AI Agents
Support, lead follow-up, and repetitive internal work handled around the clock, with a person in the loop where it matters.
An agent is only useful if you can trust what it says when nobody is watching.
We start from the conversations and tasks you already have (tickets, inbound leads, internal requests) and work out which parts genuinely do not need a person. That scoping happens with the engineer who will build the thing rather than a salesperson, so what we quote is what gets shipped.
The build covers retrieval over your own content, guardrails on what the agent may promise, and clean escalation to a human the moment it hits the edge of its competence. Every agent goes out with an evaluation suite so you can see, in numbers, how it behaves before it touches a customer.
6 stages, run by the same 2 or 3 people from first call through the tuning window.
Workflow audit
We sit with the people doing the work and map where time actually goes, which cases repeat, and which ones need judgment.
Scope and guardrails
We agree what the agent handles, what it never handles, and what happens at the boundary. This is written down before anything is built.
Knowledge wiring
Your documentation, policies, and history get structured and indexed so retrieval returns the right passage rather than a plausible one.
Build and evaluate
The agent is built against a test set drawn from your real cases, and scored on every iteration until it clears the bar you set.
Shadow run
It runs alongside your team without customer contact, so you can compare its answers to theirs and correct the gap.
Launch and tune
Go live on a narrow slice, widen as the numbers hold, and keep tuning through the support window.
Questions
Will an AI agent sound robotic to my customers?
We use your approved source material and tone, then test real conversation patterns. The agent follows those rules and hands uncertain or sensitive cases to a person.
What can an AI agent actually handle?
Common support questions, order and shipping requests, lead qualification, follow-up, routing, and repeatable internal tasks. We choose the first use case based on volume, risk, and the quality of the available source material.
What happens after it's built?
We review performance, update source material and rules, and maintain the connected workflows. Decisions and ownership are documented so the system does not depend on one person.