AI agent development for startups and SMBs, built to reach production

An AI agent is software that uses a large language model to plan and complete a multi-step task with limited supervision. It decides what to do, calls tools or APIs to take real actions, observes the result, and continues until the task is done. Unlike a chatbot that only replies with text, an agent can read context, retrieve data, and act inside your systems.

A chatbot answers questions in text; a RAG assistant answers questions grounded in your documents; an AI agent goes further and takes actions: calling tools, updating records, or running a multi-step workflow to actually complete a task. Many real systems combine all three: retrieval to stay grounded, conversation for the interface, and agentic tool-calling to get work done.

Use the agents built into tools you already pay for (Copilot, ChatGPT Enterprise, Agentforce) when the task lives inside that one platform. Build a custom agent when the task spans several systems, needs your private data and permissions, must follow your own rules, or has to be measured and trusted in production, which platform agents and no-code builders cannot do reliably.

We ground the agent in your real data with retrieval and citations, scope its tool permissions so it can only do safe things, add human-in-the-loop checkpoints before high-risk actions, and build an evaluation suite that measures accuracy on real cases. Guardrails, fallbacks, and production monitoring catch the rest. This evaluation layer is what separates a reliable agent from a demo.

We measure cost per task during the pilot, then route simple steps to smaller models, cache repeated context, set token budgets, and track spend in production. That keeps cost predictable as usage grows, and tells you before launch what each completed task will cost.

It covers one real workflow, built and running on your own data; an evaluation set that measures accuracy before launch; and the cost per task with a production rollout plan. It is a fixed-scope first step, so you decide whether to take the agent to production based on measured results, not a demo.

We are model-agnostic and choose per use case: Claude (Anthropic), OpenAI GPT models, or open models like Llama and Mistral where data residency or cost matter. We build with Python, FastAPI, and Django, orchestrate with LangGraph or the OpenAI and Claude agent SDKs, connect tools via the Model Context Protocol (MCP), retrieve with pgvector or Pinecone, and trace and evaluate so the system stays measurable.

It depends on the task, how many systems the agent touches, and how much evaluation the risk level demands. We start with a fixed-scope Agent Pilot Sprint on one real workflow, which gives you a working agent, measured accuracy, and a fixed estimate for the production build. From there we add grounding, integrations, guardrails, and evaluation before a staged rollout with a human in the loop.

Yes. You own all source code, prompts, evaluation suites, and intellectual property we produce. Everything is committed to your repositories as we build, with no lock-in, so you can run, extend, or bring the work in-house at any time.