Pick the right window and event types
Start by choosing a time window that matches the use case: recent sales conversations might need 30–90 days, while legal or billing issues may need 12 months. Use business rules (e.g. keep billing, contract-signing, meeting outcomes, support tickets, and escalation notes) and drop noisy telemetry (outbound marketing opens, automated system pings).
Decide which activity types to prioritise: status changes, meeting notes, signed documents, problem reports and customer requests are high-value; repeated delivery receipts, low-value task reminders and system-generated pings are low-value. Apply the same principle across platforms — HubSpot activity filters, Salesforce Activity History, or marketing events from Marketo/Pardot can all be filtered to the same operational rule-set.
Normalise, collapse and redact noisy entries
Normalise timestamps and authors to a single timezone and standard name format so the AI doesn't see duplicated identities or time confusion. Collapse repetitive streams: convert an email thread into “3-message thread, latest: customer asks about invoice status” rather than passing every message verbatim.
Deduplicate similar notes using simple text-similarity checks (exact match, same subject line, or short fuzzy match). Redact or tokenise PII: replace national identifiers, full card numbers and personal addresses with placeholders and keep a flag that says what was redacted rather than the raw value.
Build the one‑paragraph context, add provenance and gate it
Create a one-paragraph timeline summary that leads with the most recent action and includes 3–6 short clauses: date → actor → action → outcome, then append source links or IDs on a single line for reviewers. Example: “2026-07-20: Tom called re: overdue invoice (invoice#12345, activity_id=abc); 2026-07-21: Support ticket opened about login issue (ticket#987).” Keep links/IDs so staff can jump back to source easily.
Add lightweight provenance and gating fields for every context packet: source_ids, created_at, summary_generated_by, confidence_score, review_status, last_reviewed_by. Only feed context to the AI when review_status is Approved or confidence_score exceeds a sensible threshold. For small teams this can be a boolean flag reviewers toggle.
Quick implementation options and validation
- CRM workflows: use HubSpot or Salesforce automation to flag, normalise and mark activity types to keep.
- Integration tools: use Zapier or Make to capture webhooks, apply simple transforms, and write a cleaned row into a spreadsheet or database.
- Serverless function: use a small cloud function to run dedupe, redact, and a short extractive summariser when you need more control or lightweight NLP.
- Spreadsheet review: keep a review column in a shared sheet for early human gating before automating the flag to AI.
Validate with simple metrics and sampling: measure reduction ratio (events → retained events), token estimate saved, human review pass rate, and an accuracy check where reviewers sample 5–10 summaries weekly and score whether the AI context contained all actionable points. Start with conservative thresholds, iterate the filters if reviewers keep spotting missing items.
If you want help designing the gating and provenance fields or setting an initial Zapier/Make or serverless pattern, Optira can help implement a small, review-first pipeline for your CRM.