→ 01
Defined scope, narrow blast radius
Every agent has a clear job, a clear set of tools, and a clear stop condition. No open-ended chatbots pretending to be agents.
01/CUSTOM AI AGENTS
THE PROBLEM You have repetitive multi-step work — research, drafting, triage — that one person could automate, but no one has the time.
AI that does the work — not just suggests it.
We design and ship custom AI agents that take an instruction and run a sequence of steps to actually complete the work — calling APIs, reading documents, drafting outputs, and writing back to your systems. We pair them with an evaluation harness so you can trust them in production.
→/OUTCOMES
→ 02
Tool-use, not just text
Agents read and write to your real systems via APIs you control — Notion, Google Workspace, Stripe, your database, internal tools.
→ 03
Evals from day one
We ship a test set with the agent. Quality regressions show up on a dashboard, not in customer complaints.
→ 04
Plain-English handover
Source code, prompts, and runbooks live in your repo. Your team can change behavior without us.
→/TOOLING
A representative — not exhaustive — set of tools we reach for on custom ai agents engagements. We pick by fit, not by brand loyalty.
- Claude (Anthropic)
- OpenAI APIs
- LangChain / LangGraph
- MCP (Model Context Protocol)
- TypeScript / Python
- PostgreSQL / pgvector
- Vercel / AWS Lambda
→/PROCESS
STEP 01
Pick the job
We work with you to identify a single, narrow task an agent can own — not a vague 'AI assistant.'
STEP 02
Design the tools
We map the agent's permitted actions to the APIs and tools it needs. Anything outside that map is off-limits by design.
STEP 03
Build + evaluate
We ship a working agent on a test set, iterate on the prompts and tooling until the eval scores hold up.
STEP 04
Deploy + monitor
Production rollout with logs, evals, and a kill switch. You see how it's doing.
→/FAQ
What's an 'agent' versus 'just an AI integration'?
An integration calls an LLM once and uses the answer. An agent runs a loop — reasoning, calling tools, reading their results, deciding what to do next. We use whichever the problem deserves.
How do we trust it?
We build an eval set during the engagement and run it against every change. You see scores before deploys.
Whose API key gets billed?
Yours. You own the keys, the prompts, and the cost.