Embedo.

The problem

Two problems share one root cause: an organization's real knowledge — the decisions, commitments, and process exceptions made in meetings, emails, and chat — has no structured home. Coordination-heavy knowledge workers watch decisions and action items evaporate across four to six disconnected tools, with no passive signal on which projects are stalling, then spend hours reconstructing what happened.

At the same time, teams deploying internal AI agents keep hitting the wall: "the agent doesn't know our process." Gartner projects 40%+ of agentic AI projects will be canceled by end-2027 because most organizational data isn't positioned to be consumed by agents. Competitors — Microsoft Copilot, Glean, Granola, Fireflies, Otter, Fellow, Zoom AI — are query-driven, stateless per meeting, or locked to a single stack.

The solution

Embedo (Ambito.ai) is a persistent contextual layer that converts meetings, email, chat, and other workplace communication into a permission-aware knowledge graph — the organization-specific context that frontier models reason against. The thesis is deliberate: competing on intelligence is unwinnable for a startup, so the moat lives one layer down, in the context substrate frontier labs can't build from outside the customer.

Embedo occupies three confirmed white-space dimensions: passive ambient intelligence that surfaces stalling projects and dropped commitments without being asked, a longitudinal commitment accountability layer that tracks whether commitments were honored across time, and hierarchical organizational intelligence scaling from individual to team to company with permission-scoped views. A dual-consumer design serves both a human dashboard and an AI-agent MCP server from one architecture.

How it works

A source-agnostic ingestion pipeline covers four MVP sources — meeting transcripts, email threads, calendar events, and Slack/Teams channels. A Haiku classifier → Sonnet extractor pipeline produces typed entities (decisions, commitments, projects, people) with confidence scores and verbatim source-span attribution, stored in a permission-aware knowledge graph. AI project detection clusters communications into named projects with no manual tagging, and commitments are monitored across future communications.

A permission-aware MCP server (FastMCP on Anthropic's MCP Python SDK) exposes this inference layer to any MCP-compatible agent, enforcing access control at retrieval time — agents receive only entities and source spans the requesting user is authorized to see, never raw transcripts, with a P95 < 500ms target and OAuth 2.1 scoped tokens. The knowledge-worker "Ask Ambito" surface becomes a second MCP consumer, synthesizing across sources with inline source attribution. The stack is Next.js 15 + Turborepo, Python/FastAPI, and PostgreSQL + pgvector, deployable as SaaS, BYOK, or VPC.

Who it's for

Embedo is enterprise B2B with two parallel personas served from the same layer. The primary buyer is the Enterprise AI Agent Deployer — an engineering or product leader deploying internal agents who has already found manual prompt injection, Notion/Confluence exports, and custom RAG brittle and stale. This is Y Combinator's explicitly named "Company Brain" buyer.

The primary end-user is the coordination-heavy, outcome-accountable knowledge worker — title-agnostic (account manager, PM, ops lead, engineering manager) — who coordinates across channels and is the person others ask "what did we decide?" Revenue is dual-track: enterprise MCP contracts at $60K–$250K+/year and per-seat SaaS at $30–45/user/month, monetizing the same contextual layer twice.

Why it matters

Both markets are growing fast. Gartner projects 40% of enterprise apps will feature task-specific AI agents by 2026, up from under 5% in 2025 — 8x in one year — while over a billion knowledge workers face fragmented work (48% of employees report work feels chaotic; 30–45 extra minutes per meeting hour on follow-up). Y Combinator dedicated two of fifteen Summer 2026 Request-for-Startups slots to this exact space.

The architectural moat is that one structured context layer serves both a human dashboard and an agent MCP, on heterogeneous non-M365 stacks that Copilot underserves, growing more valuable as MCP adoption expands. Embedo is at capstone-prototype stage, built on a validated pipeline from two prior JPMorgan hackathon builds, with a $2.75M pre-seed sizing and a defined enterprise-first, procurement-led go-to-market.

At a glance

Project

Embedo

Built by

Erick Manrique

One-liner

Enterprise AI layer that converts meetings, email, chat, and other workplace communication into a permission-aware knowledge graph and MCP-queryable context for humans and AI agents.