,summary:`A large diversified holdings company's access-request workflow took 3 business days on average and produced a chronic tail of standing, over-privileged entitlements auditors kept flagging. pronix.ai built an agentic provisioning workflow that resolves standard requests in 11 minutes, applies least-privilege by default, and gave the SOX team a clean audit for the first time in four years.`,readTime:`9 min read`,updated:`Q1 2026`,audience:[`CIO`,`CISO`,`Head of IAM`,`Head of Internal Audit`,`VP IT Operations`],outcomes:[{metric:`3.2 days â 11 min`,label:`Standard access request cycle time`},{metric:`-71%`,label:`Standing privileged entitlements`},{metric:`0`,label:`SOX audit findings on access controls`},{metric:`$4.1M`,label:`Annualized IAM operations savings`}],related:[`global-manufacturer-ai-it-service-desk`,`global-tech-firm-aiops-incident-response`,`large-national-insurer-llm-finops`],__author:`strategy`},{slug:`global-tech-firm-aiops-incident-response`,capabilities:[`Agent Assist`,`AI Ops / Analytics`],title:`62% faster MTTR on P1 incidents at a global tech firm â AIOps + agentic response`,seoTitle:`Case Study â AIOps & Agentic Incident Response on ServiceNow & PagerDuty â pronix.ai`,metaDescription:`How pronix.ai cut P1 MTTR 62% and false-positive pages 47% â delivered with AIOps on ServiceNow, PagerDuty, Datadog and Kore.ai.`,client:`Global technology firm`,industry:`Enterprise`,platform:`ServiceNow ITOM · PagerDuty · Datadog · Kore.ai Agent Platform · Amazon Bedrock`,eyebrow:`Case study · Enterprise IT · AIOps`,summary:`A global tech firm's SRE org was drowning in alerts and losing senior engineers to on-call fatigue. pronix.ai deployed an AIOps + agentic incident-response layer that correlated signals across Datadog, ServiceNow ITOM and PagerDuty â 62% faster P1 MTTR, 47% fewer false-positive pages, and post-incident summaries drafted before the war room ended.`,readTime:`9 min read`,updated:`Q1 2026`,audience:[`CIO`,`VP Infrastructure`,`Head of SRE`,`Director of Incident Management`],outcomes:[{metric:`-62%`,label:`P1 MTTR`},{metric:`-47%`,label:`False-positive P1 pages`},{metric:`5-8 days â 4 hours`,label:`Post-incident summary turnaround`},{metric:`-19 pts`,label:`On-call attrition (senior SRE)`}],related:[`global-manufacturer-ai-it-service-desk`,`enterprise-agentic-access-provisioning`,`large-national-insurer-llm-finops`],__author:`cx`},{slug:`regional-bank-conversational-ai-core-banking`,capabilities:[`Conversational AI`,`Contact Center Migration`,`RAG / Knowledge AI`,`AI Ops / Analytics`],title:`52% tier-1 call containment at a regional bank â conversational AI for everyday banking`,seoTitle:`Case Study â Conversational AI for Core Banking at a Regional Bank â pronix.ai`,metaDescription:`How pronix.ai contained 52% of tier-1 banking calls and cut transferred-call AHT 34% â delivered on Kore.ai Agent Platform and Amazon Connect.`,client:`Regional US bank`,industry:`Financial Services`,platform:`Kore.ai Agent Platform · Amazon Connect · Salesforce Financial Services Cloud`,eyebrow:`Case study · Financial Services · Conversational AI`,summary:`A regional bank's contact center was drowning in balance inquiries, transfers, payment questions and statement requests. pronix.ai deployed a Kore.ai Agent Platform virtual assistant on voice and chat with secure authentication and core-banking integrations â 52% tier-1 containment and a materially better customer experience.`,readTime:`8 min read`,updated:`Q1 2026`,audience:[`Head of Retail Banking`,`VP Contact Center`,`Chief Digital Officer`,`CIO`],outcomes:[{metric:`52%`,label:`Tier-1 call containment`},{metric:`-34%`,label:`AHT on transferred calls`},{metric:`+0.6pt`,label:`Customer CSAT`},{metric:`24/7`,label:`Self-service coverage`}],related:[`regional-bank-agentforce-fraud-triage`,`national-bank-agent-assist-next-best-action`,`credit-union-card-services-automation`],__author:`cx`},{slug:`national-bank-agent-assist-next-best-action`,capabilities:[`Agent Assist`,`RAG / Knowledge AI`,`AI Ops / Analytics`],title:`29% faster handle time for banking agents â real-time guidance and next-best-action`,seoTitle:`Case Study â Agent Assist for Banking Agents â pronix.ai`,metaDescription:`How pronix.ai reduced AHT 29%, lifted FCR 23% and cut after-call work 40% â delivered with Kore.ai Contact Center AI on Genesys Cloud.`,client:`National bank`,industry:`Financial Services`,platform:`Kore.ai Contact Center AI · Genesys Cloud · Salesforce Financial Services Cloud`,eyebrow:`Case study · Financial Services · Agent assist`,summary:`A national bank's agents were navigating a dozen systems and inconsistent policy documents while customers waited. pronix.ai deployed Kore.ai Contact Center AI as a real-time overlay on Genesys Cloud â surfacing next-best-action, policy guidance and auto-summaries grounded on the sanctioned knowledge base.`,readTime:`7 min read`,updated:`Q1 2026`,audience:[`VP Contact Center`,`Head of Retail Banking Operations`,`CIO`,`Chief Learning Officer`],outcomes:[{metric:`-29%`,label:`AHT`},{metric:`+23%`,label:`First-call resolution`},{metric:`-40%`,label:`After-call work`},{metric:`5mo â 3mo`,label:`New-hire time-to-productivity`}],related:[`regional-bank-conversational-ai-core-banking`,`credit-union-card-services-automation`,`investment-mgmt-five9-to-kore-ai-migration`],__author:`cx`},{slug:`credit-union-card-services-automation`,capabilities:[`Conversational AI`,`AI Ops / Analytics`],title:`81% of card services requests resolved self-service â block, PIN and dispute automation`,seoTitle:`Case Study â Card Services Self-Service Automation â pronix.ai`,metaDescription:`How pronix.ai resolved 81% of card-services requests self-service and cut agent calls 67% â delivered on Kore.ai Agent Platform and Amazon Connect.`,client:`Large US credit union`,industry:`Financial Services`,platform:`Kore.ai Agent Platform · Amazon Connect · Fiserv · Salesforce Financial Services Cloud`,eyebrow:`Case study · Financial Services · Self-service`,summary:`Card-related calls â lost-card block, PIN reset, transaction disputes, fee waivers and activation â were one of the credit union's highest-volume, highest-friction contact drivers. pronix.ai built a secure self-service experience on Kore.ai Agent Platform that resolved 81% of these requests without a human.`,readTime:`7 min read`,updated:`Q1 2026`,audience:[`Head of Card Services`,`VP Contact Center`,`Chief Digital Officer`,`CIO`],outcomes:[{metric:`81%`,label:`Card requests resolved self-service`},{metric:`-67%`,label:`Card call volume to agents`},{metric:`24/7`,label:`Instant card service coverage`},{metric:`+0.8pt`,label:`Member satisfaction`}],related:[`regional-bank-conversational-ai-core-banking`,`national-bank-agent-assist-next-best-action`,`regional-bank-agentforce-fraud-triage`],__author:`cx`},{slug:`global-manufacturer-ai-workplace-employee-portal`,capabilities:[`Conversational AI`,`RAG / Knowledge AI`,`Employee Experience`],title:`One AI workplace front door at a global manufacturer`,seoTitle:`Case Study â AI Workplace Front Door at a Global Manufacturer â pronix.ai`,metaDescription:`How pronix.ai replaced eight internal portals with a single AI workplace assistant for tens of thousands of employees â 71% self-service resolution and 9 days saved per hire.`,client:`Global industrial manufacturer`,industry:`Manufacturing`,platform:`Microsoft 365 Copilot · Azure AI Foundry · ServiceNow · Workday`,eyebrow:`Case study · Employee Experience · AI Workplace`,summary:`Employees searched eight portals to answer a single question. pronix.ai delivered one governed AI workplace assistant across Teams, the intranet and mobile â grounded in HR, IT and policy content â taking self
1-service resolution to 71% and pulling nine days out of the onboarding path.`,readTime:`8 min read`,updated:`Q1 2026`,audience:[`CIO`,`Chief People Officer`,`Head of Digital Workplace`,`VP Employee Services`],outcomes:[{metric:`71%`,label:`Employee questions resolved self-service`},{metric:`-9 days`,label:`Time to productivity for new hires`},{metric:`8 â 1`,label:`Portals employees must know`},{metric:`0`,label:`Entitlement-leak findings at audit`}],related:[`global-bank-ai-for-it-service-desk`,`healthcare-system-ai-for-hr-shared-services`,`insurer-enterprise-knowledge-ai-policy-grounding`],__author:`strategy`},{slug:`global-bank-ai-for-it-service-desk`,capabilities:[`Conversational AI`,`Employee Experience`],title:`Cutting IT service-desk ticket volume 44% at a global bank`,seoTitle:`Case Study â AI for IT Service Desk at a Global Bank â pronix.ai`,metaDescription:`How pronix.ai deflected 44% of IT tickets and cut mean time to resolve by 38% at a global bank â delivered on ServiceNow and Azure AI Foundry.`,client:`Global bank`,industry:`Financial Services`,platform:`ServiceNow · Azure AI Foundry · Entra ID · Intune`,eyebrow:`Case study · Employee Experience · AI for IT`,summary:`Password, access and device tickets consumed two thirds of a global bank's service-desk capacity. pronix.ai shipped an AI-for-IT layer that resolves those journeys end to end â including the underlying transaction â deflecting 44% of ticket volume with a full audit trail for every automated action.`,readTime:`8 min read`,updated:`Q1 2026`,audience:[`CIO`,`VP IT Service Management`,`Head of End User Computing`,`CISO`],outcomes:[{metric:`-44%`,label:`Ticket volume to human agents`},{metric:`-38%`,label:`Mean time to resolve`},{metric:`92%`,label:`Automated actions completed without rework`},{metric:`100%`,label:`Privileged actions with human approval`}],related:[`global-manufacturer-ai-workplace-employee-portal`,`logistics-employee-ai-agents-frontline`,`retail-group-workflow-automation-integration`],__author:`strategy`},{slug:`healthcare-system-ai-for-hr-shared-services`,capabilities:[`RAG / Knowledge AI`,`Employee Experience`],title:`AI for HR shared services across a large nonprofit health system`,seoTitle:`Case Study â AI for HR Shared Services at a Health System â pronix.ai`,metaDescription:`How pronix.ai took 63% of HR shared-service contacts to self-service and cut case cycle time 41% across a large nonprofit health system.`,client:`Nonprofit health system`,industry:`Healthcare Providers`,platform:`Workday · ServiceNow HRSD · Azure AI Foundry`,eyebrow:`Case study · Employee Experience · AI for HR`,summary:`HR shared services was answering the same benefits, leave and payroll questions thousands of times a month while complex cases waited. pronix.ai delivered an AI-for-HR layer that resolves routine journeys end to end and hands genuinely complex cases to a person with the context already assembled.`,readTime:`8 min read`,updated:`Q1 2026`,audience:[`Chief People Officer`,`VP HR Operations`,`Head of Total Rewards`,`CIO`],outcomes:[{metric:`63%`,label:`HR contacts resolved self-service`},{metric:`-41%`,label:`Cycle time on complex HR cases`},{metric:`24/7`,label:`Coverage for clinical shift workers`},{metric:`+1.2pt`,label:`HR service satisfaction`}],related:[`global-manufacturer-ai-workplace-employee-portal`,`logistics-employee-ai-agents-frontline`,`insurer-enterprise-knowledge-ai-policy-grounding`],__author:`strategy`},{slug:`logistics-employee-ai-agents-frontline`,capabilities:[`Employee Experience`],title:`Employee AI agents for a global frontline logistics workforce`,seoTitle:`Case Study â Employee AI Agents for Frontline Logistics Teams â pronix.ai`,metaDescription:`How pronix.ai put governed employee AI agents in the hands of a global frontline logistics workforce â 3.1x faster request completion, zero unsupervised privileged actions.`,client:`Global logistics operator`,industry:`Logistics & Transportation`,platform:`Azure AI Foundry · ServiceNow · SAP SuccessFactors · Microsoft Teams`,eyebrow:`Case study · Employee Experience · Employee AI Agents`,summary:`Drivers, dock staff and depot supervisors had no realistic way to complete an internal request mid-shift. pronix.ai deployed a small set of bounded employee AI agents â shift swap, equipment, expense and access â that c
1omplete the transaction on a phone in under two minutes, with human approval on anything that matters.`,readTime:`9 min read`,updated:`Q1 2026`,audience:[`COO`,`CIO`,`VP Field Operations`,`Chief People Officer`],outcomes:[{metric:`3.1x`,label:`Faster request completion`},{metric:`-40%`,label:`Supervisor administrative relay time`},{metric:`78%`,label:`Frontline monthly active use`},{metric:`0`,label:`Unsupervised privileged actions`}],related:[`global-bank-ai-for-it-service-desk`,`healthcare-system-ai-for-hr-shared-services`,`retail-group-workflow-automation-integration`],__author:`strategy`},{slug:`insurer-enterprise-knowledge-ai-policy-grounding`,capabilities:[`Contact Center Migration`,`RAG / Knowledge AI`,`AI Ops / Analytics`,`Employee Experience`],title:`Enterprise knowledge AI grounded on 2.4M policy documents at a national insurer`,seoTitle:`Case Study â Enterprise Knowledge AI at a National Insurer â pronix.ai`,metaDescription:`How pronix.ai grounded 2.4M policy and procedure documents into one governed answer layer â 94% answer accuracy and citations on every response.`,client:`National insurer`,industry:`Insurance`,platform:`Azure AI Search · Azure AI Foundry · SharePoint · Guidewire`,eyebrow:`Case study · Employee Experience · Enterprise Knowledge AI`,summary:`Underwriters and claims staff were working from conflicting versions of the same procedure. pronix.ai built a governed enterprise knowledge layer over 2.4M documents with ownership, freshness and citation enforced â 94% answer accuracy on the evaluation set and a citation on every single response.`,readTime:`9 min read`,updated:`Q1 2026`,audience:[`Chief Underwriting Officer`,`VP Claims`,`CIO`,`Chief Risk Officer`],outcomes:[{metric:`94%`,label:`Answer accuracy on the evaluation set`},{metric:`100%`,label:`Responses with a source citation`},{metric:`-380k`,label:`Stale documents retired before launch`},{metric:`-52%`,label:`Time spent searching for procedure`}],related:[`global-manufacturer-ai-workplace-employee-portal`,`healthcare-system-ai-for-hr-shared-services`,`retail-group-workflow-automation-integration`],__author:`strategy`},{slug:`retail-group-workflow-automation-integration`,capabilities:[`RAG / Knowledge AI`,`Employee Experience`],title:`Workflow automation and integration across 27 back-office processes at a retail group`,seoTitle:`Case Study â Workflow Automation & Integration at a Retail Group â pronix.ai`,metaDescription:`How pronix.ai automated 27 cross-system back-office workflows at a retail group â 2.7x faster cycle times and 96% straight-through processing.`,client:`Multi-brand retail group`,industry:`Retail & Ecommerce`,platform:`Azure Logic Apps · Azure AI Foundry · SAP · Workday · ServiceNow`,eyebrow:`Case study · Employee Experience · Workflow automation & integration`,summary:`Onboarding a store associate touched five systems and eleven manual handoffs. pronix.ai rebuilt 27 back-office workflows as governed, event-driven integrations with AI handling the judgment steps â 96% straight-through processing and cycle times cut by 2.7x.`,readTime:`8 min read`,updated:`Q1 2026`,audience:[`COO`,`CIO`,`VP Shared Services`,`Head of Enterprise Architecture`],outcomes:[{metric:`96%`,label:`Straight-through processing`},{metric:`2.7x`,label:`Faster end-to-end cycle time`},{metric:`-18pt`,label:`Rework rate on back-office cases`},{metric:`27`,label:`Processes live in 9 months`}],related:[`global-bank-ai-for-it-service-desk`,`logistics-employee-ai-agents-frontline`,`insurer-enterprise-knowledge-ai-policy-grounding`],__author:`strategy`}].map(({__author:e,...n})=>({...n,author:t[e]})),a=new Set([]),o=i.filter(e=>!a.has(e.slug));function s(e){return!a.has(e)}function c(e){return i.find(t=>t.slug===e)}export{o as a,t as c,s as i,r as l,i as n,n as o,c as r,e as s,a as t};
Line numbers count LF bytes from the start of the resource, as the search results do. Vendor segments are library code the classifier recognised; they are stored but not indexed. Bytes are shown as Latin1 characters, one per byte.