Full-Stack AI.
Deployed in 30-90 Days.
ARPIA AI engineers deploy your complete AI stack—from data integration to knowledge graphs to governed AI agents—in production in 30-90 days. Not consultants. Not implementation partners. Expert AI engineers who build with you, then transfer knowledge so you can scale independently.
Enterprise AI Fails When You Deploy Pieces,
Not the Full Stack
Most enterprise AI projects fail because organizations try to stitch together pieces—a data platform here, an LLM wrapper there, governance bolted on as an afterthought. You don't need more point solutions. You need AI engineers who deploy the complete stack.
Stitching Point Solutions Together
Buy a data warehouse. Add an LLM API wrapper. Bolt on governance later. Try to integrate everything yourself. 6-12 months later, you have a fragile Frankenstein system that breaks when anything changes. This isn't AI—it's integration hell.
Platform Without Engineering = Stuck
DIY platforms give you tools but no engineering expertise. Your team spends months learning, experimenting, failing. Even with documentation, you lack the AI architecture experience to deploy production-grade systems. You need AI engineers, not documentation.
Custom Code From Scratch = $500K+ / 18 Months
Traditional consultants build custom solutions from scratch—no platform leverage, no reusable components. $500K-$2M projects, 12-18 month timelines, perpetual dependency because they never transfer knowledge. And when you want a second use case? Start over.
The Complete AI Stack.
Deployed as One System.
ARPIA AI engineers deploy your entire AI stack—not pieces, not integrations, but a unified system where every layer is designed to work together. Platform technology + AI engineering expertise = production-ready AI in 30-90 days.
| Capability | DIY Platforms | Traditional Consulting | ARPIA AI Engineering |
|---|---|---|---|
| Time to Value | 6-12 months (trial & error) | 12-18 months (waterfall) | 30-90 days (proven process) |
| Cost Structure | Platform fees only | $500K-$2M+ projects | Platform + deployment fee |
| Expert Guidance | Documentation only | Yes (expensive) | Yes (included) |
| Knowledge Transfer | None | Minimal (creates dependency) | Built-in (you own it) |
| Scalability | Do it yourself | Hire consultants again | Deploy 10+ use cases independently |
| Risk Level | High (80% failure rate) | Low (but expensive) | Low (with platform leverage) |
| Ongoing Dependency | None (you're on your own) | High (forever consultants) | None (after 30-90 days) |
UNIFIED ARCHITECTURE
Every layer designed to work together—not bolted-on point solutions
PLATFORM-ACCELERATED
Built on proven ARPIA Platform—not custom code from scratch
KNOWLEDGE TRANSFER
You own it after 30-90 days—not perpetual consulting dependency
From Discovery to Production
in 30-90 Days
Our AI engineers follow a proven full-stack deployment process. You're involved in every phase—learning as we build—so when we hand over the keys, you're ready to deploy the next 10 use cases independently.
Discovery & Architecture
- • Deep-dive into business objectives and success metrics
- • Map current data landscape (ERPs, CRMs, databases, APIs)
- • Assess AI readiness and identify technical constraints
- • Define governance requirements (SOX, HIPAA, GDPR, etc.)
- • Architect the full AI stack for your environment
Data Integration & Foundation
- • Connect to your enterprise systems (ERP, CRM, databases)
- • Build AI-optimized data reflection layer
- • Set up real-time data synchronization
- • Configure API gateway and MCP protocol endpoints
- • Implement data quality controls and validation
Knowledge Architecture & Reasoning
- • Design knowledge ontology for your business domain
- • Map business logic and decision workflows
- • Build semantic reasoning layer
- • Configure AI reasoning rules and heuristics
- • Create decision frameworks and approval chains
AI Agents & Governance
- • Develop AI agents on ARPIA Platform
- • Configure governance policies (RBAC, data access)
- • Build approval workflows and human-in-loop controls
- • Implement compliance controls (SOX, HIPAA, GDPR)
- • Create user interfaces (AppStudio or MCP integration)
Deployment & Knowledge Transfer
- • Deploy to production environment
- • Run pilot with real users and real data
- • Train your team on ARPIA Platform
- • Document architecture, workflows, operations
- • Conduct knowledge transfer sessions
What You Actually Get
This isn't consulting theater. You get working AI deployed to production, plus everything needed to scale independently—no ongoing dependency on our team.
Built and Deployed
Working Use Cases in Production
Deployed, tested, validated with real users. Connected to your actual systems (not demos). Governance policies enforced automatically.
Configured ARPIA Platform
Knowledge ontology mapped to your domain. Data integrations to ERPs, CRMs, databases. AI agents with your business logic. Audit trails and compliance controls.
Documentation Package
Architecture diagrams and data flows. Governance policies and RBAC configuration. API integration guides. Operational runbooks.
Ready to Scale
Team Training
Hands-on ARPIA Platform training. Knowledge ontology management. AI agent development workshops. Governance policy configuration.
Technical Enablement
Access to ARPIA Academy courses. Private Slack channel with AI engineering team. 30 days post-deployment support. Monthly office hours for next use cases.
Ongoing Support
Platform updates and new features. Community access (forums, best practices). Optional: Quarterly strategic reviews.
After 30-90 days, you have working AI in production AND the capability to deploy your next 10 use cases independently.
Real Use Cases, Real Results
Every use case shown on our Industries page was deployed by ARPIA AI engineers in 30-90 days. Here's what customers achieved with full-stack deployment.
Clinical AI Assistants for Care Teams
AI assistants with governed access to complete patient history, clinical guidelines, drug databases, and medical literature—reducing documentation burden and improving care quality.
- Clinicians spend 2+ hours on documentation per patient hour
- Critical patient information buried in 300+ page charts
- Drug interaction checks require manual lookup
Deployed: Full-stack AI with HIPAA-compliant governance, knowledge ontology for clinical domain, integration to EMR systems. Role-based access for 2,000+ clinicians.
Proactive AI: Intelligent Task Generation
Proactive AI continuously monitors transaction patterns, account behavior, and market signals—detecting anomalies early and automatically generating prioritized tasks for fraud detection, credit risk, and compliance.
- Critical issues discovered too late (fraud, credit risk)
- Teams overwhelmed with alerts, miss what matters
- Manual monitoring can't scale across portfolios
Deployed: Real-time data integration from core banking systems, knowledge graph for risk patterns, proactive AI agents with governance policies. 80% reduction in false positive alerts.
Proactive AI: Quality & Predictive Maintenance
Proactive AI continuously monitors production sensors, equipment health, and quality metrics—detecting anomalies early and generating prioritized tasks before issues become costly failures or defects.
- Equipment failures cause unplanned downtime ($M/hour lost)
- Quality issues discovered after entire batch produced
- Sensor data overwhelming—teams miss critical signals
Deployed: Sensor data integration, ERP connectivity, knowledge graph for equipment patterns, predictive maintenance AI agents. $6.5M annual savings from prevented downtime & scrap.
Meet Your AI Engineering Team
You're not getting project managers or implementation consultants. You're getting AI engineers, data architects, and governance specialists with deep enterprise deployment experience across Financial Services, Healthcare, Manufacturing, and Government.
Principal AI Engineer
Enterprise AI architecture design, full-stack system design, knowledge ontology modeling, multi-system integration strategy, governance framework design.
AI Engineer
AI agent development on ARPIA Platform, LLM integration and prompt engineering, reasoning workflow design, model evaluation and optimization, production AI deployment.
Data Integration Engineer
ERP/CRM/database integration, API development and MCP protocol, data pipeline architecture, real-time data synchronization, data quality and validation.
Governance Specialist
AI governance policy design, regulatory compliance (SOX, HIPAA, GDPR, FedRAMP), RBAC configuration and access controls, audit trail implementation, risk management and security.
Typical Deployment: 2-4 AI engineers (based on use case complexity)
Always: 1 Principal AI Engineer (your main point of contact)
Plus: Specialized engineers based on your technical requirements
AI Engineering & Full-Stack Deployment FAQs
Let's Build Your Full-Stack AI Together
Schedule a 30-minute engineering call with our AI engineering team. We'll discuss your use case, technical requirements, timeline, and whether ARPIA's full-stack approach is the right fit.
No sales pitch—just engineers talking about what's possible.