Agents that do real work.

What should the agent own, and what should it never touch? We start there. Scoping an agentic system is mostly about drawing the boundary between judgment and execution, and we design that boundary before we write a line of code: tools, permissions, escalation paths, and the human checkpoints that make the whole thing trustworthy.

From single-purpose agents that handle one workflow flawlessly to multi-agent systems that research, draft, review, and deliver. We build on the models and frameworks that fit your stack, not whichever vendor bought lunch last.

Agents are only as capable as the tools they can reach. We build MCP servers that give a model clean, typed access to your systems: structured tool schemas, validated inputs, explicit boundaries, and response shapes an agent can actually act on. The same server that powers an autonomous agent can answer a question in a chat client, so the work you do once pays off in both.

An agent that guesses is worse than no agent. We ground answers in your real data with retrieval-augmented generation: embeddings, vector search over pgvector, entity extraction, and multi-source retrieval that pulls the right context from the right place. The model stops inventing and starts citing what your systems already know.

Your business already runs on software that works. The fastest wins come from connecting intelligence to it: your CRM, your inbox, your docs, your data. We wire AI into the tools your team already lives in, so adoption is not a separate project. Usually that means MCP servers and retrieval systems built for your stack.

How do you know it works? Not vibes: evals. Before an agent touches production we define what correct looks like, build the test harness that measures it, and keep measuring after launch. We compare models head to head rather than guessing, so the choice between OpenAI, Anthropic, and open-source models is a number, not a hunch. If the system degrades, you hear it from the dashboard, not from a customer.

Demos are forgiving. Production is not. Rate limits, retries, fallbacks, observability, cost ceilings, JSON output constraints, and deterministic code for the parts that should never be left to a model. The unglamorous engineering that separates a clever prototype from a dependable system.