Starting with meetings, expanding to all company data — Indigo structures the information that matters and makes it queryable by every AI agent in your stack.
Confidential — for discussion purposes only. Not an offer to sell securities.
Decisions live in meetings. Context lives in docs. Relationships live in email threads. None of it is structured, connected, or accessible to the AI tools teams are adopting.
Company knowledge is scattered across meetings, documents, Slack, email, and CRMs. 40% of the workweek is spent in meetings alone — $37B/year in unproductive meeting costs.
70% of decisions aren't tracked after the moment they're made. Institutional knowledge walks out the door with every departure, reorg, and forgotten thread.
8 in 10 knowledge workers want AI gains — but AI tools are only as good as the context they can access. No structured onramp exists for real company data.
The deeper problem: meetings, docs, comms, and institutional knowledge all live in silos — unstructured, unsearchable, and completely invisible to AI. Your agents can't act on context they can't reach. The company data layer doesn't exist yet.
AI productivity + meeting intelligence global market (2025-2030 CAGR: 28%)
English-language knowledge workers with active AI tool budgets
Decision-makers and executives: 6M users at $40/month
AI productivity tools CAGR — category accelerating fast
Meetings are the richest, most understructured data source in every company — and the perfect entry point. Indigo captures that signal first, then expands to structure all company data into a queryable layer for AI.
Every important decision happens in a meeting. Indigo captures decisions, action items, and context in real time — structuring the data that matters most, first.
Meetings are just the beginning. The same architecture expands to docs, email, Slack, CRM, and every data source — building the unified context layer AI agents need.
Native macOS + Windows. Global shortcut I. Always-on, auto-captures. Command palette for instant queries. Real-time decision detection.
Terminal-native access to your signals. JSON output for scripting. BYOK model config (OpenAI, Anthropic, Google, xAI). Pipe intelligence into any workflow.
npm i -g indigo-cliCentral hub for signals, knowledge graph, settings, analytics. OAuth with Google Calendar, Gmail, Drive. Mobile-responsive.
Shipping in v0.2: MCP server — Claude Desktop and any AI agent can query your Indigo signals directly. One config line; your meetings become AI-queryable infrastructure.
MCP is becoming the standard interface between AI agents and business data. Indigo structures company data — starting with meetings, expanding to docs, comms, and CRM — and makes it queryable by any MCP-compatible tool.
Indigo's MCP server exposes query_collection, aggregate_collection, and collection_info — giving AI agents read-only, tenant-isolated access to your structured company data. Every Indigo user becomes a live MCP data provider.
Meetings are the beachhead. The long game is structuring every company data source — docs, comms, CRM, and beyond — into a unified layer that AI agents can query and act on.
Meeting data layer: capture, structure, query. Desktop + CLI + Web. MCP server shipping in v0.2.
Add docs & comms: Gmail, Slack, Drive integrated into the data layer. Knowledge graph UI. AI agents query across meetings + documents.
Full company data layer: CRM, project tools, and every business system structured and queryable. AI agents act on unified company context — not siloed apps.
Recognized Indigo's meeting intelligence thesis
Per bootcamp cohort. Early proof of enterprise demand for the OS methodology pattern.
Flywheel: Advisory clients become power users -> product feedback loop -> SaaS improvements -> Data Platform unlocks API revenue -> organic bottom-up growth via CLI/developer channel -> Advisory -> SaaS -> Data Platform loop compounds.
Our defensible moat: the context protocol layer. Competitors capture audio. Indigo captures meaning — and exposes it as queryable infrastructure for your entire AI stack. As MCP adoption accelerates, Indigo becomes more valuable as the data source.
Engineers discover Indigo via npm install -g indigo-cli. Terminal-native access lowers friction. JSON output enables integrations. Developers pull Indigo into their org.
CEO builds in public on X and LinkedIn. Product updates, AI insights, and methodology content drive organic discovery. Advisory leads come through thought leadership.
Exec AGI Bootcamp clients experience Indigo + HQ methodology firsthand, become product power users, refer their networks. Advisory revenue funds product development.
Applied to Vercel AI Accelerator (Feb 2026) — AI credits from Anthropic/OpenAI/AWS fund the model-agnostic layer. Demo day leads to seed investor introductions.
Serial entrepreneur running three ventures simultaneously (LiveRecover, Indigo). Architect of the HQ Personal OS methodology. Drives product vision, GTM, and investor relationships.
Technical co-founder leading day-to-day product and engineering. Built the hot/cold data architecture, MCP integration, and multi-surface delivery (desktop + CLI + web).
Serial entrepreneur. Co-founded Winc (wine DTC). Founded Westbound & Down Brewing (acquired Aspen Brewing). Turned around Banctek Solutions (payments, 80+ employees, sold). Built restaurant and hotel group (7 locations, 200+ staff). 15+ years strategy and operations. Daniels College of Business, University of Denver. Existing Voyage/LiveRecover investor.
10+ years experience, 300+ projects, ~7M weekly npm downloads. Led Facebook open-source project (ur.react.dev). Contributed to Epic Games. Core engineer across Indigo desktop, CLI, and web.
Visionary branding and UX design. Drives Indigo's product design language, knowledge graph UI, and user experience across all surfaces.
Community-first capital that aligns our early believers with our growth.
Min — [MAX] max
Pre-money valuation TBD
Not an offer to sell securities. A formal offering circular will be filed with the SEC via a registered intermediary. Investments are subject to risks including potential loss of principal.
Proceeds accelerate three vectors: product (MCP layer + knowledge graph), distribution (developer GTM + advisory scale), and team (GTM hire).
MCP server GA, knowledge graph UI, Daily Brief, cloud agent deployment. v0.2 to v1.0.
GTM hire, developer community (CLI/npm), PR outreach, advisory program scaling, content.
AI compute costs, infrastructure (Vercel, MongoDB), legal/compliance (Reg CF, SOC 2 roadmap), G&A.
Q1: MCP server GA, knowledge graph UI, [ARR target]
Q2-Q3: GTM hire onboarded, enterprise pilot, [user target]
Q4: Multi-source data platform live, [ARR target] run rate
We're not just raising money. We're building a stakeholder community that grows with Indigo — and positions us for the public markets.
Open round on [Wefunder / Republic]. Democratize ownership. Our users become our evangelists with economic upside.
Deploy capital into data platform and enterprise distribution. Build ARR. Institutional seed when unit economics prove out.
Reg CF shareholders already hold registered securities. Direct listing provides liquidity without traditional IPO underwriting. Community becomes the float.
Reg CF creates equity holders from your user community. As the Data Platform scales, these equity holders become the foundation for a direct listing — turning community capital into liquid public shares.
Every company generates critical data across meetings, docs, comms, and tools — but none of it is structured for AI. Indigo organizes all of it into a queryable layer that every AI agent in your stack can access.
Questions: corey@getindigo.ai / Product: getindigo.ai
This presentation contains forward-looking statements. Past performance does not guarantee future results. Investing in early-stage companies involves significant risks including potential loss of all invested capital. This is not an offer to sell securities. Any offering will be made only through a formal offering circular filed with the SEC. Please review all offering materials carefully before investing.
| Component | Stack | Status |
|---|---|---|
| Desktop App | Electron, migrating to Tauri (Rust) | Live |
| CLI (indigo-cli) | Node.js, npm, TypeScript | v0.1.3 |
| Web Dashboard (HQ) | Next.js, Vercel, TypeScript | Live |
| Signals MCP Server | Node.js MCP SDK, MongoDB | Shipping v0.2 |
| Data Layer | MongoDB (tenant-isolated), Cognito auth | Production |
| Integrations | Google Calendar, Gmail, Drive (OAuth) | Live |
| AI Layer | Model-agnostic: OpenAI, Anthropic, Google, xAI | Live |