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1nd ownership of how your organization designs, builds, tests, and runs transformations at enterprise scale. Instead of every project reinventing connectors, pipelines, and governance, you get a consistent model â accelerated with AI for code generation, test automation, and CI/CD review â so engineering leaders can promise delivery dates with confidence. Healthcare networks and retail groups typically need this when patient or customer data must flow in real time across dozens of systems without breaking compliance or blowing API budgets.",problemsSolved:[{label:"Transformation backlogs grow while business teams wait weeks for simple API or connector changes.",icon:"ListTodo"},{label:"Production incidents trace back to untested pipelines, unclear API contracts, or runaway cloud/API spend.",icon:"Flame"},{label:"Teams use different tools and patterns â so knowledge walks out the door when people leave.",icon:"Users"}],outcomes:[{label:"Predictable transformation delivery with automated testing and governed API lifecycle control.",icon:"CheckCircle"},{label:"Lower total cost of transformation ownership and faster time-to-value for new digital initiatives.",icon:"Zap"}],icon:"Waypoints",cta:{label:"Explore Transformation Services",contactTab:o}},{id:"S3",slug:"api-to-agentic-transformation",name:"API-to-Agentic Transformation",valueProposition:"Turn existing enterprise APIs into agent-ready architectures with MCP servers and custom AI agents.",challengeHeadline:"You have hundreds of APIs â but no practical path to the agentic, AI-native experiences leadership expects.",whatItMeans:"API-to-Agentic Transformation bridges the gap between your current API estate and the agentic future. We assess which services are ready for AI agents, design MCP servers and custom agents that understand your middleware and data boundaries, and operationalize them in production â not as demos, but as governed enterprise capabilities. Financial services and platform teams adopt this when copilots and autonomous workflows must safely call internal systems without exposing sensitive logic or creating uncontrolled AI sprawl.",problemsSolved:[{label:"AI pilots work in isolation but cannot securely access the ERP, CRM, or transformation layers they need.",icon:"Unplug"},{label:"Middleware and API complexity make it unclear which systems can be agent-enabled â and which cannot.",icon:"HelpCircle"},{label:"Competitors talk about agentic AI while your architecture still treats APIs as static point-to-point connections.",icon:"TrendingUp"}],outcomes:[{label:"Production-ready agent architectures built on code-level understanding of your existing APIs.",icon:"BrainCircuit"},{label:"Faster path from AI strategy to real autonomous workflows your business can trust and govern.",icon:"Rocket"}],icon:"BrainCircuit",cta:{label:"Start Your Agentic Journey",contactTab:o}},{id:"S4",slug:"database-cloud-grc",name:"Database, Cloud-Native Modernization & GRC",valueProposition:"Modernize data schemas and ETL estates while embedding privacy and GRC compliance from the start.",challengeHeadline:"Your data platform cannot keep up â and every migration raises compliance and privacy alarms.",whatItMeans:"Database, Cloud-Native Modernization & GRC is how you replatform schemas, ETL, and data infrastructure for cloud-native scale while building privacy and governance in â not bolting them on after an audit failure. We rearchitect data models, move pipelines to modern cloud patterns, and align controls to SOC 2, GDPR, HIPAA, and ISO expectations with AI-assisted monitoring and masking. Healthcare providers and regulated enterprises engage here when fragmented databases, manual compliance reporting, and PII exposure risk fines, reputational damage, and stalled analytics programs.",problemsSolved:[{label:"Legacy schemas and batch ETL cannot support real-time analytics or AI workloads the business already promised.",icon:"Database"},{label:"Privacy teams cannot confidently answer who accessed sensitive data â or whether PII is properly masked.",icon:"ShieldAlert"},{label:"Cloud migration stalls because nobody has mapped data risk, retention rules, and compliance gaps.",icon:"CloudOff"}
1],outcomes:[{label:"Cloud-ready data infrastructure with embedded GRC â auditable, secure, and built for AI consumption.",icon:"ShieldCheck"},{label:"Faster, safer data modernization with fewer compliance surprises during and after migration.",icon:"Lock"}],icon:"DatabaseZap",cta:{label:"Modernize Your Data Infrastructure",contactTab:o}},{id:"S18",slug:"datastax-etl-pipeline-modernization",name:"ETL Pipeline Modernization",valueProposition:"Modernize batch and streaming ETL with AI-accelerated data engineering â without betting the program on a single proprietary platform.",challengeHeadline:"Batch pipelines lag behind the business â and your data layer cannot scale to real-time without a rewrite.",whatItMeans:"ETL Pipeline Modernization is how you move from brittle batch jobs and overloaded relational pipelines to governed, real-time data movement on cloud-native infrastructure â accelerated by AI across assessment, design, and delivery. We inventory legacy ETL estates, map lineage and dependencies, design CDC and streaming ingest through event buses, orchestrate pipelines with observable SLAs, and right-size data models for scale and schema evolution. AI-assisted engineering speeds pipeline generation, test coverage, and migration planning so your team delivers incrementally instead of waiting on a seven-figure platform rollout. Public-sector agencies and high-volume digital businesses choose this path when transaction streams, operational records, or IoT data must flow in near real time across regions â without nightly batch windows holding decisions hostage.",problemsSolved:[{label:"Overnight batch jobs miss SLAs, delay reporting, and break when source systems change without warning.",icon:"Moon"},{label:"Vendor roadmaps push seven-figure platform bets â when AI-guided data engineering could modernize pipelines incrementally at a fraction of the cost.",icon:"Gauge"},{label:"Data teams spend more time fixing pipelines than delivering the analytics and AI use cases the business funded.",icon:"Wrench"}],outcomes:[{label:"Real-time, governed data movement on a platform-flexible foundation â built for scale, schema evolution, and zero-downtime cutover.",icon:"Radio"},{label:"Lower pipeline operating cost and a data foundation ready for streaming analytics and AI â without proprietary platform lock-in.",icon:"ArrowDownUp"}],icon:"ArrowDownUp",cta:{label:"Modernize Your Data Pipelines",contactTab:o}}]},{id:"mainframe-cloud-migration",name:"Mainframe Exit & Cloud Migration",matrixLabel:"Mainframe & Cloud",tagline:"Rehost \xb7 Replatform \xb7 AWS cutover",description:"Exit Rocket Software mainframe estates through pragmatic rehost or cloud-native replatforming â without unnecessary business risk.",icon:"Cloud",sortOrder:2,accent:{from:"from-slate-500",to:"to-indigo-500",border:"border-slate-200 dark:border-slate-700",bg:"bg-slate-50/50 dark:bg-slate-900/30",text:"text-indigo-600 dark:text-indigo-400",badge:"bg-indigo-100 dark:bg-indigo-900/40 text-indigo-700 dark:text-indigo-300"},infographic:{type:"lift-shift",title:"Mainframe exit path",description:"Rehost or replatform to AWS",steps:[{label:"Discover",icon:"Search"},{label:"Rehost",icon:"CloudUpload"},{label:"Replatform",icon:"GitBranch"},{label:"Optimize",icon:"TrendingUp"}]},services:[{id:"S17",slug:"mainframe-to-aws-migration",name:"On-Prem Mainframe to AWS Rehosting",valueProposition:"Rehost Rocket Software mainframe workloads on AWS without rewriting COBOL, PL/I, or business logic.",challengeHeadline:"Mainframe costs keep climbing â but a full rewrite feels impossible and every year of delay is millions lost.",whatItMeans:"On-Prem Mainframe to AWS Rehosting is a pragmatic exit from expensive on-premise Rocket Software estates by rehosting COBOL, PL/I, JCL, CICS, and IMS/DB2 workloads in AWS â without converting source code or rewriting decades of proven business logic. We map dependencies, plan parallel-run cutover, migrate data from DB2 and VSAM to cloud-native data stores, and optimize cost after landing in cloud. Banking, insurance, and government programs turn here when MIPS pricing, datacenter contracts, or skills shortages make mainframe retention unsustainable but business risk rules out a big-bang rewrite.",problemsSolved:[{label:"Mainframe licensing and infrastru
1cture consume budget that leadership wants redirected to cloud and AI.",icon:"DollarSign"},{label:"The team that understands COBOL and JCL is retiring â and hiring replacements takes years.",icon:"UserMinus"},{label:"Previous modernization attempts stalled because rewrite scope exploded and business could not accept downtime.",icon:"PauseCircle"}],outcomes:[{label:"Predictable lift-and-shift to AWS with validated cutover â business logic intact, risk controlled.",icon:"Cloud"},{label:"Immediate cost visibility and a platform path to modern transformation and analytics around core workloads.",icon:"LineChart"}],icon:"Terminal",cta:{label:"Plan Your Mainframe Exit",contactTab:o}},{id:"S19",slug:"mainframe-replatforming",name:"Mainframe Replatforming to Cloud-Native",valueProposition:"Move beyond rehost into cloud-native architectures while preserving validated business logic and controlling migration risk.",challengeHeadline:"Your core workloads run in cloud â but batch-centric monoliths still block APIs, real-time channels, and the AI use cases leadership already promised.",whatItMeans:"Mainframe Replatforming is the next step after rehost â refactoring COBOL, PL/I, and batch-centric workloads into modern services, APIs, and event-driven patterns on AWS while preserving business rules your organization has trusted for decades. We decompose monoliths in phased waves, validate parity at each boundary, and open data for analytics and AI without a risky big-bang rewrite. Banks, insurers, and government agencies choose replatforming when leadership needs cloud economics plus modern transformation speed â and rehost alone will not get them there.",problemsSolved:[{label:"Rehosted workloads still behave like mainframe batch â limiting real-time channels and partner APIs.",icon:"Clock"},{label:"Teams cannot iterate quickly because code structure mirrors decades-old job streams and copybooks.",icon:"Code"},{label:"Leadership wants AI and analytics on core data â but monolithic runtimes keep information locked in legacy patterns.",icon:"Database"}],outcomes:[{label:"Cloud-native services and APIs around core business logic â phased cutover with validated functional parity.",icon:"GitBranch"},{label:"A modernization runway for transformation, digital channels, and AI acceleration on top of trusted rules.",icon:"Rocket"}],icon:"GitBranch",cta:{label:"Plan Your Replatforming Journey",contactTab:o}}]},{id:"platform-implementation",name:"Enterprise Platform Delivery",matrixLabel:"Enterprise Platforms",tagline:"Salesforce \xb7 MuleSoft \xb7 Oracle \xb7 ERP AI",description:"Implement and connect Salesforce, MuleSoft, Oracle Fusion, and ERP platforms with AI-native extensions.",icon:"Boxes",sortOrder:3,accent:{from:"from-purple-500",to:"to-pink-500",border:"border-purple-200 dark:border-purple-800",bg:"bg-purple-50/50 dark:bg-purple-950/20",text:"text-purple-600 dark:text-purple-400",badge:"bg-purple-100 dark:bg-purple-900/40 text-purple-700 dark:text-purple-300"},infographic:{type:"platform-nodes",title:"Connected enterprise stack",description:"Salesforce â MuleSoft â Oracle",steps:[{label:"Salesforce",icon:"Users"},{label:"MuleSoft",icon:"Share2"},{label:"Oracle",icon:"Building"},{label:"AI Layer",icon:"Sparkles"}]},services:[{id:"S5",slug:"salesforce-integration",name:"Salesforce & CRM AI Integration",valueProposition:"Connect Salesforce Agentforce, Data Cloud, and CRM to your enterprise ecosystem with AI automation.",challengeHeadline:"Salesforce was supposed to unify the customer view â instead it became another silo next to ERP and data lakes.",whatItMeans:"Salesforce & CRM AI Integration connects Agentforce, Data Cloud, and Sales, Service, and Marketing Cloud to the rest of your enterprise â ERP, data platforms, and AI services â so CRM teams work from one trusted customer picture. We implement Salesforce capabilities, activate Data Cloud, and wire real-time integrations that power AI agents and automated workflows, not just dashboards. Retail, financial services, and B2B organizations invest here when customer experience programs fail because agents lack inventory, billing, or service history that lives outside Salesforce.",problemsSolved:[{label:"Sales and service reps switch between six systems to answer one customer question â eroding NPS and productivity.",icon:"Shuffle"},{label:"Agentforce and AI CRM ambitions stall because underlying customer data is fragmented and unreliable.",icon:"Bot"},{label:"CRM-to-ERP integrations are fragile point solutions that break every time either platform upgrades.",icon:"Link2Off"}],outcomes:[{label:"A connected Salesforce stack with unified data and AI-ready workflows across the customer lifecycle.",icon:"Contact"},{label:"Faster CRM program ROI â less swivel-chair work, more automated resolution and upsell insight.",icon:"HeartHandshake"}],icon:"Contact",cta:{label:"Connect Your Salesforce Stack",contactTab:o}},{id:"S6",slug:"mulesoft-implementation",name:"MuleSoft Implementation & Migration",valueProposition:"Design, build, and migrate to MuleSoft for enterprise API-led connectivity at AI-accelerated speed.",challengeHeadline:"Point-to-point transformations multiply every year â and your API strategy exists only on slides.",whatItMeans:"MuleSoft Implementation & Migration establishes API-led connectivity as an operating model, not a one-off project. Whether you are greenfield on Anypoint Platform or migrating from legacy ESBs and iPaaS tools, we design the architecture, build reusable APIs, move Runtime Fabric workloads, and enable your developers to sustain the platform. Manufacturing and global enterprises typically engage when M&A, cloud adoption, or digital product launches expose an transformation hairball that slows every line-of-business initiative.",problemsSolved:[{label:"Duplicate transformations between the same systems waste money and create conflicting versions of the truth.",icon:"Copy"},{label:"Legacy ESB or custom middleware cannot support the API volume and speed digital channels require.",icon:"ServerCrash"},{label:"Internal teams lack MuleSoft depth â so platform investment sits underused after the initial implementation partner leaves.",icon:"GraduationCap"}],outcomes:[{label:"A governed MuleSoft estate with reusable APIs, clear ownership, and faster delivery for new connections.",icon:"Webhook"}
1,{label:"Reduced transformation TCO and a foundation for agentic AI, cloud, and partner ecosystem growth.",icon:"Network"}],icon:"Webhook",cta:{label:"Plan Your MuleSoft Journey",contactTab:o}},{id:"S7",slug:"oracle-fusion-implementation",name:"Oracle Fusion Cloud Implementation",valueProposition:"Implement Oracle Fusion ERP, HCM, SCM, and CX connected to Salesforce and MuleSoft for unified enterprise flow.",challengeHeadline:"ERP modernization is overdue â but disconnected finance, HR, and supply chain systems make every decision slower.",whatItMeans:"Oracle Fusion Cloud Implementation delivers a unified ERP, HCM, SCM, and CX footprint â implemented through Functional Setup Manager (FSM) configuration and connected to Salesforce and MuleSoft so data flows across finance, people, supply chain, and customer operations. We handle module rollout, FSM-driven setup, data migration, cutover planning, and the transformation fabric that keeps Fusion from becoming another isolated cloud silo. Innoart applies AI agents to the configuration work every Fusion program depends on â interpreting business requirements, accelerating FSM setup tasks, validating cross-module dependencies, and reducing the manual configuration cycles that typically dominate delivery timelines for implementation partners and enterprise teams alike. Large enterprises, public-sector bodies, and SI partners choose this approach when on-prem Oracle or fragmented SaaS tools block real-time reporting, shared services efficiency, and compliance across entities.",problemsSolved:[{label:"Finance closes take too long because HR, procurement, and operations data live in incompatible systems.",icon:"CalendarClock"},{label:"Fusion programs fail silently at the transformation layer â CRM and ERP still do not agree on customer or order truth.",icon:"Split"},{label:"Leadership lacks confidence in cutover planning after seeing peers miss go-live dates and overrun budgets.",icon:"AlertCircle"}],outcomes:[{label:"Fusion live with connected Salesforce and transformation layers â one enterprise record for critical processes.",icon:"Landmark"},{label:"Clearer operational visibility, faster close cycles, and a platform ready for AI enrichment on top.",icon:"BarChart3"}],icon:"Landmark",cta:{label:"Start Your Oracle Fusion Journey",contactTab:o}},{id:"S8",slug:"erp-ai-customization",name:"ERP AI Customization",valueProposition:"Make Oracle, Salesforce, and ServiceNow AI-native with conversational interfaces and autonomous workflows.",challengeHeadline:"Your ERP is powerful â but users still fight clunky screens while competitors ship AI-first experiences.",whatItMeans:"ERP AI Customization layers conversational AI, autonomous workflows, and intelligent interfaces on the platforms you already run â Oracle, Salesforce, ServiceNow, and others â so teams interact in natural language instead of memorizing transaction codes. We design governed AI extensions, connect ERP data to agents and copilots, and manage token and compute spend so innovation stays FinOps-friendly. Operations and shared-services leaders adopt this when training costs, error rates, and cycle times remain high despite expensive ERP licenses that should have simplified work years ago.",problemsSolved:[{label:"Knowledge workers avoid the ERP because simple tasks require ten clicks and specialist training.",icon:"MousePointerClick"},{label:"AI experiments bypass ERP controls â creating shadow automation that finance and audit cannot approve.",icon:"EyeOff"},{label:"Token and AI compute costs spike with no governance tied back to business transactions or ROI.",icon:"Coins"}],outcomes:[{label:"AI-native ERP experiences â conversational, automated, and still governed by your existing controls.",icon:"MessagesSquare"},{label:"Higher user adoption, fewer errors, and measurable productivity gains without replacing core ERP investment.",icon:"TrendingUp"}],icon:"MessagesSquare",cta:{label:"Explore ERP AI Customization",contactTab:o}}]},{id:"ai-solutions",name:"AI Accelerator & Intelligent Automation",matrixLabel:"AI Accelerator",tagline:"Studio \xb7 Agentic AI \xb7 Copilot \xb7 Domain Brain",description:"Production-grade AI on AI Studio â from agentic orchestration and copilots to code acceleration and domain-specific models.",icon:"Brain",sortOrder:4,accent:{from:"from-violet-500",to:"to-blue-500",border:"border-violet-200 dark:border-violet-800",bg
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1,{label:"Self-service BI tools were rolled out but adoption stalled because non-technical staff still cannot query safely.",icon:"UserX"}],outcomes:[{label:"Conversational access to live enterprise data â governed, explainable, and embedded where work happens.",icon:"MessageCircle"},{label:"Faster decisions and less analyst bottlenecks â so data teams focus on strategy, not report factory work.",icon:"Zap"}],icon:"Sparkles",cta:{label:"See the Copilot in Action",contactTab:i}},{id:"S12",slug:"acceleration-engine",name:"Acceleration Engine (AI Code & Asset Gen)",valueProposition:"Turn business intent into production-ready APIs, workflows, connectors, and code with governed AI generation.",challengeHeadline:"Engineering capacity is the bottleneck â and every transformation or modernization project competes for the same scarce team.",whatItMeans:"Acceleration Engine uses governed AI generation to translate business intent into production-ready technical assets â APIs, transformation connectors, workflow definitions, and modernized code â with validation, traceability, and RBAC-safe deployment built in. It is how platform and delivery leaders compress months of hand-coding into reviewed, auditable output without sacrificing enterprise standards. System integrators and internal IT groups adopt this when backlog growth outpaces hiring and leadership expects AI to accelerate delivery, not just chat.",problemsSolved:[{label:"Transformation and modernization queues are measured in quarters â while the business measures urgency in days.",icon:"Hourglass"},{label:"Developers spend most of their time on boilerplate connectors and mappings instead of differentiated features.",icon:"Code"},{label:"Ungoverned AI coding tools create security and quality risk that release managers cannot sign off on.",icon:"ShieldAlert"}],outcomes:[{label:"Dramatically faster delivery of transformation assets and modernized code â with enterprise validation gates intact.",icon:"Rocket"},{label:"More output from existing teams â freeing senior engineers for architecture instead of repetitive build work.",icon:"Users"}],icon:"CodeXml",cta:{label:"Accelerate Your Delivery",contactTab:o}},{id:"S13",slug:"domain-brain",name:"Domain Brain (Own Your AI / SLM)",valueProposition:"Build a proprietary fine-tuned small language model trained on your enterprise data for domain accuracy.",challengeHeadline:"Generic LLMs hallucinate on your domain â and sending proprietary data to public APIs is not an option.",whatItMeans:"Domain Brain is your organization's own fine-tuned small language model â trained on curated, anonymized enterprise data for expert-level accuracy in your vocabulary, processes, and regulations without the cost and privacy risk of always calling large public models. We handle data curation, model selection, fine-tuning, bias audits, and edge or on-prem deployment so you own the intelligence layer. Banks, insurers, and healthcare organizations invest here when customer-facing or clinician-facing AI must be precise, private, and defensible to regulators.",problemsSolved:[{label:"Public LLMs give plausible but wrong answers on internal policies, products, or clinical protocols.",icon:"Brain"},{label:"Legal blocks cloud LLM usage because sensitive corpus data cannot leave your perimeter.",icon:"Lock"},{label:"Large-model inference costs scale unpredictably as usage grows across departments.",icon:"TrendingUp"}],outcomes:[{label:"A proprietary domain model you control â accurate, cost-efficient, and deployable on your terms.",icon:"HardDrive"},{label:"Regulator-ready AI with curated training data, bias review, and responsible AI guardrails included.",icon:"Award"}],icon:"BrainCircuit",cta:{label:"Build Your Domain Brain",contactTab:o}},{id:"S14",slug:"low-code-process-automation",name:"Low / No-Code & Process Automation",valueProposition:"Let business teams automate workflows in plain English with AI-generated BPMN and governed connectors.",challengeHeadline:"Business teams know exactly what should be automated â but IT cannot keep up with the request queue.",whatItMeans:"Low / No-Code & Process Automation empowers citizen developers and operations teams to describe workflows in plain English and receive val
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