What we do — and how we do it.
We embed with engineering and product teams for four to twelve weeks, working hands-on from architecture decisions through to a production-ready system with real evaluation coverage. Every design decision is documented in Architecture Decision Records. Every agent behavior is covered by an eval scenario before a line of agent code is written.
No juniors, no slide decks. You get a runnable reference implementation, documented failure modes, and a team that knows how to operate what we built.
- Agentic system architecture & multi-agent orchestration
- Eval-first engineering — harness design before agent code
- Trust & safety frameworks — guardrail stacks and HITL design
- LLM selection, routing, and benchmarking
- Production MLOps — deployment, monitoring, cost modeling
- Vertical reference implementations for your domain
Agentic AI for Shipment Exception Management
A production-grade reference implementation for an agentic exception management system built on LangGraph, FastAPI, and LiteLLM. Covers the full stack: business problem, 6-agent architecture, state machine lifecycle, 7-layer guardrail framework, eval-first engineering, and a runnable deployment on AWS.
- The Problem — Four structural failure modes of manual exception handling at scale
- Agentic Control Tower — 6-agent architecture with exception lifecycle state machine
- Data Layer — Bronze → Silver → Gold pipeline with circuit breaker patterns
- Trust & Safety — 7-layer guardrail stack, HITL design, four "never" rules
- Eval-First Engineering — Four-pillar eval framework, pass@k vs pass^k
- Implementation & Deployment — LangGraph wiring, Terraform, AWS cost model
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