Services AI agent deployment
Take one workflow off your team and get those hours back.
We take one repetitive workflow off your team and run it as a governed agent, tested against your own past cases before it goes live.
25+ years in enterprise IT, including Fortune 500 regulated-data environments.
No preparation needed. You leave with next steps in writing.
Assess, build, embed
- 25+ yrs enterprise IT
- Fortune 500 regulated-data environments
- Founder-led
Take one repetitive, error-prone workflow off your team and get those hours back, with an agent that has to clear an agreed accuracy threshold on your own past cases before it touches live work.
The problem
The cheap part is the agent. The expensive part is the cleanup.
Installing a capable agent takes an afternoon, and most ship with broad access to your shell, files, and systems the moment they run. That is fine on a personal laptop. In a regulated business it is an exposure with a price attached, and the price is paid by the people who have to work out afterwards what it touched. The agent is the easy part. Deploying it so you can supervise it, prove it works, and turn it off cleanly is the work.
What we do
Hours back, without inheriting the exposure.
- The hours come off one specific workflow, because the agent is built for that job rather than being a generic assistant pointed at your business and hoped for.
- You know the error rate before it can cost you anything: the agent is measured against your own past cases, with an accuracy and exception-rate threshold you agree to before go-live.
- The blast radius stays small. Each agent gets only the access its job requires, with credentials brokered rather than embedded.
- A bad release costs a rollback rather than an incident, because everything is versioned and reversible inside your own environment.
In practice
Three shapes this work takes, and how we prove it.
- Document-intake agent
- Takes the keying and filing off a person entirely. It reads incoming files, extracts the fields your process needs, and files them, flagging anything low-confidence for a human instead of guessing.
- Reconciliation agent
- Replaces the hours somebody spends eyeballing two systems line by line. It compares the records and surfaces only the exceptions, so the time goes to the handful of cases that actually differ.
- Support-triage agent
- Shortens the queue today without lengthening it next quarter. It drafts responses and routes cases, with a human approving anything that leaves the building.
- How we prove it is good enough
- You find out what it costs you in errors before it can cost you anything in production. A labeled evaluation set built from your own past cases, an agreed accuracy and exception-rate threshold, and a documented run against that set before anything touches live work. Confabulation, which is the term NIST uses in AI 600-1 for a model stating something false with confidence, is a measurable rate on a labeled set rather than a thing you hope about. If the agent does not clear the threshold, it does not go live. That is the entire gate, and it is the reason this engagement can be argued about with numbers rather than adjectives.
What you get
How each agent is controlled and proven.
- A scoped identity per agent, with least-privilege access to only the systems its job needs and credentials brokered, never embedded, so the exposure stays proportional to the work
- An evaluation set built from your real past cases, with a documented acceptance threshold the agent must clear before go-live, so the accuracy conversation happens in numbers
- Human approval on risk-flagged actions, version control with rollback, and monitoring of volume, exceptions, and error rates, so a drift in quality shows up as a number rather than as a complaint
How we deliver it
Capability without the exposure.
You get the capacity back without inheriting the risk, because every agent ships with permissioning, audit logging, and approval gates around it from the first commit. Where an agent writes into a system of record, the CRM or case system a regulator or examiner will later read, it writes under its own scoped identity, so its actions land in that system's native audit history instead of disappearing under a shared service account and costing somebody a reconstruction later.
How a governed agent runs
Where this shows up
See the work in context.
Is this you
- Who this is for
- Best for teams with a defined, repetitive workflow, hours going into it every week, and a real cost of error.
- Who it is not for
- Not for open-ended experimentation with no owner and no way to measure success, and not the place to start if AI is already running across several teams with nobody accountable for it. That is the AI Operating System.
Questions
Questions leaders ask.
- How do you keep an agent from doing something it should not?
- The cheapest control is the one that limits reach, so each agent gets least-privilege access to only the systems its job needs. Above that sit approval gates, which route risk-flagged actions to a named person before they run.
- How do we know an agent is good enough to go live?
- We measure it against a labeled set of your real past cases and only promote it once it clears an accuracy and exception-rate threshold you agree to. The threshold is set before the build, not negotiated afterwards, which is what keeps the decision to go live from turning into an argument nobody can settle.
- What happens when an agent gets something wrong?
- A bad version costs a rollback rather than an incident. Agents are versioned and monitored, so one can be rolled back or disabled without disturbing the rest of the system, and the exceptions land in the record rather than in somebody's inbox.
- We already have agents running. Can you govern those instead of building new ones?
- Yes, and that is usually the governance and compliance engagement rather than this one. This page is about building an agent for a workflow you have not automated yet. If the agents already exist and the real question is whether you can prove they are controlled, start there.
If this is on your plate, let's talk.
A twenty minute intro call is the simplest next step: we work out which step fits, and you leave with one specific thing to act on. If you want the senior read on your business rather than a routing conversation, that is the AI Leverage Briefing.
Within one business dayYour scope is settled, in writing.
What you walk away with Prioritized 90-Day Roadmap · Risk Register · Governance and Compliance Gap Assessment · Safe-to-Deploy Read
25+ years in enterprise IT, including Fortune 500 regulated-data environments.
If AI is not the right tool for your problem, you will hear that from us.