What We Do
Every engagement follows the same three-phase model. It's designed for one outcome: a fully operational agentic team your engineers own and run — without calling us.
Diagnose
Every engagement starts here. We run a structured audit of your workflows, data, and team to identify where agentic AI creates the most leverage. Output: a prioritized implementation roadmap with effort estimates and expected ROI per initiative.
Duration: 2-4 weeks
Deliverable: Agentic Roadmap
Build
We design, build, and test your agentic systems alongside your engineers. Not black-box delivery — your team is embedded in every sprint. Stack is standardized (LangGraph + LangSmith + Postgres + AWS) and documented to the component level.
Duration: 8-16 weeks
Deliverable: Running systems, full codebase
Transfer
The engagement ends when your team is fully operational. We deliver runbooks, architecture decision records, LangSmith eval suites, and training sessions. You run it. We stay on call for 30 days.
Duration: 2-4 weeks
Deliverable: Runbooks, evals, training, 30-day hypercare
Our Stack
We're opinionated because we've made the tradeoffs so you don't have to.
| Layer | Technology | Why |
|---|---|---|
| Agent framework | LangGraph + LangChain | Production-proven, auditable, debuggable |
| Observability | LangSmith | Traces, evals, prompt management |
| Memory and retrieval | Postgres + pgvector | Open-source, self-hostable, no vendor lock |
| Tool protocol | MCP | Emerging industry standard |
| Cloud | AWS | Largest talent pool; broadest AI service catalog |
| IaC | Terraform | Most widely recognized; easy client handoff |
| Language | Python 3.12+ | Largest LLM/AI ecosystem; type-safe with mypy |