Production AI
Most agentic AI in 2026 doesn't ship. We build the kind that does — stateful agent orchestration with LangGraph, MCP tool integration, context engineering for retrieval and memory, and evals that run in CI, operated in production with real customers, real load, and real failure modes.
A production multi-step agent pipeline on AWS — S3 trigger → analysis agents → EventBridge orchestration → persistence and UI generation → completion and post-processing. Idempotent, observable, with checkpoints and interrupt points for human review. Powering a multi-tenant AI platform.
What we build
Stateful agent orchestration with LangGraph — StateGraph-based agent control flow with durable execution, checkpointing, and native interrupt primitives for human review. Applied across sports, marketing, finance, and connected-media products.
MCP servers and tool integration — Bridging Claude and other agents to enterprise tools and data systems.
Multi-provider LLMs — Claude, OpenAI, Gemini, and self-hosted open-weight models.
Context engineering & agentic retrieval — Designing everything in the model's context window, not one-shot RAG: agentic retrieval that decides when to retrieve, judges sufficiency, fills gaps, and synthesizes — over vector stores (pgvector, Pinecone-class) and graph databases (Neo4j-class), with reranking and memory.
Evals & continuous evaluation — Offline eval harnesses plus online, continuous evals shipped in CI. Eval-drift tracking, regression gates, grounding and safety scoring.
Domain-specific ML — Fine-tuning, vision, audio, time-series.
Production deployment — Trace-level observability with independent alerting on eval drift, latency, cost, and error rate, plus audit logging, idempotency, and replay.