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From the service wall

scope, price, finish line

AI Agents & Assistants

Support, ops, and knowledge agents that actually know your business.

The roster

sound familiar?

Who books this.

  • Support and CX leaders

    Ticket volume keeps climbing and most of the answers already live in your help center. You want faster first response without the experience feeling like a machine wrote it.

  • Operations and IT directors

    Your queue is a routing desk: access requests, status checks, where-things-live questions. You want the routine handled and the exceptions escalated to a person with context.

  • HR, enablement, and knowledge owners

    Policy sits in one system, process in another, and the real answers sit in a few veteran heads. You want employees to ask one place and get a sourced, current answer.

The finish line

what you keep

Done looks like this.

No. 01

A working agent in production, scoped to a job it can do well

No. 02

A documented map of the agent’s permissions, sources, and escalation paths

No. 03

An audit trail that shows what the agent did and why

No. 04

A team trained to supervise, correct, and extend it

Proof

measured, not claimed

Proof from this line of work.

A real AI Agents & Assistants engagement, led by the number that moved. We publish nothing we didn’t measure.

$2.5M

Revenue recovered

Telecom

A Backlog Agent Recovered $2.5M in Dormant Revenue

A telecom company had years of past projects sitting dormant: real revenue nobody had time to chase. We built a backlog-builder agent to work the list and reopen the right conversations. It recovered $2.5 million in dormant revenue.

Job ticket

specifics, in writing

The job, on one plate.

Job ticket

Format
Scoped project: discovery, build, supervised pilot, handover
Timeline
Set at scoping. First agents ship narrow on purpose.
Delivery
Remote build with on-site working sessions as needed
Where it runs
Your stack, your accounts. You own everything we build.
Prerequisites
None. We scope around the systems you already have.
From you
A subject-matter owner, sample requests, and source documents

The build

guardrails included

How an agent earns its desk.

  1. The support agent

    Answers the questions your help center already answers, drafts replies for the trickier ones, and routes anything involving an exception, a refund, or a frustrated human straight to your team. First response gets faster; your people keep the judgment calls.

  2. The ops agent

    Handles the internal routine: access requests, status checks, form filling, routing to the right owner. The moment a request needs approval or judgment, it stops and asks a person.

  3. The knowledge agent

    Answers from your documentation, policies, and past decisions, and cites the source every time. When sources conflict or run out, it says so and points to the owner instead of guessing.

  4. Guardrails and governance

    Every agent ships with scoped access, a defined human handoff, and an audit trail of what it did and why. Guardrails come before autonomy: wider scope is earned in the logs, not promised in the pitch.

Packages

no mystery pricing

Pick the shape. We fill in the numbers.

No. 01

First agent

One agent, one job, scoped narrow enough to ship, pilot, and judge honestly.

No. 02

Agent plus workflow pair

An agent and the automation that feeds it, built as one system with one handover.

No. 03

Agent program

A sequence of agents across teams, shipped under shared guardrails, shared governance, and one roadmap.

Numbers on the first call. No discovery-call maze.

After we ship

we answer the phone

We stay until it sticks.

Shipping is the midpoint, not the finish line. Every agent leaves with documentation, an escalation runbook, and a supervised period where we watch the logs with you and tune what we find. After that comes async support and a direct line to the person who built it, not a ticket queue.

Most agent projects die of ambition. Too much autonomy on day one, pointed at a job nobody wrote down, judged by a demo instead of a log. We build in the opposite order: one job with a known source of truth, the narrowest access that lets the agent do it, and real requests with a person checking the work until the log says it’s ready for more.

An agent that actually knows your business is a grounding problem before it is a model problem. So the build starts in your help center, your policies, your past tickets, and the folder nobody admits is the real source of truth. We do that unglamorous work first, wire the agent to cite where every answer came from, and teach it the most useful sentence in the building: that one isn’t mine, here’s the person who owns it.

Guardrails are not the boring part of the project. They are the project. Scoped permissions your security team reviews before anything touches production, a named human on the receiving end of every handoff, and an audit trail a person can read without a decoder ring. When the logs show the agent behaving, we widen its scope one notch at a time. That’s how you end up with an agent your team trusts instead of one they quietly route around.

And sometimes the honest recommendation is no agent at all. A surprising number of agent requests are really workflows with a fancier name: deterministic, cheaper, finished sooner. We’ll tell you which one you’re holding on the first call, because the goal was never to sell you an agent. It’s to hand your team a tool they still use after we leave.

FAQ

asked at the counter

Asked often. Answered straight.

What is an AI agent, and how is it different from a chatbot?

A chatbot follows a script. An agent can read your documentation, take defined actions in your systems, and recognize when a request is beyond it. We treat that last skill as the most important one.

What should our first agent be?

One job with clear inputs, a known source of truth, and an obvious handoff. First-response support, internal IT and ops questions, and policy lookup are common starting points. If a simpler workflow would do the job, we’ll say so and build that instead.

What happens when the agent doesn’t know the answer?

It admits it, then hands the request to a named person with the context attached, so nobody starts over. An agent that guesses is a liability. Refusing gracefully is a feature we build on purpose.

Will an agent replace our support team?

No, and we won’t build toward that. The agent takes the repetitive volume so your people spend their day on conversations that need judgment and care. Zero people replaced is the stance we’re proudest of.

Can the agent connect to our internal systems and data?

Yes, with scoped access: the narrowest permissions that let it do its one job and nothing else. Your security team reviews every scope before production, and every action the agent takes lands in the audit trail.

How do you keep an AI agent from making things up?

Grounding in your sources, required citations, a tight scope, and testing against real requests before it meets a real user. Nothing makes the risk zero, so we also design for the miss: low-confidence handoffs and an audit trail that surfaces a bad answer fast.

Who maintains the agent after you leave?

Your team, on purpose. You get the documentation, the full configuration, and training for the people who supervise it, plus a support window and a person to call after that. We don’t build black boxes you have to rent forever.

What does an AI agent build cost?

Project-based for a scoped build, retainer if we’re running an agent program together. You’ll hear numbers on the first call, once we’ve seen the job. Scope drives everything, which is why we don’t publish a rate card.

Your team is smarter than the software. We just prove it.

Ready when your team is.

A 30-minute call. Real questions, real answers, zero pressure.