AI in production —
not on a slide.
We build judgement-class automation for the work that actually moves the business. No demo theatre. No autonomy where it doesn't belong. Always behind a confidence threshold a human signed off on.
Stop benchmarking
models.
Start benchmarking
decisions.
Most AI projects fail not because the model was wrong, but because it was plugged into the wrong decision. The right test isn't accuracy — it's whether the cost-of-being-wrong is acceptable against the time-to-being-right.
Some decisions tolerate a 5% error rate at 200ms. Some decisions don't tolerate any error rate at all. The work isn't picking the model — it's mapping the decision before the model ever runs.
- Classifiers with confidence thresholds
- RAG over your own structured + unstructured data
- Extraction from messy documents at scale
- Drafting that a human reviews before sending
- Autonomous agents acting on irreversible decisions
- Chatbots replacing humans on regulated work
- "AI features" added for the press release
- Pipelines without an offline evaluation harness
Where AI belongs —
and where it doesn't.
Plot the decision on two axes: cost-of-being-wrong and volume of decisions per day. The diagonal tells you the answer.
Pure human judgement
Hiring decisions, M&A, legal exposure calls, medical diagnosis without specialist oversight.
AI-assisted human
Underwriting, fraud review, claims triage, medical pre-screening, content moderation appeals.
Probably not worth it
Occasional admin tasks, one-off internal queries, tasks done less than weekly.
The sweet spot
Ticket classification, document extraction, lead enrichment, draft generation, smart routing.
↪ The first thing we do in any AI engagement is plot your candidate decisions on this grid. Roughly one in three lands in Q4.
Six AI patterns,
shipped to production.
Routing & triage
Inbound items — tickets, emails, leads, claims — sorted into the right queue with confidence. Below threshold drops to a human in seconds.
Document extraction
Invoices, contracts, ID, KYC — pulled into structured fields, validated, and pushed into your systems. Failed extractions queue for review.
Grounded answer systems
RAG over your own knowledge — runbooks, policies, contracts, product data. Citations mandatory. No citation, no answer.
First-draft generation
Replies, reports, summaries, RFP responses — drafted in your voice, sitting in a human's queue for one click of approval, one of edit.
Predictive scoring
Churn, demand, default, lead conversion — ranked probabilities that drive ops. Often the right answer is gradient boosting, not a transformer.
Anomaly & drift detection
The system notices when something stops looking like itself — fraud patterns, ops drift, data-quality decay — before any dashboard surfaces it.
The right model
is rarely the
biggest one.
↪ The architecture is portable: every model is behind an adapter, swappable in a day. Your business logic doesn't care which one ran.
The boring scaffolding
that makes AI
production-grade.
Confidence thresholds
Every model output carries a confidence score. Below threshold, it lands in a human queue. Calibrated per decision, not per model.
Offline eval harness
A frozen test set, scored on every change. No model goes to production without beating the previous one on metrics you signed off on.
Shadow mode
Models run silently against real traffic before they ever act. Predictions logged, compared to humans, reviewed weekly until trusted.
Drift monitoring
Models decay quietly when the world changes. We monitor input distribution and output calibration — alerts fire before performance does.
Audit trail
Every prediction recorded with input, model version, confidence, and the eventual outcome. Replayable on demand, defensible to a regulator.
Kill switch
Any model can be disabled in seconds, falling back to the pre-AI flow. Tested quarterly. Hoped never used. Always there.
Two weeks to know
if AI is the answer.
Before any model gets built, we run a structured readiness audit. Output: a written brief that names the right candidates — and the wrong ones — by name.
Inventory the decisions
Every recurring decision in your operation, plotted on the spectrum. The honest list, not the demo list.
Audit the data
Ground-truth labels, distribution, drift, edge cases. Most projects fail here, before any model is touched.
Run a baseline
A small frontier model, off the shelf, prompted carefully — measured against your data. Sets the floor before any custom work.
Score & rank
Each candidate decision weighted on volume, savings, risk, and feasibility. You leave with a numbered roadmap.
The written brief
A 14–20 page document with named candidates, baseline numbers, and a build plan. Yours to keep, regardless of what comes next.
FIXED FEE —Roughly 30% of audits end with us recommending against AI for the candidate workload. We're fine with that.
Three ways
to start.
Readiness Audit
Two weeks to know what's worth building, what isn't, and where to start. Written brief at handover.
- Decision spectrum mapped
- Off-the-shelf baseline measured
- Ranked roadmap, written brief
Build
End-to-end build of one or more AI capabilities into your operation. Shadow mode → threshold → live.
- Eval harness on day one
- All six guardrails baked in
- Knowledge transfer to your team
Steward
We own the lifecycle of your AI fleet — drift, retraining, evals, threshold tuning — until your team takes the keys.
- Drift & calibration monitoring
- Quarterly model refresh
- Hand-off plan from day one
↪ Indicative. Every engagement is scoped from a written brief — no per-model surprises, no usage roulette.
Support intake
went from 11min
to 22 seconds —
and accuracy went up.
"The model is right more often than our best agent — and we know exactly when it isn't."
The questions
we hear on
every first call.
Mostly variations of "is this safe", "will this hallucinate", and "what about our data". Fair questions.
Q · 01
"What about hallucinations?"
+
"What about hallucinations?"
Q · 02
"Where does our data go?"
+
"Where does our data go?"
Q · 03
"What if the model gets worse over time?"
+
"What if the model gets worse over time?"
Q · 04
"Are AI agents the future of automation?"
+
"Are AI agents the future of automation?"
Q · 05
"Will this lock us into one model vendor?"
+
"Will this lock us into one model vendor?"
Where in your
operation does
judgement scale
worse than volume?
That's where AI earns its place. Bring it to a 30-minute call — we'll tell you, honestly, whether it's worth building and what it would take.