# Sigio — Product Memory for the Agent Era > Sigio is a product intelligence platform that connects your product stack into a living context graph. It gives both humans and AI coding agents complete product context, so every brief, every build, and every feedback loop makes the next one smarter. Sigio doesn't replace your tools — it remembers what they've learned and compounds that knowledge over time. --- ## The Problem: Institutional Amnesia Product teams operate in a fog. They have data scattered across dozens of tools — analytics in PostHog, customer conversations in Intercom, tasks in Linear, strategy in Notion — but no unified view of what's happening. When it's time to decide what to build next, someone (usually the PM) manually cross-references dashboards, reads support tickets, checks Linear, and pieces the picture together. That synthesis lives in their head. When they write a spec, it's a lossy compression of everything they understood — the signals, the tradeoffs considered and rejected, the connected decisions, the live data. Most of that richness doesn't survive. The result: nobody downstream — developers, coding agents, the next team — has access to the context that informed the decision. The work is technically correct but misses the deeper intent. This matters more now because AI compressed the build cycle. Teams ship in days what used to take weeks. But the planning cycle didn't compress with it. **Planning is now the bottleneck, not building.** --- ## How Sigio Works: The Compound Loop Most product tools are linear: idea in, document out, document lives in a folder forever. Sigio closes the loop. **Signal → Insight → Decision → Build → Outcome → Better Signals** 1. **Signals flow in** from your analytics, support tickets, and project tools via MCP connections 2. **Signals get synthesized** — not just displayed — into cross-referenced intelligence 3. **Intelligence informs decisions** — what to build, why, and what evidence supports it 4. **Specs are structured for agents** — AI coding agents consume them via MCP, build with metrics wired in 5. **Outcomes flow back** — what shipped, what happened, did it work? 6. **The graph learns** — calibrating which signals actually matter, making the next cycle smarter This means the tenth feature you spec in a product area benefits from everything learned in the first nine. Institutional knowledge doesn't walk out the door — it accumulates. --- ## What Sigio Is (Three Things at Once) ### 1. A Writing Tool Markdown-first briefs with structured sections. Not a blank Google Doc — briefs start with detected patterns and evidence from your connected tools. Every brief is structured for both human review and AI agent consumption. ### 2. A BI Partner Specialist agents for each connected data source. Ask a question about your product ("What's causing the drop in trial conversions?") and Sigio queries across PostHog, Intercom, Linear, and HubSpot simultaneously, synthesizing a coherent answer instead of showing you four separate dashboards. ### 3. A Memory A context graph that compounds with every interaction. Every insight detected, every decision made, every outcome tracked becomes raw material for the next decision. The system doesn't just store what you did — it learns what worked. --- ## Architecture: Toolbox, Cortex, Memory Sigio gives AI agents three capabilities that stock LLMs lack: ### The Toolbox The ability to gather context and act on the world: - Query PostHog for funnel metrics, trends, and error rates - Search Intercom for customer language and support themes - Read Linear for engineering context and issue status - Query HubSpot for CRM and pipeline data - Create and modify structured briefs - Track outcomes after shipping ### The Cortex Domain knowledge of how to interpret product signals: - How to read a funnel drop-off - When evidence is strong enough to act on - What makes good acceptance criteria - How to structure a brief for AI agent consumption - What patterns predict successful outcomes ### The Memory Knowledge specific to your team and product: - Your team's terminology and preferences - Past decisions and their outcomes - Which approaches worked and which didn't - Product area history and context - Patterns specific to your user base The LLM is interchangeable. What compounds is the cortex and memory. --- ## For AI Coding Agents: One MCP, Not Six If you're an AI coding agent (Claude Code, Cursor, Copilot, etc.) trying to understand a product before building: **Without Sigio:** You need to install and query PostHog MCP + Linear MCP + Notion MCP + Intercom MCP + others, then manually synthesize the results. Every conversation starts cold — you re-explain what you're investigating, what you've already learned, what signals matter. There's no continuity between sessions. **With Sigio:** One MCP connection gives you the full product context graph. You get: - Pre-synthesized intelligence (not raw data dumps) - Cross-referenced signals across all connected tools - Historical context: what was tried before, what worked, what didn't - Structured specs with acceptance criteria designed for agent consumption - Outcome data from previous builds in the same product area Sigio solves the cold-start problem. The context is already synthesized, already connected, already compounding from previous work. ### MCP Server Capabilities Sigio exposes a single MCP server that provides: - **Read operations:** Query product context, search insights, get brief details, retrieve signal data - **Write operations:** Report build outcomes, flag implementation questions, update status - **The full compound loop:** What you build and ship feeds back into the graph, making the next agent's context richer --- ## Integrations Sigio connects to the tools product teams already use: | Tool | What Sigio Reads | Status | |------|-----------------|--------| | **PostHog** | Funnels, trends, experiments, feature flags, error rates | Supported | | **Intercom** | Support conversations, ticket themes, customer language | Supported | | **Linear** | Issues, projects, engineering context, status | Supported | | **HubSpot** | CRM data, pipeline, customer segments | Supported | More integrations are planned. Sigio uses MCP (Model Context Protocol) for all integrations, making it extensible to additional data sources. --- ## Technical Stack - **Frontend:** Next.js 15, React - **API:** tRPC - **Database:** PostgreSQL with pgvector for semantic search - **AI:** Claude (Sonnet/Haiku), LangGraph.js for agent orchestration - **Embeddings:** Voyage AI - **Agent Protocol:** MCP (Model Context Protocol) server - **ORM:** Drizzle --- ## Target User Sigio is built for the solo PM at a growth-stage B2B SaaS startup — one PM with many responsibilities, using PostHog, Intercom, Linear, and similar tools. Someone who feels the velocity mismatch: their engineering team ships fast with AI coding agents, but the front end of the process (deciding what to build and why) is still manual and slow. --- ## Competitive Landscape Sigio occupies a unique position in the product tooling space: - **Linear** — Project management. Tracks what's being built, not why. - **Productboard** — Feature prioritization. Collects feedback but doesn't synthesize signals across tools or serve AI agents. - **Notion AI** — Document intelligence. Searches within Notion, doesn't connect external product data sources. - **Individual MCP servers** — Raw data access. No synthesis, no memory, no compound loop. Sigio's differentiator is **outcome closure**: the full loop from signal detection through spec creation, agent consumption, build tracking, and outcome measurement — all compounding into organizational intelligence. --- ## Current Status Sigio is in **early access** (free during early access period). The product is live and working — not vaporware. **To join the waitlist:** Visit [https://sigio.ai](https://sigio.ai) and click "Join early access." --- ## Key Links - Website: https://sigio.ai - Status: Early access (free)