Find the problem that sounds like yours.
Each card is a real engagement, stated as the problem — filter by the platform or service that runs it, then open the one that fits.
16 of 16 use cases
Stop hallucinations before customers find them
“Your AI assistant answers confidently and wrongly — and you only hear about it from customer complaints, not from your test suite.”
Walk into a regulatory AI audit already prepared
“The regulator asks how your AI complies — and the evidence lives in scattered docs, tribal knowledge, and a 60-day scramble.”
Cut regression cycles from days to hours
“Your release cadence is set by how long regression takes — and regression takes a week because humans maintain brittle scripts.”
Prove your credit models lend fairly
“Your credit decisioning AI may be biased — and you won’t know until a regulator, a journalist, or a lawsuit tells you.”
Validate claims automation before it denies the wrong claim
“Automated claims decisions are fast — until one wrong denial becomes a regulatory complaint and a headline.”
Ship clinical AI that clinicians can trust
“Clinical decision support that is 95% accurate is also 5% dangerous — and nobody can tell you which 5%.”
Survive your biggest sale day without a war room
“Your platform works fine at 2× load and falls over at 9× — which is exactly what your next sale event will deliver.”
Know your release is ready before you ship it
“Go/no-go calls run on gut feel and a spreadsheet — and the defects that escape are always in the requirements nobody traced.”
Catch model drift before your users do
“The model you evaluated in January is not the model serving traffic in June — and nothing in your stack will tell you.”
Test with production-grade data you are allowed to use
“Your best test data is production data — and using it is a privacy violation waiting for an incident number.”
Deploy a voice or chat agent that stays on script
“Customer-facing agents improvise — discounts they can’t offer, advice they can’t give, promises you can’t keep.”
Benchmark AI against your human baseline before cutting over
“You are replacing human workflows with AI on faith — without parity evidence, the first quality dip becomes an executive escalation.”
Stand up an AI-ready capability center in 90 days
“You need an AI engineering capability yesterday — and the usual GCC build takes 18 months of hiring before anything ships.”
Run AI inside your perimeter — fully air-gapped
“Your regulator, your board, or your country says the data cannot leave — and every AI vendor you talk to is cloud-only.”
Make RLHF gains stick in production
“Your alignment gains decay within a quarter — annotator noise, reward hacking, and distribution shift eat them quietly.”
Test ADAS and embedded AI to functional-safety standards
“Your embedded AI has to satisfy ISO 26262 and SOTIF — and your AI team has never shipped under functional safety.”
You bring the problem. We bring the method.
Every use case above is a problem we have solved in production — stated the way you’d say it, with the approach and the numbers behind it. We bring the method; you bring the problem.
Read it as a menu of problems, not a catalogue of products. Open the card that sounds like yours; the platform and service behind it are what we bring to solve it.