Solution · AI Operations

Keep your AI's knowledge current: the function that stops your assistant answering with last year's policy

An AI is only as good as the knowledge it pulls from, and that knowledge expires on its own. The base you built on day one drifts from reality — a price changes, a policy, a procedure — and the assistant keeps answering with the old version. Keeping it current isn't "re-uploading the files" now and then: it's an operating function that detects what changed, re-ingests just that, validates and invalidates stale answers, with an owner and a cadence per source.

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The problem

You built the knowledge base on day one and it's aged on its own ever since: the AI answers with the old version and people stop trusting it

The knowledge was loaded once — a dump of files into a vector store — and nobody defined who updates it, on what trigger or with what validation; so within months it describes an operation that no longer exists.
Old and new versions of the same fact coexist: the assistant sometimes gets it right and sometimes answers with last quarter's price, policy or procedure, and the user doesn't know which to believe.
When something changes at the source — the ERP, the CRM, the wiki, the ticketing system — the base doesn't notice: there's no change detection or delta re-ingestion, so the update depends on someone remembering.
Nobody measures freshness: there's no knowing which sources are current, which haven't been refreshed in months, or how many answers are being given over stale content, until a customer or an employee complains.

Cost of staying the same

A knowledge base that ages doesn't fail the day you sign it: it fails at three or six months, when the mix of old and new erodes trust and people stop using the assistant — the documented reason so many AI deployments lose adoption just when they should take off. And the cost is double: on one side you throw away the project's investment, because an AI nobody trusts is an expensive license left unopened; on the other, every answer with the stale policy is a real risk — a mis-priced commitment, a wrong compliance fact, a promise that no longer applies. Knowledge doesn't maintain itself, and treating it as a one-off build instead of an operation is the silent way a project dies of apparent success.

The solution

We build and run your AI's knowledge as a living function —change detection, delta re-ingestion, validation and invalidation of the stale— with a cadence and an owner per source

1We separate knowledge by behavior instead of dumping it all in one bag: the stable stuff (explanations, procedures, definitions) lives in a document repository; what changes live (status, prices, availability) is read from the operating systems — ERP, CRM, ticketing — on the spot, not frozen in a copy that expires. It's the discipline of integrating AI with your systems applied to knowledge.
2We build the refresh pipeline: it detects what changed in each source, re-ingests and re-indexes only the deltas — it doesn't reprocess everything every night — and sets the cadence per source, from minutes for fast-changing content (tickets, wiki) to daily for documentation, respecting each system's rate limits.
3We validate before publishing and invalidate the stale: new content passes a quality check before it goes in, and when a source changes, the answers that depended on the old version are flagged stale so the AI stops serving them. What's doubtful escalates to a person instead of publishing blind.
4We add an owner, a trail and measurement: each source has a responsible person and an update trigger, each refresh logs what changed and when, and a dashboard shows real freshness. All measured: average age per source, coverage of sources kept current, answers served over stale content and time from a change to the AI reflecting it.

What changes

What you stop losing

The AI stops answering with the old version because knowledge refreshes at each source's pace — not once a year — so adoption doesn't drop at three months out of distrust.
Mechanism
The refresh is cheap to sustain because it re-ingests only the deltas — what changed — instead of reprocessing the whole base every night, respecting each system's limits.
Mechanism
Freshness becomes governable: each source has an owner, cadence and trail, so you can prove what the AI knew and when, instead of finding the stale fact in a customer's complaint.
Mechanism
What we measure: average age per source, coverage of sources kept current, % of answers served over stale content and time from a change to the AI reflecting it.
What we measure

Spec sheet

Work it removes: letting the knowledge your AI relies on get loaded once and age on its own —no change detection, no delta re-ingestion, no validation and no owner— until it answers with last year's policy, price or procedure
Typical setup: 3–5 weeks
Input: your knowledge sources (wiki, docs, Notion, SharePoint) and your live systems (ERP, CRM, ticketing) the AI pulls answers from, and the changes happening in them every day
Output: a pipeline that detects what changed in each source, re-ingests and invalidates only the deltas, validates before publishing, and keeps freshness with an owner, cadence and trail per source
Works with: NotionSharePointGoogle DriveConfluence
Can connect to: Your ERP / CRM / ticketing systemYour vector store / retrieval engine (RAG)
What we measure: average age per sourcecoverage of sources kept current% of answers over stale contenttime from a change to the AI reflecting it
Good fit for: companies with RAG, internal assistants or agents in production that pull from a knowledge base and notice adoption dropping or answers going stale after a few months
Not a fit for: those with no assistant or RAG in production yet: first you build the capability, then you operate its freshness; and the editorial call of what the correct policy is, which stays with each source's owner

Frequently asked questions

This starts where that ends. Building the RAG is constructing the capability: connecting the sources, indexing them and getting the assistant to answer. Keeping the knowledge current is operating it afterward: detecting what changes, re-ingesting just that, invalidating the stale and sustaining freshness month to month. A well-built RAG nobody maintains ages just the same; that's why this is a continuous function, not a project that closes.

Because it's expensive, slow and fragile. Reprocessing the whole base every night burns resources, hits each system's rate limits and still doesn't tell what changed from what didn't. The way that holds up is the reverse: detect the change at the source and re-ingest only that delta, at the cadence each source needs — minutes for fast-changing, daily for stable. Less cost, fresher, and with a trail of what got updated and when.

We run it, and that's the point. Freshness isn't installed, it's maintained: sources change, new ones appear and cadences get tuned. We build you the pipeline and the dashboard, and we run it as an AI Operations function — with an owner, SLAs and measurement — so the knowledge your AI pulls from stays current without depending on someone remembering. If you'd rather run it yourself, we leave it built and the criteria written down.

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You’ve pinned the problem. We ship the fix and leave it measured.

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