Platform / The loop
One loop, eight steps, one gate.
AI captures your company's knowledge, cleans it, and keeps it current. A named person approves it. Nothing goes live without that.
The Cognatum Knowledge Loop. Eight steps on a closed loop, in four phases. Intake: 01 Capture, 02 Structure. Refine: 03 Clean. Elevate: 04 Enrich, 05 Improve. Govern & activate: 06 Approve, 07 Integrate/Deploy, 08 Reuse. Step 08 returns to step 01, around Knowledge base.
Knowledge base
Governed, for people and AI
01 · Intake
Capture
Ingest from every source.
02 · Intake
Structure
Normalize the format.
03 · Refine
Clean
Update, deduplicate, reconcile, archive.
04 · Elevate
Enrich
Add context and tags.
05 · Elevate
Improve
Refine clarity, usability.
06 · Govern & activate
Approve
Human sign-off, audited.
A person holds this step
07 · Govern & activate
Integrate/
Push live everywhere.
08 · Govern & activate
Reuse
Serve people and AI.
→ back to 01
Intake
- 01 Capture Ingest from every source.
- 02 Structure Normalize the format.
Refine
- 03 Clean Update, deduplicate, reconcile, archive.
Elevate
- 04 Enrich Add context and tags.
- 05 Improve Refine clarity, usability.
Govern & activate
- 06 Approve Human sign-off, audited. A person holds this step
- 07 Integrate/Deploy Push live everywhere.
- 08 Reuse Serve people and AI.
Without Cognatum
Where AI projects stall.
An AI system does not go live until a review board signs it off.
Regulated enterprises are deploying AI assistants and agents they cannot prove are answering correctly, and their AI initiatives are stalling at governance review because of it.
Governance review rarely objects to the model. It objects to the knowledge the AI reads: nobody can say who approved that content, when, or against which source.
The Cognatum Knowledge Loop is an eight-step process that runs continuously. AI captures knowledge from every system where it is created, structures it, cleans it, enriches it, and improves it. A human approves it. Cognatum then deploys it to every channel that needs it, and consumption signals feed the next cycle.
Approved records
318
In review
41
Flagged stale
27
Open gaps
37
- Which regions are approved for data residency? 24
- Who approves a retention exception over seven years? 19
- What evidence do we keep for a supervisory review? 17
- Which model versions are approved for regulated answers? 14
- How is a superseded procedure withdrawn from every channel? 11
With Cognatum
Eight steps, four phases.
AI runs seven of the steps. A person holds one, and it is the one that decides what goes live.
- Intake, steps 1 and 2. Knowledge enters from every source and is normalized into a consistent, findable form.
- Refine, step 3. A continuous hygiene pass: update, deduplicate, eliminate contradictions, archive.
- Elevate, steps 4 and 5. Context, tags, relationships, and clarity, driven by how the knowledge performs in use.
- Govern and activate, steps 6 to 8. The human gate, deployment to every channel at once, and the consumption that feeds the next cycle.
Phase one
Knowledge comes in.
Step 1 is Capture. AI connects to the systems where knowledge already gets written: shared drives, chat, tickets, CRM records, wikis, and portals. Everything it finds arrives as a candidate, and a candidate is served to nobody. It exists to be worked on.
Step 2 is Structure. Every capture becomes one kind of thing: a title, a body, a link to where it came from, and an owner. Nobody can sign off on a screenshot in a chat thread, and nobody can trace one afterwards.
Intake is deliberately wide. Capturing broadly costs you nothing, because nothing captured is live. The gates come later, and they are narrow.
The question
Which retention period applies to client trade communications?
Today, without a governed home
- A slide deck from two years ago says five years.
- A policy PDF on a shared drive says six.
- A chat thread says it depends, and links nothing.
- Nobody knows which one is current.
With one approved entry
Six years, the first two in an accessible place, on non-rewriteable media.
Phase two
The work nobody staffed.
Every company agrees the cleanup matters. Almost none has ever funded it, because it competes with everything that has a deadline. The library grows anyway.
Step 3 is Clean, and it holds a phase to itself: what a person is asked to approve should be worth approving.
- Update. AI compares each entry against its sources. Change a source and every entry that depends on it is flagged and routed for re-check. The revision is drafted and queued for the Approve gate; the approved version stays live until a person signs off on the new one.
- Deduplicate. Near-duplicates are merged into one canonical entry, because every surviving duplicate is a chance for an AI system to retrieve the wrong copy.
- Eliminate contradictions. Disagreements between two entries, and between an entry and its source, are detected rather than waited for.
- Archive. A retired entry stops being served on every channel at once, and its full record, approval history included, is retained.
Approved
318
In review
41
Flagged stale
27
- Approved
Which retention period applies to client trade communications?
D. Whitfield · approved 3 days ago
- Approved
Which approvals are required for a retention exception?
M. Alvarez · approved last week
- In review
How long are supervisory review records kept?
J. Park · in review
- Stale
Which regions are approved for data residency?
unassigned · source changed
Conflicts
When two sources disagree.
Two entries say different things. Both were approved. Both are live.
Cognatum compares entries against each other and against their sources, so a disagreement is detected rather than waited for. What happens next depends on whether the answer is knowable from the record.
- Where one version supersedes the other on a date the record can show, AI reconciles it and queues the result for the Approve gate.
- Where it is a genuine disagreement, the two are held apart and routed to a named person, never averaged or resolved by recency.
- While a flag is open, no contested version is served as approved: the question comes back as a conflict, not a confident answer.
Item A states backup sites are NOT in cold standby mode, while Item B states they are NOT in warm standby mode, leaving no logically consistent availability mode for backup sites to occupy.
95% confidence · 98% similarity · llm adjudication
Phase three
Context, clarity, and use.
Step 4 is Enrich. AI adds context, tags, relationships, and who each entry is for. For your people that means finding things. For an assistant it means answering from the entries approved for that audience, rather than from the nearest matching text.
Step 5 is Improve. AI refines wording based on how an entry performs when people use it: what gets reused, what gets abandoned, what makes somebody ask a follow-up.
walk_hits=2, co_cited=3, embedding_cos=0.87
The gate
A person signs it.
Step 6 is Approve. AI prepares the package: the entry, its sources, its history, and exactly what changed since the last approved version. A named person reviews it and signs off.
The decision is recorded with the approver, the date, and the source. Every approved entry carries that record from then on, in a form a reviewer can read at a glance.
The word for that record is provenance: who approved an entry, when, from what source, and at which version.
“I approved this once. I should not be answering the same question eleven times a year.”
Published as the approved answer.
Approver: you · just now · sha256 c4d1…b90
Everywhere at once
Then it goes everywhere.
Step 7 is Integrate and Deploy. The approved entry goes to every connected channel at once, over MCP, the open standard AI assistants use to reach outside data, and a plain API. There is no rollout channel by channel, and no window where the portal and the assistant say different things.
Step 8 is Reuse. The entry serves your people, your applications, your workflows, and your AI systems, with that record attached to every answer. Clearance is checked on every request, so an agent answers within the clearance of the person it acts for, never through a shared service account.
Consumption generates the signals that feed Capture, Clean and Improve in the next cycle. The loop closes and begins again.
Cognatum governs the entry
source · version · approver · permissions
Fixed language
Wording that must not change.
Paraphrase is what a language model does by default. It is also the exact failure that disclosure review, labeling review, and supervisory review exist to catch. A model that helpfully tightens a sentence has changed a regulated statement, and no amount of provenance on the answer repairs that.
So some entries are marked as fixed language. A fixed entry is returned word for word, in full, or it is not returned at all. It is exempt from the Improve step, it cannot be summarised into an answer, and an assistant drawing on it quotes rather than restates.
- Disclosure and risk language that was reviewed as written
- Labeling and indication text, and the claims tied to it
- Contract and policy wording that a counterparty has agreed to
Returned word for word
“These statements have not been evaluated by the Food and Drug Administration. This product is not intended to diagnose, treat, cure, or prevent any disease.”
D-102 · v4
- PDP template Unchanged
- Support macro Unchanged
- AI assistant Refused
What was refused
Asked to send “isn’t meant to” in place of “is not intended to”. Rewording approved text is refused, not corrected.
Where next
The same machinery elsewhere.
The loop leads with the governance-review problem because that is where an unproven answer costs the most.
Support
Support
approved entries answer internal and customer-facing questions, instead of whatever an agent happens to find.
Onboarding
Onboarding
new hires learn from the approved version of what the organization knows, not from the first document a search returns.
General knowledge management
General knowledge management
any body of knowledge worth governing can run the loop. The regulated use cases lead; they do not limit.
Common questions
What a reviewer asks.
What happens at the Approve gate?
AI prepares an approval package: the entry, its sources, its version history, and a precise account of what changed since the last approved version. A named approver reviews the package and signs off, and the decision is recorded with the approver's identity, a timestamp, and the source. Nothing is served to any consumer, human or machine, without passing this gate.
How does provenance work on an entry?
Every live entry carries a provenance record: the named approver, the approval timestamp, the source it derives from, and its version history. When an AI system answers from Cognatum, the answer is tied to the specific entry and version it drew from, so the chain runs from the answer back to a dated, attributable approval decision. That chain is what a reviewer or auditor walks.
What happens when two sources conflict?
The Clean step detects contradictions between entries and between an entry and its sources. Where the record resolves the conflict unambiguously, for example a dated supersession, AI reconciles it and queues the result for approval. Where two authoritative sources genuinely disagree, the conflict is flagged for human review, and no contested version is served as approved until a human resolves it.
Can the assistant reword approved language?
Not for entries marked as fixed language. Those are returned word for word, in full, or not returned at all. They are exempt from the Improve step, they cannot be summarised into an answer, and an assistant drawing on one quotes it rather than restating it. Everything else is subject to the normal loop, where a rewrite is a proposal that queues for the Approve gate.
What happens when we get a question nobody has approved an answer to?
It is recorded as a gap rather than absorbed. The assistant says there is no approved answer instead of writing one that sounds right, and the question is routed back to Capture with an owner. Gaps are evidence too: what your library does not cover is a thing a reviewer can ask about, and it is better to hold that list than to find it one bad answer at a time.
How do AI assistants and agents connect?
Over MCP and REST API. An assistant or agent connects to the Cognatum MCP server and retrieves governed entries with provenance attached, scoped to the permissions of the requesting user or system. The same interfaces carry usage signals back into the loop, and write-back to source systems runs over the same connections.
What does an evidence export look like?
For an entry, a set of entries, or a review period, the export assembles the approval records with approver, timestamp, and source, the version history, the deployment record, and any retirement record. It is evidence you supply toward your own obligations under frameworks such as ISO 30401, the EU AI Act, or NIST AI RMF. Cognatum does not confer compliance with any of them; it gives you the trail your reviewers ask for.
How does an entry retire?
The archive sub-step of Clean identifies entries that no longer apply: superseded, expired, or contradicted by an approved successor. A retired entry stops being served on every channel at once, and its full record, including its approval history, is retained. An answer given while the entry was live remains traceable after retirement.
Does an approved entry stay approved when its source changes?
No. When the update sub-step detects a material change in a source, the entry is flagged and a revision routes through Clean and the Approve gate as a new version. Until a human approves the revision or retires the entry, consumers continue to receive the last approved version, with its approval date visible, so the state of the entry is never ambiguous.
How much of the loop do our people have to staff?
Humans concentrate at two points: the Approve gate, and conflicts the Clean step flags when authoritative sources genuinely disagree. AI runs capture, structure, cleaning, enrichment, improvement, deployment, and serving continuously. The division is deliberate: machine effort goes to the upkeep that grows with the library, and human judgement goes to the decisions that carry legal weight.
Knowledge governed. Intelligence everywhere.
See it on your own content, in your own environment.