Weekly checkpoints — quick, low-effort checks
- Source smoke test: scan new items from each source (shared drive, website pages, CRM exports from HubSpot or Salesforce, Marketo/Pardot lists) and mark anything obviously stale, confidential or irrelevant before it reaches the index.
- Redaction & provenance flag: confirm new items have required redaction (PII removed or masked) and a provenance tag (source, date, owner). If you use a sheet as a staging area, add two columns: "redacted?" and "provenance" and require a yes/value before ingestion.
- Retrieval health: run 10–20 sample queries your team uses and note answers with low confidence, missing sources, or obvious date errors. Log each as a short ticket for review rather than trying to fix on the spot.
Monthly maintenance — pruning, deduplication and cadence
Once a month do a deeper pass focused on noise and duplication. Export metadata: title, source, date, hash/fingerprint. Identify duplicate or near-duplicate documents (same hash or very similar title/body) and decide: merge, keep newest, or archive. Keep the rule simple and written down so the team is consistent.
Check your refresh cadence by source. Time-sensitive sources (pricing pages, policy docs, legal) should be refreshed or re-ingested monthly; slow-moving content (company history, evergreen guides) can be on a 3–6 month cadence. Record the cadence in a single sheet or field in your CMS/CRM so automation can pick it up.
Also perform a provenance audit: pick a random sample of answers and trace them back to stored sources. If provenance fields are missing or wrong in HubSpot, Salesforce, Marketo or Pardot exports, fix the ingestion mapping rather than patching documents manually.
Automation patterns, metrics to track and a small human-review loop
Use low-cost automation you already have: a shared Google Sheet or Excel Online as a staging table, a scheduled webhook or simple script (Python/Node) to pull new items, a dedupe step using file hashes, and a scheduled push into your vector index. For small teams this pipeline can run on a single weekday schedule and still keep the corpus useful.
Start tracking a handful of metrics this week: percent of documents updated in last 90 days, daily/weekly drift alerts (queries flagged for low retrieval confidence), monthly duplicate rate, percent of records with provenance filled, and human-review pass rate (sample of AI answers that a reviewer approves). These are simple counts that fit a single sheet and tell you when maintenance pressure is growing.
Keep reviews lightweight: a weekly sample of 10–20 AI answers routed to a two-person review queue, triaged into quick fixes, re-ingest (after redaction/correction), or escalation. If you need a practical hand to map this into your CRM or sheet-based pipeline, Optira can help run a short workshop to build the first weekly→monthly routine without heavy tooling.