gather-evidence

To collect product evidence, normalize raw items from connected sources like support tickets and CRM records into structured claims with personas, timestamps, and strength ratings. Apply identity resolution to deduplicate accounts and weight evidence by recency for downstream ranking.

Evidence normalization transforms raw signals from analytics, reviews, and issue trackers into structured items containing claims, personas, funnel stages, and links. It applies signal filtering and freshness checks to ensure only verified, weighted evidence items pass to decision workflows.

Deduplicate customer feedback across systems by applying identity resolution to match the same person or account across connected sources. Assign confidence scores and weight each evidence item by strength and recency to identify the strongest recurring pain patterns.

Yes, this approach works with unstructured file uploads and web sources by applying source probing and signal filtering. It extracts recurring customer pain from available connectors and normalizes the raw web data into structured, scored evidence items.

The best way to score evidence items is to apply freshness checks and assign confidence ratings during normalization. Weight each item by signal strength and recency, then pass the structured, weighted evidence directly to downstream synthesis and ranking workflows.

You should not use automated evidence gathering when source probing cannot reliably access your target systems, or when raw items lack sufficient metadata for identity resolution and signal filtering. It requires connected sources or structured uploads to produce weighted, normalized evidence.