9+ channels become
one intelligence layer.
9+ direct channels cover Slack, Email, App Store, Google Play, Public Portal, Feedback Widget, Web Scraper, Web Form, and Reddit. Anything beyond that pipes in via REST API, Zapier, or Make. Distribute through MCP to Claude, Cursor, ChatGPT — and now MCP runs inbound too: connect Confluence and Ask Mind reads your wiki live.
Two directions. One intelligence layer in the middle.
Most PM tools treat integrations as a flat list. The actual architecture is directional.
Every place customers say things.
Slack, Email, App Store, Google Play, Public Portal, Feedback Widget, Web Scraper, Web Form, Reddit — plus REST API, Zapier, and Make for anything custom. Channel attribution preserved on every signal.
Classified in under a second.
Every signal — Slack message, App Store review, widget ticket, Portal vote — runs through classification, deduplication, sentiment scoring, feature linking, and account attribution. By the time a PM opens the workspace, the synthesis is done.
Distribution to AI clients.
MCP exposes 20+ read-only tools to Claude, Cursor, ChatGPT, and any MCP client — and Jira sync (LIVE, bidirectional) keeps engineering in their flow. Signals never get trapped in an inbox waiting to be read.
Every place customers say something becomes a signal. Every place your team works becomes a surface.
Every place customers say things.
The 9+ direct channels cover where most B2B SaaS customers actually leave feedback — chat, ticketing, reviews, in-product. Every signal is captured with channel attribution preserved, so a Living PRD can cite "Slack #customer-success thread" or "App Store · Beta Inc · 3-star review" by name. Pipeline classification happens in under a second per signal.
Every place your team works.
The intelligence layer surfaces wherever the team already lives. MCP serves AI clients. Jira sync keeps engineering on rails. The public portal and feedback widget complete the customer loop. One brain. Different windows for engineering, marketing, support, exec, and customers.
Ask Mind reads where your team writes.
MCP runs both directions. Outbound, AI clients query MindBacklog. Inbound, MindBacklog becomes the MCP client — reaching into your own systems of record and reading them live. Connect Confluence and Ask Mind searches your team wiki the moment you ask — specs, design docs, runbooks, decisions — citing the real page alongside your signals and roadmap data. No index, no copy of your wiki; nothing is imported or stored, and disconnecting removes access instantly. This is a platform, not a single integration: Confluence is live today, Amplitude is next, and it speaks the same open standard as every AI client on the other side. Growth and Scale plans.
Setup takes two minutes: paste a scoped Atlassian API token on the Integrations page, MindBacklog verifies the connection against Atlassian's official MCP server, and Ask Mind gains a grounded wiki-search tool. Amplitude is next — live metrics, funnels, and experiment results as evidence in Ask Mind's strategic answers.
For anything not in the 9+ direct channels.
Auto-classified, linked, sentiment-scored — same pipeline as every native channel. No webhook setup. No middleware. Three examples below cover the patterns most teams need.
Send one classified signal in under 200ms.
curl -X POST https://api.mindbacklog.com/v1/signals \
-H "Authorization: Bearer YOUR_TOKEN" \
-H "Content-Type: application/json" \
-d '{
"text": "Users report search is slow and missing filters",
"source": "internal-tool",
"account": "Acme Corp",
"metadata": {"thread_id": "T-4419", "user": "cfo@acme"}
}'
The pipeline classifies the signal type, scores sentiment, links to features, and attaches metadata. Returns the signal ID and the features it was linked to.
Restore 18-36 months of history in one call.
curl -X POST https://api.mindbacklog.com/v1/signals/batch \
-H "Authorization: Bearer YOUR_TOKEN" \
-H "Content-Type: application/json" \
-d '{
"signals": [
{"text": "...", "source": "productboard-export", "captured_at": "2024-08-12"},
{"text": "...", "source": "productboard-export", "captured_at": "2024-08-14"}
]
}'
Up to 1,000 signals per request. The pipeline reclassifies and links against your existing features automatically. Used by every team migrating from Productboard, Canny, or internal spreadsheets.
Read classified signals back out for downstream automations.
curl https://api.mindbacklog.com/v1/signals?since=2026-05-26T00:00:00Z \ -H "Authorization: Bearer YOUR_TOKEN"
Returns every classified signal since the timestamp with feature links, sentiment, and account attribution. Useful for triggering downstream alerts, dashboards, or your own analytics pipeline.
How a signal flows through the pipeline.
Five steps. Average end-to-end latency is under one second per signal. Concrete example below: a Slack message from a customer success channel about search frustration.
The signal arrives.
Slack webhook fires. The payload includes the message text, channel ID (#customer-success), user, thread parent, and timestamp. MindBacklog acknowledges the webhook and enqueues the signal for classification — no blocking on Slack's side.
The pipeline categorizes and scores.
Classifier identifies signal type (feature request, bug, sentiment complaint), pulls sentiment ("frustrated, search-related, blocking weekly workflow"), tags relevant departments (Customer Success, Engineering), and runs deduplication against the prior 90 days of signals.
Signal connects to features, accounts, competitors.
The engine attaches the signal to the "Search filters" feature card (similarity match), links it to the Acme Corp account (from user email domain), and flags Competitor X — who shipped search filters in May — as competitive context for the Living PRD.
RICE recalculates.
The new signal updates Reach (account count), Impact (sentiment weight), and Confidence (channel diversity). The RICE score for Search filters bumps from 847 to 892. The change is reflected in the Intelligence Hub trending view and the daily intelligence brief.
The artifact reflects the new evidence.
The Living PRD for Search filters now cites 47 signals (up from 46). The Roadmap card shows the updated RICE. The next regeneration of the daily brief includes the trend. Total elapsed time from Slack message to updated artifact: under a second.
What each channel passes through.
Citation quality depends on what the channel exposes. Below: the metadata each direct channel preserves so Living PRDs can cite source by name, account, and context.
Slack
Channel name, user email, thread parent, message timestamp, reactions count. Used to cite "#customer-success · CFO@acme · 3 replies."
Web Scraper
Source URL, scrape timestamp, content snippet, author handle when visible. Used to cite "G2 review · 3 stars · Mar 2026" or "Competitor changelog · feature shipped Q1."
App Store
Star rating, app version, country, review timestamp, reviewer handle. Sentiment is cross-checked against the star rating during classification.
Google Play
Rating, app version, device model, country, helpful votes. Useful for spotting platform-specific bug patterns alongside the App Store stream.
Sender domain (mapped to account), subject line, thread depth, attachment count. Domain-based account attribution lights up automatically.
Web Form
Form ID, custom field map, submitter email, referrer URL. Hosted form or embed code — every submission lands classified.
Public Portal
Vote count, submitter email, comment thread, status. Voting weight feeds RICE Reach directly.
Feedback Widget
In-app page URL, user ID, workspace ID, session length. The widget captures where in the product the user was when they submitted.
What's next — without quarter promises.
Channels actively in build. No specific ship dates because the order can shift based on customer demand. Subscribe to changelog updates for ship notifications.
Azure DevOps
Bidirectional sync mirroring the Jira integration. For teams on the Microsoft stack.
Microsoft Teams
Channel signal capture mirroring the Slack integration. Common ask from enterprise.
GitHub Discussions
For developer-tool products that get most of their feedback in GitHub.
Zendesk
Macro and tag mapping preserved. Ingestion alongside Email and Web Scraper.
Discord
For community-led products. Channel-level capture with role-based attribution.
Anything not in this list is still buildable today via REST API, Zapier, or Make. The "coming soon" label means native ingestion with full metadata fidelity.