AI Agent Development

We build production-grade AI agents that run inside your operations: tool calling, memory, retrieval, and the guardrails to ship them safely. Not chatbot demos. Agents that do real work and stay reliable.

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The pattern we see

A demo agent is easy. A reliable one is the hard part.

Agents that work on happy-path inputs and break on real ones
No memory, no retrieval, no grounding in your actual data
No guardrails, logging, or evals, so you cannot trust the output
Locked to one model vendor with no way to swap or self-host
Wiring an LLM to a prompt takes an afternoon. Making an agent that handles real inputs, calls the right tools, recovers from errors, and does not go off the rails in production is a different discipline. Most AI agent projects stall there: impressive in a demo, untrustworthy on Monday.
What we build

Agents engineered like production software, not prompts.

We build agents against a model interface, not a single vendor, with tool calling, memory, retrieval over your data, and the guardrails and evals that make them safe to run unattended. They plug into the systems you already use and are documented and owned by your team.
Tool calling wired to your real systems and APIs
Memory and RAG grounded in your documents and data
Guardrails, human-in-the-loop, logging, and an eval suite
Model-agnostic: swap vendors or self-host as a config change
See where an agent would pay off

Agents we build

Production agents aimed at the work that eats your team's time, not novelty.

  • Lead and ticket triage

    Agents that score inbound leads, classify and route support tickets, and draft first replies grounded in your knowledge base, with humans on the exceptions.

  • Document and data agents

    Extract, classify, and summarize from contracts, invoices, and forms; answer questions over your SOPs with retrieval, not hallucination.

  • Workflow orchestration

    Agents that drive multi-step processes across your tools, deciding the next action, calling the right system, and escalating when they should.

  • Internal copilots

    Assistants grounded in your internal knowledge so your team can ask 'how do we do X?' or 'what is the status of Y?' and get a real, sourced answer.

How we build them

Production-grade, not prototype

Access control, logging, error recovery, and an eval suite are first-class outputs. If an agent's behavior changes, we catch it before your users do.

Grounded in your data

Retrieval over your real documents and systems, so answers are sourced and current, not made up. We treat hallucination as a bug, not a quirk.

Model-agnostic

Built against a model interface. Swapping Claude for another model, or moving an internal agent to a self-hosted open-source model on your own GPUs, is a config change plus a re-run of the evals.

Owned by your team

Documented, handed over, and runnable without us. We can stay on to operate and extend them, but you are never locked in.

Frequently asked questions

What is the difference between an AI agent and a chatbot?

A chatbot answers questions. An agent takes actions: it calls tools, makes decisions across steps, and completes work, with guardrails and human review where it matters. We build the latter.

Can the agent use our own data without leaking it?

Yes. We ground agents in your data with retrieval and respect your data boundaries, including self-hosted or open-source models on your own infrastructure when confidentiality requires it.

Which models do you build on?

We build against a model interface rather than a single vendor, so you are not locked in. We pick the model that fits the task and budget, and swapping is a config change plus a re-run of the eval suite.

How does this relate to your other services?

Agent development is the build capability. It usually sits inside a broader automation engagement, where the agent is one part of a connected system rather than a standalone toy.

Start here

Start with Your Efficiency Scorecard

Ten minutes. It surfaces where an AI agent would actually pay off in your operations, and where it would just be expensive noise. Whether we work together or not.

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