Identify which customers to talk to, send AI‑drafted outreach tied to your roadmap topics, and conduct follow‑up interviews. Every response becomes structured evidence.
"...yeah the export feature is fine, but honestly
the permission model is what's slowing us down
. We can't delegate properly..."
Follow up on permission model concern23 days ago
The insight died in the transcript.
The problem
Discovery is broken at most product teams.
Everyone agrees you should talk to customers more. In practice, the process breaks down.
Discovery is manual and doesn't scale
One PM can do it well. The rest of the team can't keep up with scheduling, interviewing, and synthesizing.
The wrong customers get asked
Without a system matching topics to relevant customers, teams default to whoever responds first.
Follow-up doesn't happen
A customer mentions something interesting in a call. Nobody circles back. The insight dies in a transcript.
How it works
A discovery loop that compounds.
Define what you're exploring. Product Signal handles the rest.
1
Define Topic
Identify which customers have relevant experience for the topic you're exploring based on their actual feedback history, not guesswork.
Exploring topic
Account setup
Relevant customers 2
By relevance
SR
Sarah R.
98%
The account setup was confusing at first, but the tooltips helped a lot.
OnboardingUX
MK
Marcus K.
94%
Setting up my account took longer than expected — steps weren't clear.
Onboarding
Based on feedback history — not manual tags
2
AI Outreach
AI drafts contextual outreach tied to your topics, sends it to the right customers, and conducts follow-up interviews when they respond.
AI outreach
Account setup
AI AgentJust now
Hi Sarah 👋 — we noticed you mentioned
account setup
in your recent feedback. We'd love to dig deeper — would you have 5 minutes to share more?
Sent to Sarah R. · matched by feedback history
Sure! The steps weren't labeled clearly and I wasn't sure what info to have ready beforehand.
SR
3
Compound
Responses become structured evidence linked to the person, the company, and the pains it reveals. The picture gets clearer with every cycle.
SR
Sarah R.
Interview response
2m ago
"The steps weren't labeled clearly — I didn't know what information to have ready."
Structured into evidence
Company
Acme Inc.
50–200 employees
Pain
Unclear setup
Onboarding
Pattern detected: 8 of 24 responses mention team-wide impact — suggesting a company-level pain, not individual friction.
Discovery threads
AI runs the conversation. You focus on interpretation.
Define a topic or hypothesis. Product Signal identifies relevant customers from their feedback history, drafts contextual outreach, and conducts follow-up interviews when they respond. Evidence lands structured on your topic page.
New topicYou define it
Why do users churn in month 2?
Hypothesis · 24 potential customers
Customers identified
AI
SR
TK
ML
JD
+8
Matched from feedback history — all mentioned
churn triggers
or onboarding friction.
Outreach drafted & sent
AI
"Hi Sarah — we're exploring why some users feel less engaged after their first month. Based on your experience, you'd have great insight. Would you have 5 minutes?"
Sent · contextualised to her feedback
Interview conducted
AI
SR
"After onboarding I didn't really know what to do next. There was no clear second step."
Topic page
Evidence incoming
SR
Sarah R.·Acme Inc.New
No clear next step after onboarding ends
ActivationChurn risk
1 of 12 responses processed · picture getting clearer
312 entities synced across 3 sources
Live
Acme Inc.
50–200 employees · SaaS
Company
People · 3
SR
Sarah R.
TK
Tom K.
ML
Maria L.
Pains · 4
Permission modelOnboarding frictionExport limitsSlow support
Linked from
Productboard
Leexi
Customer intelligence
A connected picture of what your customers are telling you.
Product Signal ingests feedback from Productboard, Leexi, and other sources. It builds a graph of companies, people, and their pains — automatically linked, automatically searchable. Every sync makes the picture clearer.
Manifesto
Why we exist
Product teams already hear from customers all the time. The problem is not collecting more feedback. The problem is turning scattered signals into an ongoing discovery system the team can actually use.
Most product teams do not have a feedback problem. They have a continuity problem.
Useful evidence is scattered across calls, notes, and tools. Teams hear plenty. They just lose the thread between one conversation and the next.
Signals live in different systems and formats.
Interesting patterns rarely trigger the right follow-up.
Evidence decays before it shapes the next decision.
02
Who it is for
Built for product teams doing serious discovery without a research department behind every question.
Product Signal fits B2B SaaS teams that already have customer signals coming in and need a better way to decide who to talk to, what to ask, and how to keep the evidence usable over time.
Teams with Productboard, call notes, or support feedback already flowing.
PMs who need stronger discovery without adding operational drag.
Organizations that want continuity instead of one-off research projects.
03
Why it feels different
Designed for continuous discovery, not generic feedback collection.
Instead of only storing what customers already said, Product Signal identifies where the evidence is thin and helps the team close the loop with targeted follow-up.
It is proactive rather than passive.
It targets relevant customers instead of broad audiences.
It treats every response as reusable evidence, not a disconnected note.
From the blog
We write about why discovery breaks, how to make it continuous, and what changes when it runs in the background.
