AI fails on context, not intelligence.
We work on one question: can an answer about your business be trusted? Grounded in your own records, run on your premises, and judged against questions your team writes.
The problems we work on
Each one decides whether an answer can be trusted. Where the work is already in use, the page that shows it is one press away.
1. Grounding an answer
An answer is only as good as what it read.
- 1.1In useKeeping answers currentRecords change every day. An answer is only right if it read the latest version.TodaySources are read again on a schedule, and every answer rests on what was read last.Where the reading runs
- 1.2In useCiting the exact sourceA citation helps only if it points at the line that backs the claim.TodayEvery answer names the document and the line it stands on.See a traced answer
- 1.3In useReading only what the asker may seePermissions have to apply before the model reads anything.TodayAn access guard sits in front of every search, so withheld records never reach the model.The access guard
2. Judging an answer
Fluent is not the same as right.
- 2.1In useKnowing when it does not knowA confident wrong answer costs more than no answer.TodayWhen it is not sure, it says so and shows what it found.A miss, worked
- 2.2StudyingGrading grounding, not fluencyAn answer can read well and still rest on nothing.TodayEvery answer is graded by a person against the sources it cited. We are studying how to grade faster without losing that check.How grading works
3. Acting on an answer
Doing the work is where a mistake costs most.
How we evaluate
Every deployment is judged the same way, question by question, before it goes live and on every change after.
The go-live test in your contract- Pass
- Miss
- Said it was not sure
- In the regression gate
Anatomy of a miss
A right answer proves little. Here is a wrong one, followed from the question to its cause, the fix, and the run that passed.
Someone in finance asks
In the chat they already use. The answer lives in a signed agreement and one of its schedules.
What interest do we charge on late payments under the 2024 distribution agreement?
Field notes
What we have written up so far, each note with the sources it stands on.
- What is the AI context layer?Models keep getting smarter. The context they run on does not. A field guide to the AI context layer, what it is, why enterprise AI fails without it, and what it is actually made of.
- Why every enterprise AI pilot failsThe failure is not in the model. It is in the missing context layer that every enterprise AI deployment needs in order to succeed in production.
Every answer shows its proof.
Thirty minutes with the live system. Bring a question your team cannot answer today.