AI that moves a KPI — not just impresses a board.
Production AI tied to ROI, time-to-market, and governance — not experimentation theater. Built on AWS Bedrock and Anthropic Claude. Private RAG environments, document intelligence, agentic workflows, and copilots grounded in your data and governed properly. We build the layer beneath the vendor demos.
Every SaaS rep is selling AI. Most of it won't move a KPI.
The AI co-pilot in your CRM. The AI feature in your help-desk. The AI module in your core banking platform. Vendors are racing to bolt AI onto everything — and most of those bolts won't survive a real production load, a real audit, or a real ROI conversation.
The Foundry of AI by PEXIVA exists to build the layer underneath. Private GenAI deployments on AWS Bedrock and Anthropic Claude. RAG environments grounded in your real data, evaluated continuously, governed properly. Agentic workflows that automate where the math actually works. Copilots that earn their seat license. Production AI, not demoware.
What "Production AI" Actually Means
Five things that separate a real GenAI system from a sandbox demo.
- Private deployment — your data never hits a public model
- Continuous evaluation — quality measured, not assumed
- Governance & lineage — for audit, not just engineering
- Performance discipline — latency, cost, throughput targets
- Operate-ready — runbooks, monitoring, drift detection
From prototype to governed production.
The operating model behind production AI — grounded in your data, governed by controls, and observable once it is live. Executive confidence comes from traceability: what data grounds it, what controls govern it, and what KPI justifies it.
Six AI capabilities, delivered as production systems.
PEXIVA's AI practice — the Foundry — exists to ship production-grade AI for mid-market leaders. Not workshops. Not pilots that never leave the lab. Real systems with real KPIs, monitored continuously and governed properly.
Private RAG Environments
Retrieval-augmented generation systems built on AWS Bedrock or Anthropic Claude, grounded in your enterprise data. Document chunking, embedding strategies, vector storage, retrieval ranking, and continuous evaluation — all on infrastructure your security team approves of.
Agentic Workflows & Automation
AI agents that execute multi-step business workflows — claims triage, policy lookup, document processing, customer-service routing — with proper guardrails, human-in-the-loop checkpoints, and full audit trails. LangChain, LangGraph, or framework-agnostic patterns.
Document Intelligence Systems
Production-grade document processing for back-office workflows — invoices, contracts, medical records, claims, regulatory filings. Combines GenAI extraction with classical OCR and validation rules. Built to drive measurable cost reduction in document-heavy operations.
Internal Copilots & Knowledge Assistants
Private copilots for sales, customer service, engineering, and back-office teams — grounded in your company's actual knowledge base, not a generic LLM. Role-aware permissions, integrated authoring loops, and usage analytics that prove ROI.
MLOps & AI Governance
End-to-end MLOps for both GenAI and traditional ML — model registry, evaluation harnesses, monitoring, drift detection, retraining pipelines, and governance frameworks that satisfy audit and regulatory scrutiny. SageMaker, Vertex AI, or open-source toolchains.
AI Strategy & Readiness Assessments
Where can AI actually move the needle in your business? We triage use cases by feasibility and impact, evaluate your data foundations, identify the highest-ROI starting points, and produce an executable 12-month roadmap board members can actually follow.
Production AI you can trust in front of a regulator.
We build AI that ships into real operations — governed, evaluated, and monitored — not demoware. Our approach is designed to align with the NIST AI Risk Management Framework and to fit inside your own security and governance requirements.
Evaluation, not vibes
Every model and agent is measured against task-specific evals and acceptance criteria before it reaches production — with regression checks as prompts, models, and data change.
Human-in-the-loop by design
Consequential decisions keep a person in the loop. We define where AI assists versus decides, and build the review and override paths to match the risk.
Guardrails & model-risk awareness
Prompt and agent guardrails, input/output validation, and abuse and jailbreak handling — scoped to the sensitivity of the data and the decision.
Knowledge grounding
Retrieval and grounding tie answers to your trusted sources, with citations and confidence cues, so the system can say what it knows — and what it doesn’t.
Observability & monitoring
Logging, tracing, drift and quality monitoring, and cost controls in production — so issues surface as signals, not surprises.
Secure, private deployment
Private and VPC-isolated patterns, data-residency control, and “no training on your data without agreement” — designed for regulated and mission-critical environments.
Platforms and the offerings built on them.
We separate the platforms we work across from the offerings clients actually buy — with governance and public-sector readiness treated as first-class, not afterthoughts. Vendor-neutral by design; we recommend what fits your environment.
Platforms AI foundation
Platforms Data & warehouse
Platforms Data engineering & orchestration
Platforms Governance & trust
Offerings AI solution patterns
Offerings Data & ML
Readiness Public-sector & regulated AI
Framework references describe how we design and operate environments to align with these standards — not a claim of independent certification. Certifications and BAAs are scoped per engagement.
AI & GenAI — frequently asked questions
What GenAI work does PEXIVA do?
What is RAG and when do we need it?
Which models do you use?
How do you keep AI accurate and safe?
How do you measure AI ROI?
How do we get started?
Where could AI actually move a KPI in your business?
AI co-pilot strategy. Document intelligence pilot. Private RAG for regulated workloads. Agentic automation for back-office. We'll come prepared with a use-case triage, an architecture diagram, and an honest read on what's worth building.