Free · 3-5 days · No obligation
AI Opportunity Audit
We map your operations, find the highest-ROI automations, and hand you a ranked plan with payback math. Yours to keep, whoever builds it.
No obligation. No follow-up sequence.
AI quality lab
Proof the system works before you trust it, and every week after
A library of golden runs captures what good looks like on your own work. New versions replay against it before they ship, and a person hears the week quality slips. 5x ROI in 30 days. Or we work for free.
Teams we build for
- Hoyes Michalos
- Nurse Next Door
- Fedi
- UBC Sauder
- Merchant House Capital
- Picton Investments
- Campbell Froh May & Rice LLP
- Barnakl
- Hungerford
- Breez
Teams we build for
- Hoyes Michalos
- Nurse Next Door
- Fedi
- UBC Sauder
- Merchant House Capital
- Picton Investments
- Campbell Froh May & Rice LLP
- Barnakl
- Hungerford
- Breez
Teams we build for
- Hoyes Michalos
- Nurse Next Door
- Fedi
- UBC Sauder
- Merchant House Capital
- Picton Investments
- Campbell Froh May & Rice LLP
- Barnakl
- Hungerford
- Breez
The problems this solves
01
The demo was great and nobody has measured since.
The system went live on a strong impression. Whether it is still good today is a feeling, not a fact anyone can show you.
02
A prompt change helps one case and breaks another.
Someone improves the wording and the fix works on the example in front of them. The case it quietly broke surfaces later, in front of a client.
03
The vendor grades its own homework.
The team that built the system is the team telling you it works. Nobody independent has scored a single output.
04
Quality slides too slowly to notice.
Each week the output is a little worse than the last. No single day looks like a failure, so the decline is discovered a quarter late.
What we build
01 · Golden runs
We capture a library of golden runs from your own work: real inputs paired with output your people have signed off on. That library is the standard, not a public benchmark.
02 · Replay gate
Before a new version ships, it replays against the library. A version that scores below the one it replaces does not go out.
03 · Scoring
Two independent judges score every output, separately. When they disagree, the disagreement is shown to a person instead of averaged away.
04 · Experiments
A prompt change runs as an experiment scored on the golden set. The winner is promoted on evidence, and the losing version is kept so the decision can be revisited.
05 · Drift watch
After launch, scores run continuously. A person hears about a slide while it is a week old, not a quarter.
What's included
- A golden run library built from work your team already approved
- Replay wired in so every new version is scored before it ships
- Two independent judges on every scored output, disagreements routed to a person
- Drift watch after launch, tuned by your team without us. Cancel anytime
Proof
Where this lands hardest
AI for financial services→
Output your compliance team can defend, scored against work they approved.
AI for manufacturers→
Extraction and inspection results checked against runs your engineers marked correct.
AI for professional services firms→
Drafts held to the standard of work a partner already marked correct.
Weeks, not quarters.
How the full engagement works01 Discovery
WEEK 0
02 Prototype
WEEKS 1-3
03 Deploy & train
WEEKS 4-8
04 Run & improve
WEEK 9+
How we build it
Step 01
Capture the standard before changing anything.
The first build is the golden run library, drawn from your own work and signed off by your people. A suite built on someone else's benchmark measures someone else's system.
Step 02
Gate every change on a replay.
No version ships without replaying against the library. A score that drops blocks the release, and the failing cases go to a person with the evidence attached.
Step 03
Watch for drift after the applause.
Quality rarely fails loudly. It fades. Scores run continuously after launch, and a regression flags a person the week it appears instead of waiting for a complaint.
What we will not automate
Publishing a number we did not measure. A score reaches your scorecard only after the suite has run on your data, and until then the cell stays blank. A blank you can trust beats a figure you cannot.
What that means in practice
Evaluation and observability
How we prove the AI actually works: measured, monitored and regression-tested like real software, not vibes.
Where we stop. There is no engagement where we skip this. The honest variable is depth: a document pipeline gets faithfulness and extraction suites, an outbound agent gets human review sampling, a classifier gets a held-out test set. We size the harness to the risk, never to zero.
Fine-tuning and customization
Teaching an existing model your domain's style, vocabulary or task. Far cheaper than building one from scratch.
Where we stop. Fine-tuning is a cost, latency and consistency optimization, not a knowledge store. If you want the model to know your documents, that is retrieval, not training, and we will tell you which one you actually need before you pay for either.
MLOps and infrastructure
The plumbing that keeps AI running in production: deployment, scaling, monitoring and retraining, so it does not quietly degrade after launch.
Where we stop. We do not park your system on infrastructure only we understand. If your platform team runs Kubernetes on AWS, we deploy there. The architecture has to survive us leaving; that is the point of it.
Questions? Straight answers.
Also built here
Three ways in. No long discovery.
Paid · Fixed scope
First Build
One high-ROI system, built on your real data and deployed in your stack, with your team trained to run it. Fixed scope. Quoted after the audit. Covered by the 5x ROI guarantee.
Scope my first build
Paid up front. Cancel anytime after.
Free · 15 min
Intro Call
Fifteen minutes with James, not a sales rep. Bring your worst bottleneck, leave with a straight answer.
Book 15 minutes
No pitch deck.
Every first build is covered: 5x ROI in 30 days. Or we work for free. Read the full terms
Start free. Know your number in five days.
A 3 to 5 day audit of your operations, ending in a plan with the ROI math attached. No obligation.