Where expert time is the bottleneck,
we put AI into production.
For teams where expert time is the bottleneck, we put AI into production, instrumented with evaluations, cost meters, and monitoring. You keep the machine.
NDA before any data. Your data never trains anyone's models. Fixed fee, scoped to the workflow, never an hourly meter. You own every artifact we build.
Using an AI tool is not the same as operationalizing AI.
A good prompt is a demo. A production system is a business asset, it has an accuracy number, a cost per run, a monitor that pages when it drifts, and a runbook your team owns. We build the second one, on one high-value workflow at a time. We speak two dialects.
For Private Equity
Document diligence at machine speed, extraction, cross-reference, and clause-cited red-flagging that feeds your data platform. Every finding cites the page. The analyst stays the author.
Enter the PE room →For Operations
Turn expensive, repeatable work into instrumented systems. The audit no human has time to run daily, the review that never sleeps, the agent that catches waste before it leaks.
Enter the ops floor →Embedded, instrumented, handed back.
A sprint, not a slide deck. We start free, prove the system with numbers, and leave you something you own and can run without us.
Understand
We sit with the people who do the work, map the real process, and pick the one workflow where AI creates the most leverage the fastest. No boil-the-ocean roadmap.
Instrument
We build the evaluation set that grades quality against your own experts, meter the cost per run, and pin the model versions, so quality is a number and the unit economics are known before anything scales.
Ship
The system goes to production with monitoring and autonomous agents that watch for drift, silent failure, and runaway cost, and page a human the moment something moves.
Hand off
You get the runbook, the data contract, the eval harness, and the audit log. If the scorecard ever says you don't need us, that's a cheap answer to have bought.
Not a consultancy. Not a staffing shop.
Our model, cashed out plainly: fixed price, instrumented delivery, artifacts you keep, the opposite of a leverage pyramid billing hours.
- , Junior consultants learning on your time, billed hourly
- , A deck of recommendations you now have to implement
- , "It's accurate", with no number behind it
- , A dependency that deepens every quarter
- + Named accountability on every engagement
- + A running system, a fixed fee, a date
- + A scorecard graded against your analyst baseline
- + A machine you own and can run without us
The eval harness is a deliverable, not a claim.
Every engagement ships with a graded evaluation set: ground truth built with your experts, field-level scoring against their baseline, an acceptance bar set with you before anything counts as done, and a policy that a miss ships as a finding, never hidden. You keep the harness even if you never hire us again.
Systems in production, not pilots in a drawer.
A 32-account agency, running an AI audit every account can't staff by hand.
Multi-pass analysis engine
Reads hundreds of thousands of search terms and returns concrete actions across every account, the work no human has time to do daily.
"Larry," the monitoring agent
An autonomous agent that runs the audit daily, catches waste and anomalies, and reports what needs attention before spend leaks.
Who you're working with.
Mauricio Degregori
First technical hire at Community Labs, a COVID-19 testing startup that scaled from zero to a $100M revenue run rate in about six months, where he became Director of Technology. Co-founded a services business that still runs profitably, then founded AdLlama (PPC workflow software; product and IP acquired by an agency partner). Started Novan Labs, where he leads projects and go-to-market.
Nick Halverson
CTO at Jill's Office (24/7 answering for the trades) and ResponsiBid (automated quoting for home-service companies), then co-founder of HireBus (the home-services hiring platform), where he rebuilt the platform from scratch, AI-native, replacing an expensive team with agents. Wrote production systems by hand long before models did. At Novan he builds and operates the machines: pipelines, eval harnesses, cost instrumentation, and an agentic fleet now going into production at a home-improvement company.
Honest by default, before you send a single document.
An NDA precedes any data. Your documents never train anyone's models, and are deleted with attestation on request.
We feed your platform, we never try to replace it. The analyst stays the author; AI stays inside a reviewed envelope.
Fixed fee scoped to the workflow, cost metered per run. If a workflow isn't worth automating, we tell you.
You keep the eval harness, the runbook, and the code. No lock-in, no leverage pyramid, no black box.
Straight answers, before you ask.
We already use Claude and ChatGPT internally. What do you add?
We turn that usage into operational systems: evaluated against real cases, consistent in output, instrumented for cost, and wired into how the business actually works. A good prompt is a demo. We build the layer a team can depend on.
How do engagements start?
Usually with an NDA and a workflow walkthrough. We map one high-value workflow, build an evaluation set, and model what it costs to run before anything gets built. You see how we work on something real and low-risk first.
Do you replace our data team?
No. We work alongside it. We turn experiments and one-off usage into durable, evaluated workflows, and hand off systems your team can own and extend.
How do you handle security and governance?
Systems run inside your existing tools, permissions, and audit logs, so your data stays in your environment. Every recommendation is logged with its reasoning, and a person approves anything that touches a live system. We're not yet SOC 2 certified, but we'll complete a security questionnaire on request and scope a DPA before any data is touched.
How does pricing work?
Pricing is set after discovery, once we've modeled what the workflow actually costs to run. If the numbers don't justify it, we'll tell you.
What if a workflow isn't worth automating?
Then we say so, and we don't build it. We would rather lose the project than ship a system that costs more than the work it saves.
How long does it take to deploy?
It depends on the workflow and the systems involved. After the walkthrough, once we understand the scope, we give you a realistic timeline before any work starts.
Can we talk to a reference, or see more work?
We take on a small number of engagements at a time, on purpose, so the public case list is short. References are available on request, and in the first conversation we model our approach on your actual workflow so you see the fit before committing.
Tell us the workflow. We'll tell you honestly if it's a fit.
Describe the expensive, repeatable work you'd hand to a system if you could. You'll get a straight read on whether an AI system pays off, and what a first sprint would take.