Customer support agent
Handles refunds, order lookups, and account changes against your real systems. Every tool has a narrow scope and a spend cap. Hands off to a person with the full conversation attached whenever your policy says so.
Agents that do the work, not the demo.
Production AI agents wired to your tools and data. We design, deploy, and operate them — with evals, observability, budget caps, and a kill-switch.
Where agents fit
Six shapes of production agent.
Start with your busiest queue. Each shape ships alone, with its own evals and budget.
Sales SDR agent
Qualifies inbound leads, books meetings on your calendar, and routes to a rep when intent crosses your threshold. Lives in your CRM, not a side panel.
HubSpotSalesforceCalendars
Research agent
Reads across your docs, tickets, and approved web sources. Returns cited answers and structured output your team can paste straight into a brief.
RetrievalCitationsJSON out
Ops agent
Watches queues, alerts, and metrics. Proposes a fix for each anomaly. Runs only the fixes on your pre-approved runbook list.
AlertsTriageRunbooks
Internal data agent
Answers plain-English questions over your warehouse. Writes the SQL, runs it, and charts the result. Every query is logged and bounded by your row-level permissions.
Voice agent
Covers phone-first work — appointments, intake, basic support. Tells callers it is an AI up front. Transfers to a person on request and writes the transcript to your CRM.
The hard part
Keeping agents inside the lines.
Demos work because the inputs are friendly. Production inputs are not. We treat every agent like a regulated system: evaluated against your data, traced in real time, and bounded by budgets it cannot escape.
- An eval suite built from your real transcripts runs on every prompt, tool, or model change.
- Per-task budget caps and a hard kill-switch live inside the agent loop, not on a dashboard.
- Every run is traced — prompt, tool calls, latency, and cost.
- Actions above a threshold you set wait for a person to approve them.
How it goes live
Shadow mode before live traffic.
Trust comes in stages. The agent drafts before it acts. It acts on a slice of traffic before all of it. Each stage has an exit gate you sign off before we start.
- Shadow: the agent drafts, a person sends. We score its drafts against what your team did.
- Canary: a small share of real traffic, with the kill-switch one click away.
- Gates are written down before launch — eval score, escalation rate, cost per task.
- Handover includes runbooks, dashboards, and an on-call guide for your team.
Day 1
Code, prompts, and eval sets in your repo
Every change
Eval suite runs on prompt, tool, and model changes in CI
100%
Of agent runs traced — prompt, tools, latency, cost
2 wk
Demo cadence on a working agent
Every prompt change now ships with an eval score. A bad change gets caught in CI, not by a customer.
Questions
What buyers ask us first.
How long until an agent handles real work?
We plan for a narrow first agent to reach shadow mode in four to six weeks. Live traffic follows once it clears the gates you signed off. Broader scope adds time, and we size it after discovery.
Which models do you build on?
Hosted models from the major providers, or open-weight models you run yourself. Your evals, cost targets, and data rules decide. A routing layer keeps the choice reversible.
What do you need from us?
Sandbox access to the systems the agent will touch, a sample of real tasks or conversations, and one owner who can say what correct looks like. Nothing touches production until the gates pass.
Who owns the agent when you're done?
You do. Code, prompts, eval sets, and dashboards live in your repo and your cloud accounts. You can run it yourselves or keep us on to operate it.
How is it priced?
Each engagement is scoped and priced after a discovery call. Model and hosting usage is billed to your own provider accounts, so you see the real running cost.
Ready when you are
Tell us what your team is doing by hand.
A 30-minute discovery call. We'll tell you whether an agent is the right tool before you book a second one.