Signals exist,
insights don't.
Product Memory is an organizational memory engine that synthesises signals across disconnected channels, giving Product Managers complete context for every decision.
The signal exists.
Nobody has connected it.
Product teams are data-rich and insight-poor. The signal that should have changed the last three roadmap decisions already existed — in customer calls, support tickets, Slack threads, Mixpanel. It was never surfaced because no single person or system had the full picture.
And when someone did connect it, the reasoning lived in one person's head. When they left, it left with them. This is not a PM productivity problem. It is an organisational memory problem.
Context evaporates
A PM leaves. Three to six months of decision context, signal interpretation, and institutional reasoning disappears with them. The next PM relitigates every decision from scratch.
Decisions get relitigated
The reasoning behind a decision was never stored. Six months later, a new stakeholder questions it. The discussion restarts. The original context no longer exists to end it.
Analytics answers take days
A question emerges mid-meeting. The answer lives across Mixpanel, a SQL warehouse, and an analyst's calendar. By the time it arrives, the decision has already been made without it.
Leadership flies blind
What reaches the VP is several human interpretations removed from the raw signal. A support ticket becomes a Slack summary becomes a talking point. By the time it informs a strategic decision, the original evidence is gone.
Six channels.
None of them talk to each other.
The evidence for the right roadmap decision already exists in your toolstack. The problem is routing and synthesis - not collection.
Locked in Gong. Only the PM on the call synthesises it. Nobody else ever sees the raw signal.
Lives in the support org. Never connects to the roadmap. Volume makes it unreadable without aggregation.
Ephemeral. Scrolls away within days. Never captured as structured insight. Search is keyword-only.
Recorded but never indexed. Action items survive. The reasoning behind them does not.
Requires an analyst or SQL. 2-3 day queue. The meeting has moved on before the answer arrives.
Written for implementation, not reasoning. The conclusion survives. The deliberation that produced it does not.
"Today's Reality"
60-70% of a PM's week goes to gathering and synthesising signal. Context evaporates when they leave the room.
"The AI-native Context Layer"
Every decision is a commit, every signal is a source, and the full history of your product's evolution is always queryable.
Institutional Memory.
Compounding Over Time.
Product Memory acts as a version control system for product knowledge. It sits above your disconnected channels — unifying fragmented data, automating insights, and delivering hyper-contextual reasoning without a single rip-and-replace of your existing tools.
Every decision is a commit. The reasoning survives the people who made them, creating an unbreakable chain of product logic.
Five specialist agents draw from the shared context layer to do work that currently consumes hours of PM time.
From a customer support ticket to a final PRD, maintain a clear lineage of exactly why a feature was built or rejected.
Five Agents.
One Context Engine.
SIGNAL INTELLIGENCE
Surfaces patterns, friction, and gaps before you ask by continuously monitoring support tickets, calls, and slack channels.
Checkout drop-off rising
23 tickets + 4 enterprise calls cite payment friction. Up 40% vs last month.
← Support · Salesforce · Gong
Competitor shipped bulk export
8 customers mentioned it. Backlog has 34 matching votes.
← Gong · Productboard · Forum
API users want webhooks
Consistent theme across 11 dev-segment accounts in 3 weeks.
← Slack · Support · Github
PRD & SPEC
Drafts and validates specifications against your full historical context, ensuring alignment with previous decisions.
Objective
Allow enterprise users to export up to 100k records in CSV/JSON within 60s SLA.
⚠️ Technical constraint flagged
Current job queue caps at 10k rows. 100k requires arch change. Est. +3 sprints.
✓ Customer signal strong
34 requests · 8 enterprise accounts · aligns with Q3 retention goal
ANALYTICS
Natural language queries across Mixpanel, SQL, Power BI, and OTel. Get answers in minutes, not days.
"Which segments have highest onboarding drop-off this quarter?"
Drop-off by segment · Q1 2026
💡 Fintech drop-off correlates with KYC step added in Nov release. 6 support tickets confirm.
ENG HANDOFF
Acts as a spec-to-code bridge, surfacing potential codebase constraints and edge cases before development even starts.
Spec · Webhook Notifications
Real-time event delivery
POST to customer URL on order, payment, status events. <500ms SLA.
Event bus compatible
Kafka infra supports this. Low effort.
Retry logic undefined
Spec missing failure behaviour. PM decision needed.
500ms SLA not feasible
Auth middleware adds ~800ms. Spec needs revision.
DESIGN BRIEF
Translates dense PRDs into comprehensive UX briefs matching your design language and flow specifications.
Screen inventory
🎨 Design language context
Follows existing modal pattern (v2 component lib). Uses ghost button for cancel, primary CTA for confirm. Loading state matches skeleton from dashboard.
Edge cases flagged
Intelligence vs Retrieval
— Beyond standard enterprise searchRetrieval tools are reactive — they answer the question you knew to ask. Product Memory surfaces the question you didn't know you needed to ask, and brings the evidence with it.
Ready to Build Your
Context Engine?
Stop losing context when your PMs leave the room. Schedule a call and transform your context into permanent memory.