Product memory for the agent era

The best product decisions die between the dashboard and the pull request

Sigio captures the signals, evidence, and reasoning behind your specs so your team and your coding agents build from what you actually know, not a flattened summary.

See it in action ↓

For product teams using Claude Code, Cursor, and Codex

Sigio signal detail view showing onboarding completion rate analysis with PostHog and Intercom data
Claude Code
> What context do we have on onboarding?
Querying Sigio MCP server...
 
Spec: Onboarding Redesign
Signal: Drop-off ↑ 18.3% (2 weeks)
Evidence: 3 sources linked
Status: Approved — ready to build

Connects to your stack

PostHog
Intercom
HubSpot
Linear
Jira
Notion
Figma
Claude Code
Cursor
Codex

The problem

Product teams are the integration layer

Your data is in PostHog. Feedback is in Intercom. Priorities are in Linear. Specs are in Notion. Every planning cycle, your team manually stitches these together — and then the work disappears.

Research that evaporates

Last quarter your team spent days understanding churn patterns. This quarter, someone is doing the exact same research from scratch.

Specs that go stale on day one

The spec is accurate until you ship. After that, nobody updates it. Next team to touch that area inherits a document that actively misleads.

Agents that don't know why

Your coding agents ship fast. But they're building from a pasted summary — not the evidence, context, and past decisions that should inform what they build.

What if it compounded instead?

Every cycle should make the next one smarter

Today

The static spec

You’re redesigning onboarding for the third time. The spec references a drop-off metric from last quarter that’s already changed. The customer interviews that shaped v2 are in a Google Doc nobody links to. The agent building v3 doesn’t know what was tried, what the data looks like today, or what customers are actually saying about it right now.

With Sigio

The living spec

The spec is connected to the live signals. The drop-off trend updates as it changes. The past attempts, their outcomes, and the current customer feedback are all linked. Your agent builds v3 knowing everything v1 and v2 taught you.

Sigio — Onboarding Redesign v3
Drop-off rate 18.3% Live
Support threads 12 linked
Past attempts
v1 — Simplified flow (shipped Mar 2025)
v2 — Progressive onboarding (shipped Sep 2025)
v3 — Current spec

Today

The MCP sprawl

Your developer installs PostHog MCP, Linear MCP, Notion MCP, and three others just to understand why a feature exists. They’re assembling context from six tools that don’t talk to each other.

With Sigio

One MCP server

Sigio already synthesized the context. The agent asks “what do we know about onboarding?” and gets the full picture.

Claude Code
> What do we know about onboarding?
Querying Sigio MCP server...
 
Spec: Onboarding Redesign v3
Signal: Drop-off ↑ 18.3% at step 3
Evidence: 12 support threads, 2 prior attempts
Context: v1 simplified flow, v2 progressive — both reduced drop-off temporarily
Criteria: Reduce step-3 drop-off below 12%

Today

The lost thread

Six months ago, your team investigated why enterprise users churn at day 14. The research, the fix, and the outcome are spread across a PostHog dashboard, a Linear project, and a Notion doc. Your PM remembers the context, but the coding agent picking up the next retention ticket has no way to know any of it.

With Sigio

The full thread

Sigio kept the thread. The original research, what you shipped, and how it performed are all connected. Your agent picks up the next retention ticket with the full history of what your team already learned.

Sigio — Retention: Day-14 Churn
Sep 2025 Research: Day-14 churn investigation 3 PostHog funnels · 8 interviews
Oct 2025 Shipped: Onboarding checklist + email drip Churn ↓ 31% → 22%
Mar 2026 New signal: Churn rising again (24%) Agent has full context for next fix

How Sigio helps

Replace the manual work with a system that remembers

Stop digging through dashboards

Tell Sigio what to watch. It monitors your tools continuously and alerts you when something needs attention, with the evidence already gathered.

See it in action ↑

Specs that stay alive

After you ship, Sigio tracks the signals that motivated the spec. Outcomes flow back in. The spec becomes a living record of what you built and whether it worked.

Decisions backed by evidence

Every claim in a spec links back to a source — a PostHog trend, a support conversation, a usage pattern.

Product knowledge should compound, not expire

Sigio captures the signals, decisions, and outcomes across your product work so your team and your coding agents build from everything you've learned, not just what made it into the last spec.

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