AI Agent Development Company & Custom Agents
Agents that do real work.
Custom AI agents that pull from your data, reason about it, and take action — inside scoped permissions, with audit trails and evals. We're an AI agent development company that measures success in hours saved per week, not in demo wow.
What we build
Agents with a job description.
/ 01
Custom AI agents
Multi-step agents for a specific job — triage, research, drafting, data entry — wired to your systems and data, acting only within permissions you've scoped.
/ 02
AI chatbots
Customer-facing and internal chatbots that answer from your real knowledge base — with honest "I don't know" behaviour instead of confident nonsense.
/ 03
Tool & data wiring
RAG over your documents, MCP and API integrations, structured outputs — the plumbing that turns a chat model into an agent that can actually do things.
/ 04
Evals & guardrails
Test suites for agent behaviour, monitoring, cost controls, and audit trails — so you know what the agent did, and it stays good as models change.
Tools we reach for
- Anthropic Claude
- OpenAI
- Gemini
- Vercel AI SDK
- MCP
- n8n
How we work
Scope
One job, defined like you'd brief a new hire: inputs, outputs, edge cases — and what the agent must never do.
Wire
Connect data and tools with scoped permissions. The agent gets exactly the access the job needs — no more.
Prove
A working agent on real cases within weeks — judged against evals and against the human baseline it's meant to relieve.
Operate
Monitoring, cost tracking, and regression evals as models change. An agent is a system to run, not a feature to ship.
FAQ
Questions we get a lot.
- What does an AI agent development company actually build?
- Software agents with a defined job: they read from your systems, reason with a language model, and act — file, draft, route, update — inside scoped permissions. Plus the unglamorous parts that make it dependable: data wiring, evals, monitoring, audit trails.
- How much does custom AI agent development cost?
- Scope drives it — a single-job agent on existing data is a matter of weeks, not months. We start with the smallest agent that proves the value on your real cases, so the spend follows the evidence.
- Do we need an AI agent or just a chatbot?
- A chatbot answers questions; an agent completes tasks. If the goal is "let people ask about X", build the chatbot — it's simpler and cheaper. If the goal is "this work should happen without a human doing every step", that's an agent. We'll tell you which one your case actually needs.
- Which models and frameworks do you use?
- Claude, GPT, and Gemini, picked per task; agent tooling and protocols like the Vercel AI SDK and MCP where they earn their place. The stack follows the job — never the other way around.
- How do you keep an AI agent safe and reliable?
- Scoped permissions — the agent can only touch what its job requires — audit trails for every action, eval suites that catch regressions, and GDPR-conscious data handling. Autonomy is earned in steps, not granted on day one.
Related services
AI consulting & strategy
Not sure an agent is the right move? Start with an audit and a roadmap that's honest about it.
AI automation & workflows
Some jobs don't need an agent — a well-built workflow automation saves the hours at lower cost.
AI & LLM integration
AI as a product feature — Claude, GPT, or Gemini wired in with RAG, evals, and cost controls.
Working together
Have a job an agent should own?
Describe the task and the systems it touches — we'll tell you what an agent can realistically do, and what it shouldn't.