Solutions / 01 — AI Agents

Agents that act,
not just answer.

We build custom AI agents that resolve real tickets, run real workflows, and integrate cleanly with your existing tools — wrapped in guardrails and evals so you can actually trust the output.

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What we build

Three shapes of agent, customised.

Every agent is shaped around your data, your tools, and your tolerance for risk. These are the patterns that show up most often.

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Customer-facing agents

Support agents that answer from your docs, resolve common cases end-to-end, and escalate to humans with full context. Plugs into Zendesk, Freshdesk, Intercom, or a custom inbox — without losing the audit trail.

02 / 03

Internal ops agents

Agents that run the unglamorous workflows: approvals, reconciliation, document processing, internal request handling. Built to work alongside your team, not around them.

03 / 03

Guardrails & evals

Hallucinations are reduced with grounded retrieval, tool constraints, output validation, and an evaluation suite built from your real cases — so you can ship a change without crossing your fingers.

What you get

A working system. Not a deck.

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Working agent in production

Deployed to your environment — your cloud, your keys, your data — with clean logs, traces, and a kill switch.

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Eval suite & runbook

Tests built from real cases so future changes are graded honestly. Plus the runbook your on-call will actually use.

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Clean handover

Code you can read, infra you own, and as much support as you want afterwards. We don't rent you our maintenance forever.

Common questions

Asked before every agent build.

How much does it cost to build a custom AI agent?
Scoping comes first: one week to align on the outcome and the smallest useful slice, after which you get a written plan and a fixed price — no open-ended retainers. A scoped pilot typically takes 2–3 weeks; production-ready agents with integrations, evals, and observability take 6–10 weeks.
Which LLMs and frameworks do you build agents with?
OpenAI, Anthropic Claude, and open-weight models, orchestrated in Python on FastAPI with Postgres and Redis underneath. Tools connect over APIs and the Model Context Protocol (MCP), and we design so the model provider can be swapped later.
Can the agent run on our own infrastructure?
Yes. Private deployment is the default — your cloud, your keys, your data. Nothing is used to train public models.
How do you keep an AI agent from hallucinating?
Grounded retrieval, constrained tool access, output validation, and an eval suite built from your real cases — plus clear escalation paths to humans when confidence is low.

Next step

Have a workflow you'd like an agent for?

Send the rough version. We'll turn it into a scoped pilot — fixed price, written plan, two business days.

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