TL;DR

• Evidence vaults centralize customer feedback, research notes, and support insights with tags and timestamps.
• Effective vaults make evidence searchable, citable, and ritual-integrated -not just archived.
• Product decisions backed by vault evidence win stakeholder trust and reduce "HiPPO" (Highest Paid Person's Opinion) syndrome.

Jump to Why evidence gets lost · Jump to Vault architecture · Jump to Capture workflows · Jump to Retrieval rituals · Jump to Real-world example

The Product Evidence Vault: How To Archive Customer Insights That Actually Get Used

Product teams drown in evidence: Slack feedback, support tickets, user interviews, analytics screenshots. But when roadmap discussions happen, everyone relies on gut feel because finding that one crucial quote takes 20 minutes. A product evidence vault solves this by systematically capturing, tagging, and surfacing customer insights so decisions are backed by proof, not politics.

Key takeaways

• Vaults centralize scattered evidence with consistent tagging (customer segment, feature area, sentiment).
• Retrieval rituals (cite evidence in PRDs, link to vault in roadmap reviews) ensure usage.
• AI agents can auto-tag and summarize vault entries to reduce manual overhead.

Why evidence gets lost

Customer evidence scatters across tools:

• User interviews: Recorded in Zoom, transcribed to Google Docs, maybe shared in Slack, then forgotten.
• Support tickets: Live in Zendesk/Intercom; insights buried in ticket threads.
• Product feedback: NPS comments, in-app surveys, community forums -each in separate systems.
• Sales calls: Gong/Chorus recordings; strategic nuggets lost unless someone takes notes.

According to ProductBoard's Product Excellence Report 2024, 72% of product teams report difficulty surfacing past research when making roadmap decisions (ProductBoard, 2024). Result: PMs repeat research or ship features based on the loudest recent voice, not accumulated evidence.

What makes evidence "vaulted"?

A proper vault isn't just a folder of docs. It's a system with:

1. Consistent structure: Every evidence entry has timestamp, source, customer segment, tags, and the actual quote/insight.
2. Searchability: Full-text and tag-based search return results in seconds.
3. Citability: Each piece of evidence has a permanent link you can reference in PRDs, roadmap decks, or Slack threads.
4. Ritual integration: Product reviews, sprint planning, and roadmap sessions explicitly surface vault evidence.

For knowledge management context, see /blog/ai-knowledge-base-management.

Vault architecture

The vault structure determines ease of capture and retrieval.

Core schema

Every evidence entry includes:

Platform choices

Recommendation: Start with Notion or Airtable. Graduate to dedicated tools (Productboard, Athenic) once manual entry becomes a bottleneck.

Taxonomy design

Tags should be:

• Consistent: Use controlled vocabulary, not free-form.
• Multi-dimensional: Combine customer segment + feature area + sentiment.
• Hierarchical where useful: #enterprise → #enterprise-healthcare, #enterprise-fintech.

Example tag structure:

Customer: #enterprise, #smb, #free, #trial
Feature: #onboarding, #analytics, #api, #integrations
Sentiment: #positive, #negative, #feature-request, #bug
Priority: #p0-blocker, #p1-high, #p2-medium, #p3-low

Capture workflows

Consistent capture beats perfect capture. Automate where possible; make manual entry painless.

Automated capture

Pro tip: Don't auto-import everything -filter for high-signal entries (negative NPS, enterprise feedback, feature requests) to avoid noise.

Manual capture templates

For ad-hoc evidence (Slack conversations, Twitter mentions, conference chats), use quick-entry templates:

Slack shortcut example:

/vault-add
Quote: "This integration would save us 10 hours a week"
Source: Slack DM with @customer-name
Segment: #enterprise
Feature: #integrations
Tags: #feature-request #high-value

Integrate vault shortcuts into Athenic's workflow orchestrator to route high-priority entries to PM for immediate review.

Who captures evidence?

Establish a weekly "vault review" ritual where PM audits new entries, merges duplicates, and flags insights for roadmap consideration.

Retrieval rituals

A vault is useless if no one searches it. Build evidence retrieval into product rituals.

Ritual 1: PRD evidence section

Every Product Requirements Document includes an "Evidence" section citing vault entries:

Example:

## Evidence for SSO feature

- [Vault #142]: "We need SSO before enterprise rollout" – Enterprise customer, 2025-02-10
- [Vault #198]: "Security team blocked trial because no SAML" – Sales call, 2025-02-22
- [Vault #201]: "SSO is table stakes for Fortune 500" – Support ticket #4821

**Conclusion:** 12 enterprise prospects blocked by missing SSO; projected $400K ARR opportunity.

Ritual 2: Roadmap prioritization

During quarterly planning, PM presents top-requested features with vault evidence counts:

This data-driven approach reduces "HiPPO" syndrome (Highest Paid Person's Opinion).

Ritual 3: Customer research synthesis

After user interview sprints, researchers publish synthesis docs linking to vault entries:

Example structure:

## Key Themes from Q1 Interviews (n=15)

### Theme 1: Onboarding friction
- 9/15 users mentioned setup complexity
- Vault evidence: #142, #156, #159, #167, #172, #183, #189, #201, #204

### Theme 2: Missing export features
- 6/15 users requested CSV export
- Vault evidence: #145, #152, #178, #191, #200, #208

For interview analysis workflows, see /blog/ai-customer-interview-analysis.

Real-world example

Context: B2B SaaS company building project management software for creative teams.

Before vault:

• Product decisions driven by founder intuition + loudest customer.
• Research notes scattered across Google Docs.
• Roadmap debates: "I think customers want X" vs "I heard Y."

Vault implementation:

• Chose Airtable; schema: Timestamp, Source, Segment, Feature, Quote, Tags.
• Automated Intercom ticket import for feature requests + bugs.
• PM manually added user interview insights weekly.

After 6 months:

• 380 evidence entries captured.
• Roadmap prioritization shifted: SSO feature (18 evidence entries, 12 enterprise) prioritized over dark mode (22 entries, 0 enterprise).
• PRDs cite 3–8 vault entries on average; stakeholder objections dropped 40% ("I disagree, but I can't argue with 12 enterprise customers").
• Customer success team uses vault to prepare for renewal calls ("Remind them we shipped the feature they requested in Q2").

Key success factors:

1. PM championed the vault; modelled evidence-citing behaviour.
2. Made capture easy: Slack shortcuts, Intercom auto-import.
3. Built retrieval into existing rituals (PRDs, roadmap reviews).

For operational cadence patterns, see /blog/founder-operating-cadence-ai-teams.

AI-powered vaults

AI agents can reduce manual overhead in evidence vaults.

Auto-tagging

Use LLMs to:

• Read support ticket or interview transcript.
• Extract key quote/insight.
• Suggest tags (customer segment, feature area, sentiment).
• PM approves or edits before vault commit.

Example: Athenic's knowledge agents auto-tag incoming feedback and route high-priority entries to PM approval queue. See /use-cases/knowledge.

Semantic search

Traditional search matches keywords. Semantic search understands intent:

• Query: "Why do enterprise customers churn?"
• Vault results: Evidence tagged #churn, #enterprise, even if "churn" wasn't the exact word used.

Tools: Athenic Knowledge, Hebbia, Glean.

Summarization

Ask the vault: "Summarize all feature requests about API from enterprise customers in Q1."

AI reads 20 evidence entries, returns:

"Top 3 requests: rate limit increases (8 mentions), webhook reliability (6 mentions), GraphQL support (5 mentions). Common theme: integration with internal tools."

This saves PM hours of manual synthesis.

Call-to-action (Implementation stage) Start your vault this week: pick a platform, define 5 tags, and capture 10 pieces of evidence from recent customer conversations.

FAQs

How many evidence entries do you need before the vault is useful?

50+ entries creates critical mass where searches return useful results. But start capturing immediately -the vault compounds over time.

Who owns vault hygiene (deduplication, tag consistency)?

Assign one PM or product ops person. Weekly 30-minute audit: merge duplicates, fix inconsistent tags, archive outdated entries.

Should you archive evidence older than 2 years?

Archive but don't delete. Customer pain points resurface; having historical context prevents re-learning the same lessons.

How do you measure vault ROI?

Track: (1) % of roadmap decisions citing vault evidence, (2) time saved vs manual research, (3) stakeholder objection rate in roadmap reviews.

Summary and next steps

A product evidence vault centralizes customer insights with tagging, search, and ritual integration -turning scattered feedback into decision-ready proof.

Next steps

1. Choose your vault platform (Notion, Airtable, or dedicated tool).
2. Define taxonomy: customer segments, feature areas, sentiment.
3. Capture 20 evidence entries from recent conversations.
4. Add "Evidence" section to your next PRD and cite vault entries.

Internal links

• /use-cases/knowledge
• /blog/ai-knowledge-base-management
• /blog/ai-customer-interview-analysis
• /blog/build-feedback-loop-that-scales

External references

• ProductBoard Product Excellence Report 2024 – evidence surfacing challenges.
• Lenny's Newsletter: How To Build a Product Research Repository – vault design patterns.
• Dovetail Research Repository Guide – taxonomy best practices.

Crosslinks

• See also /blog/product-operations-playbook-ai
• /blog/founder-operating-cadence-ai-teams