Enterprise AI Architecture & Agentic Systems
Moving beyond fragile prototypes. Bridging 20 years of database normalization, high-concurrency routing, and air-gapped infrastructure with modern agentic execution and verifiable RAG pipelines.
Where AI Prototypes Fail In Production
Building a demo takes an afternoon. Building a production AI system that survives hostile data, regulatory audits, and peak load requires rigorous systems architecture.
Data Sovereignty & Model Exposure
Sending proprietary enterprise or citizen data to public model endpoints creates catastrophic compliance risks under GDPR, the EU AI Act, and institutional donor mandates.
Probabilistic Non-Determinism
Naive prompt wrappers produce hallucinations and inconsistent output schemas that break downstream transactional databases and operational software.
Token Runaway & Latency Spikes
Unoptimized context windows and naive multi-agent loops cause exponential token cost inflation and multi-second request latency that degrades production UX.
Brittle Prototype Debt
Demo-grade LangChain or notebook scripts lack retry logic, circuit breakers, semantic caching, and regression evals required for real-world enterprise uptime.
Engineered For Determinism & Scale
Data-Sovereign RAG & Knowledge Systems
Enterprise retrieval-augmented generation built on PostgreSQL with pgvector, hybrid dense/sparse search, and local or private VPC LLMs. Engineered with strict role-based document access controls (RBAC) and zero data leakage to public model training sets.
Deterministic Agentic Automation & ETL
Persistent multi-agent task loops that execute complex multi-step business logic, data harvesting, AST code transformations, and unstructured-to-structured document ingestion without manual human bottlenecking.
AI Architecture Feasibility Sprint
A 1-to-2 week fixed-scope architectural audit. We analyze your data readiness, benchmark token economics, validate privacy boundaries, and deliver a turnkey Architecture Decision Record (ADR) before you commit capital to development.
AI Production Hardening & Token Tuning
Rescuing and stabilizing fragile AI prototypes. We implement semantic caching layers, automated eval harnesses, fallback routing, and token optimization strategies that reduce API overhead by 40% to 70% while restoring sub-second response times.
How We Deliver Production AI
Data Readiness & Threat Modeling
Evaluating source documents, privacy boundaries, compliance exposure, and retrieval volume to establish non-negotiable architectural constraints.
Schema & Boundary Orchestration
Designing vector schemas, embedding strategies, chunking heuristics, agentic state machines, and API contracts before code execution.
Hardened Production Delivery & Evals
Deploying resilient services with automated regression evals, token monitoring, semantic caching, and graceful failure fallbacks.
What Production Architecture Guarantees
Zero-Leakage Vector Storage
Self-hosted or VPC-isolated vector databases with deterministic indexing and strict tenant data isolation.
Deterministic Structured Outputs
Strict JSON schema enforcement and Pydantic validation pipelines ensuring zero broken API payloads.
Regression Eval Harnesses
Automated ground-truth test suites preventing model drift, regression, or hallucination in production.
Token & Latency Observability
Real-time tracking of token expenditure, per-step latency, and semantic cache hit rates across all agentic paths.
Architect Your AI System For Production Reality
Whether you need a private, data-sovereign RAG pipeline, automated agentic pipelines, or an architectural audit of an existing prototype, let us discuss your requirements honestly.