Shift the unit of management from 'a model' to 'a single answer'.
The scoring criteria, the scoring location, and every asset stay with the customer.
Finding a company document, handing it over, and wiring it into work screens and equipment — that's what most call 'AI adoption'.
LLMOps starts right after that.
You only know once you've measured against the truth.
Score against an answer key — numbers, not gut feel
You can only trace it when the evidence remains.
Cite which document, which page
You must be able to recall answers already delivered.
When the source changes, single out those answers
Other tools ask "is this answer right now?" We ask "is yesterday's answer still right today?"
Every answer is bound to its instructions, its knowledge and edition, the ingestion method, retrieval, and model. When something changes, only the affected answers surface.
Answer from customer documents and mark which document and which page. If no evidence is found, respond with "no supporting evidence found."
Not a public benchmark — customer documents. The scoring criteria and the scoring location are inside the customer's server.
Deployment happens only when there is documented evidence of passing the criteria, not just when it "looks good." No one can skip in a hurry.
Tracing waits for you to ask "why." We do the opposite: the moment the source changes, we identify the affected answers first.
Measured against customer documents (not public benchmarks), scored inside the customer's server, and the criteria stay put even when the model changes.
Quality practices trace impact whenever a change occurs — for machines and raw materials. Only documents sit outside that discipline.
You trace the lots produced by it
You already do thisYou find the products it went into
You already do thisYou should → find the answers grounded on it
This part is missingNo one has actually counted the answers that were grounded on v4.
There is no way to find the people who acted on those answers.
Revision notices go out, but they don't reach the answers that were already delivered.
When guidance is revised, the past answers that relied on it get left dangling. Our implementation starts identifying what is shaken the moment the source changes — because the records make it possible.
Locate past answers grounded on the knowledge that changed.
LiveNotify the owner: "the source behind this answer has changed." (Includes finding recipients.)
Next stepPrevent the same stale answer from being generated again.
Next stepWhichever of these six changes, we single out only the affected answers.
Shipping AI to the field without validation — we block it before (pre), during (risk), after (post), and in records, following the deployment flow itself.
Run against the answer key inside the customer's server. Data doesn't leave, and quality and server capacity are measured together.
Deployment happens only when there is documented evidence of passing the criteria, not when it merely "looks good" → no one can skip in a hurry.
Continuously measure the rate of missing evidence, retrieval score drops, citation gaps, and field error reports, and notify owners the moment thresholds are breached → so the felt experience doesn't quietly shift.
A queue is a signal → measure time-to-answer and queue length together to determine when to scale server capacity.
Within what we have examined, source-tracing tools stop at "tracing" answers → we have not found capability that actually flips them back.
We surface the affected answers first, without being asked. Finding recipients and notifying them is the next step.
Every action should be recorded somewhere — there is a common spec across the five products we reviewed.
Record which deployment went out, by whom, when, and why, in a form that cannot be silently erased. When audits or certifications ask "why was this version shipped," we can present the evidence exactly as it was.
Those gaps are recall → within our review, there was no capability that actually flipped delivered answers back. Tracing only answers "why did you answer that way" when asked. We identify the affected answers the moment the source changes, without being asked.
Automatic rejection of below-bar models by code
LiveFlag past answers whose source has changed
Next stepCapture who, when, and why — in a correctable form
Design stage
