Product Intelligence

What this is for

Give the whole product team one calm place to understand what changed, decide what deserves attention, and keep shipped work connected to the result it was meant to create.

When this helps

• Analytics tells you what moved but not why.
• Customer language, strategy, and delivery context live in separate tools.
• Teams ship work and lose the thread before anyone checks whether it helped.
• AI answers sound plausible when the live source was never checked.

How it works

Product Intelligence composes your existing Product Graph, connected integrations, inbox items, priorities, documents, and outcome validations into a read-only cockpit. For a current question, AI Chat still receives the full built-in toolset and any connected MCP tools; it can inspect the live source first, then use workspace memory to explain the result. The cockpit does not make a roadmap commitment for you.

Before you start

• One real product question in plain language.
• At least one trusted source such as PostHog, Slack, Help Scout, JustCall, Jira, Linear, Trello, Shortcut, or an uploaded document.
• The decision boundary: what you might change, test, pause, or learn next.
• Optional constraints such as launch timing, team capacity, risk, segment, or target metric.

What you should see

• A snapshot of signals, documents, active sources, and open attention items.
• Priority candidates with their score or evidence rationale.
• Coverage gaps that make a confident answer impossible.
• Open outcomes and the next review point for shipped work.
• A direct hand-off into AI Chat, Inbox, Prioritization, Strategy, or Outcomes.

Try it now

1. Open Product intelligence from the sidebar.
2. Start with a question such as “Why did signups change over the last five days?” or “Which customer problem should we solve next?”
3. Open the relevant attention item and inspect the linked source evidence before accepting the summary.
4. Use Ask Specky to investigate the live analytics, support, delivery, or MCP source; ask it to show what it checked and what remains unknown.
5. Turn the result into a priority, PRD, experiment, or outcome validation only after a human reviews the evidence and assumptions.

A real example

A founder sees signups drop for five days. The question goes to Specky, which checks PostHog first, compares the change with a recent release, then looks for matching support and Help Scout language. The useful output is not a confident story invented from a dashboard: it is a short brief that separates the observed drop, the evidence-supported causes, the missing data, and the smallest next test.

Your next move

Run Track feature impact with outcome loops after the decision ships, and use Integrations Overview when an important evidence source is still missing.