Two weeks, fixed price, agent made shippable
Your agent works in the demo and nobody can say whether it is safe to ship
Two weeks, fixed scope, fixed price. I instrument your agent, build the eval suite, calibrate the escalation policy, and leave you the dashboards and a number you can defend.
The problem
The gap between a working demo and a shippable agent is statistical, and nobody on the team owns it
The agent works. Whether it works well enough, how you would know if it stopped, and what error rate your escalation threshold actually buys are four different questions, and answering them needs a statistician rather than another engineer. Most teams do not have one, cannot justify hiring one, and ship anyway.
The insight
A score that averages away a critical failure is a score that gets you shipped and then paged
Four dimensions, blended so the weakest one dominates rather than being offset by the strongest. An agent with excellent drift detection and an uncalibrated escalation threshold is not a seven out of ten; it is the threshold, and the threshold is the thing that will produce the incident. The scorecard is deliberately hard to feel good about.
Composite over four measured dimensions — FDR-corrected drift detection, conformal escalation calibration, paired-design eval power, and suite determinism — blended half on the mean and half on the minimum so a critical gap cannot be averaged away.
How it works
Four steps, no data science team
Instrument the agent, score all four dimensions, deliver the scorecard. You see the number before any work is committed.
You pick which gaps to close. Usually the weakest and whichever your buyer asks about.
Eval suite built, escalation policy calibrated, drift detection wired to Slack.
Dashboards, the runbook, and the re-score. Everything runs without me afterwards.
Who it is for
The founder or engineering lead who has to say it is ready
Teams with an agent in or near production and a launch they are nervous about. Usually between five and forty engineers, no statistician, and a customer or a board asking a question they cannot answer.
Pricing
- –Four-dimension score
- –Written findings
- –No commitment
- –Instrumentation
- –Eval suite built
- –Escalation policy calibrated
- –Dashboards and runbook
- –Re-score at handover
- –Monthly re-score
- –Drift monitoring
- –Quarterly eval refresh
- –Direct access
Competition
What exists, and what it does not do
| Who | What they do | The gap |
|---|---|---|
| Hiring an ML engineer | The obvious alternative. | Three months to hire, a salary, and most candidates are model builders rather than statisticians. This is two weeks and a fixed number. |
| Observability vendors | Sell you a platform and a dashboard. | They sell tooling. Nobody configures it, nobody interprets it, and the escalation threshold is still a guess. This ends with the interpretation. |
| General AI consultancies | Open-ended engagements to help you build. | Priced hourly, scoped loosely, staffed by generalists. Fixed scope and a published method is the differentiation. |
| Shipping and finding out | What most teams do. | Free until the incident, at which point the cost is the customer. |
It is a service, so it does not scale and it consumes the founder entirely — three concurrent engagements is the ceiling and there is no fourth. It also caps out early: the same two weeks cannot be sold twice to the same customer, so growth depends on constant new logos. The reason to do it anyway is that it pays in week two rather than month twelve, and every engagement is customer discovery somebody else funds. The failure mode is comfort — a founder who does three of these and stops building has become a consultant by accident.
Market
Priced against the cost of an incident, or of the hire that will not happen for a quarter
Twenty engagements a year at $14,000 is $280k, which is not a company and is enough to fund one. Its real output is the product specification for Abstain, written from three customers rather than from a guess.