How long does it take to build an AI agent?

For a single, well scoped workflow, days to a few weeks depending on how many systems it has to touch and how clean the data is. The scoping gate exists so that estimate is grounded rather than guessed.

Will it work with our existing systems?

That is the requirement, not a bonus. Agents are built into the CRM, helpdesk, ERP or inbox your team already lives in. If a system has no usable interface we say so during scoping rather than after invoicing.

What happens when the agent gets something wrong?

Every agent ships with defined failure modes, a confidence threshold and a human escalation path. Wrong answers are logged and fed back into the evaluation set.

You do, along with the code, the prompts and the evaluation set. You can run it in house or have us operate it.

Build

Custom AI agents for business

An agent is production software. It needs a scope, a test set, a fallback and someone accountable when it is wrong.

The short answer

A custom AI agent is worth building when a workflow is high volume, rule shaped at the edges and expensive in human time, and when the cost of an occasional wrong answer is recoverable. Build one workflow first, evaluate it against a real test set, put a human fallback behind it, then scale. Firms that start with five agents at once usually ship none.

What you get

Every engagement leaves something behind that works without us in the room.

01

One workflow scoped, built and integrated into the systems you already run

02

An evaluation set drawn from your real cases, scored before anything goes live

03

Human in the loop fallbacks and escalation paths on every failure mode

04

Monitoring, logging and cost controls from day one

05

Documentation your own team can maintain, plus the option for us to run it

How it runs

Fixed price per workflow.

Scope

One workflow, written down: inputs, outputs, edge cases, what good looks like and what must never happen.

Build and integrate

Built against your stack, your data and your permissions model. No parallel shadow system to maintain.

Evaluate

Scored against a test set of your own historical cases. If it does not clear the bar, it does not launch.

Run and scale

Monitoring, model upgrades and the next workflow, once the first one has earned it.

What it costs