You never start from an empty page.
The knowledge your organization already holds is embedded from day one. So the moment a new incident appears, the relevant prior experience is already assembling around it — value from your first analysis, not in six months.
One language across every plant
Your sites call the same failure five different things, and the engineer who left spelled it a sixth way. The engine reconciles them into one concept, so searching any of them finds all of them — and nobody has to maintain a taxonomy by hand.
Your proven experience outranks everything else
What your engineers reviewed and approved carries more weight than anything imported, generic or external. Relevance is not a popularity contest: your own resolved cases come first, and where each one came from travels with it.
The loop is measured, not assumed
Each time a solution is applied, the engine records whether that failure came back. A fix proven to have held reads differently from one nobody has tested yet — and thin evidence stays visibly thin instead of being rounded up.
Most knowledge bases stop when the document is filed. This one starts there.
Storing what you did is the easy half. The half that compounds is finding out whether it worked — and letting that answer change what the next engineer is shown.
Your team puts a solution to work, and it stays tied to the case it came from. Not a note in a document — a decision the engine can follow up on.
From then on the engine watches whether that same failure comes back on that equipment. Silence is a result too, and it is recorded as one.
A fix that held for a year and one that failed again next month stop reading the same. Evidence decides which is which — not whoever wrote it down.
So the next engineer with that failure gets a different, better answer than the last one did. The knowledge moved because the plant did.
One canonical body of knowledge — not five disconnected tools.
Documents, operational systems, your knowledge base, and your own resolved analyses are unified into a single structure your whole organization draws from. So trust compounds in one place instead of fragmenting across silos.
- Problem → Cause → Solution, expressed consistently across every siteNo more reconciling five spreadsheet formats
- De-duplicated so one real failure isn't ten near-identical records
- Every workflow shares it — RCA, CAPA, and what comes next
Built on the knowledge you already have.
The experience your organization already holds is embedded at onboarding, so your very first analysis builds on your own resolved cases — and that proven experience is weighted ever higher as it accrues.
- Useful from your first analysis — not after six months of data entry
- Your evidence is weighted above generic guidance in every retrieval
- Compounding return — built so the value curve steepens with use
A knowledge base is only worth having if it refuses to flatter you.
Most of what makes stored knowledge trustworthy is what the engine declines to do with it.
It will not score a solution nobody has applied
Effectiveness stays unscored until there is real evidence behind it. An empty track means no applications yet — never a zero dressed up as a result.
It will not let a generic source outrank your engineers
Reviewed, approved experience from your own plants sits above anything imported or external. That ordering is not configurable into your disadvantage.
It will not silently duplicate a case
One record per analysis, updated in place when the analysis is reworked. A knowledge base that grows by duplication is a document graveyard, not an asset.
It will not hide a degraded state
If part of the knowledge base is still building or momentarily unavailable, the search says so. You never get a quiet "nothing found" that actually means "not ready yet".
Depth on the floor, honesty in the AI.
Two things separate DatumKeep from a generic assistant: an AI posture built to be trusted, and a real engineer’s cockpit for the methods your teams already run.
Confidence you can defend in the audit room
Every answer is cited, calibrated, and yours — and honest about what it does not know.
Provenance on every match
Each surfaced case cites its source and the context it matched on — nothing is asserted without a traceable origin your engineers can open and check.
Honest effectiveness scoring
Solutions carry a confidence-calibrated score — never a lone percentage. Thin evidence reads as an early signal with wide uncertainty, not false precision.
Honest when it cannot answer
A degraded or still-building knowledge base says so explicitly. You never get a silent "nothing found" that hides an unconfigured or empty corpus.
Your knowledge stays yours
Multi-tenant by construction with Row-Level Security. Your company-private cases are never blended into another tenant, and the models never train on them across customers.
A person signs off
A quality gate checks the chain of evidence before conclusions stand, and every result is reviewed and confirmed by your team — AI assists, it never has the last word.
The methods your engineers already run — built in
Structured RCA the way the floor actually works it, from problem framing to a verified control plan.
Is / Is-Not analysis
A structured Kepner-Tregoe Is / Is-Not grid bounds the problem before hypotheses are drawn — AI proposes the Is-Not facts, your engineers confirm them.
Change-point timeline
What changed, and when — line events, maintenance and process shifts aligned on one timeline so the trigger window is obvious, not argued.
Risk priority, rated inline
Rate severity, occurrence and detection on a confirmed cause; the risk priority number is computed, tracked, and carried into the corrective plan rather than lost in a spreadsheet. (A full FMEA module is roadmap — this is risk scoring inside the investigation you are already running.)
Control plan
Confirmed causes become a control plan of concrete controls — so the fix is verified in the field, not just written down.
MSA / Gauge R&R gate
Measurement trust is checked with Gage R&R before conclusions lean on the numbers — the workspace flags when the data itself is not validated.
Operator qualification matrix
A no-blame qualification matrix tracks who is trained and current on each operation — informing analysis without ever pointing a finger at a person.
Your terminology, mapped
Map the platform vocabulary to the terms your plants actually use, so DatumKeep speaks your language across every site and team.
Knowledge is half of it. The other half is statistical evidence — control charts, capability reported with its interval, and measurement-system checks, computed by deterministic engines rather than asserted.
See process quality →