Your team makes decisions in meetings and commitments in threads — and most of it evaporates before Friday. Ambito is the persistent contextual layer your team reads from to remember what was decided yesterday, who owns the launch, and which thread went dead without resolution. The same layer your AI agents — Claude, Copilot, Gemini — can finally read from.
No spam. Researching the problem. Building openly.
Every enterprise tool captures a fragment of work. None of them talk to each other. The result is a context vacuum — and it’s the structural gap behind the 95% of enterprise AI pilots that never reach production.¹
You’ve walked into six meetings since Monday morning. By the third one you stopped taking notes — you were already late to the fourth. A task got assigned in the second one but nobody wrote it down. Someone Slacks you asking “what did we land on for the Atlas migration?” — and you genuinely don’t remember. By Friday, you’ll spend an hour reconstructing the week. Next week, you’ll do it all again.
The internal copilot you launched to your CS team last week has answered 200 questions over the weekend — and forty are wrong. One told a customer the refund window is 90 days. It’s 30. The model isn’t broken; it’s working exactly as designed. It just doesn’t know what your team decided in last quarter’s all-hands, what Engineering announced in Slack, or what’s in the runbook nobody updated. You’ll spend Friday writing correction memos. Next week, you’ll do it again.
The Context Graph: a persistent layer your team reads from and your agents query.
Passively read across every source your team already produces: meeting transcripts, email threads, calendar events, and Slack/Teams channels — plus canonical documents like policies, runbooks, contracts, and specs. No new behavior or manual uploading required from anyone.
Extract decisions, commitments, projects, and people into a queryable knowledge graph with source attribution on every entity. Identity persists across sources and time.
A permission-aware MCP server exposes this context to any AI agent — Claude, Copilot, Gemini, your internal stack. An ambient dashboard organizes it for humans: project-by-project view, task tracking across meetings and threads, and surfaced signals like stalling projects or dropped commitments — without anyone having to ask.
The first ambient dashboard that reads across every meeting, email, and channel your team coordinates in — not just yours — and surfaces what matters the moment you open it. No queries. No searching. The context is already there.
The MCP server your agents needed three months ago. Permission-aware out of the box. Point any MCP-compatible agent at structured organizational context — meetings, threads, policies, runbooks — with source attribution on every response. Without rebuilding RAG every quarter.
Three architecture decisions we settled before writing the first line of code:
Every query is scoped to the requester's authorized context before retrieval — humans through enterprise SSO, agents through OAuth 2.1 scoped tokens (per the MCP standard). No one—and no agent—can access what the person running it can't.
SaaS, BYOK (your LLM keys, your data path), VPC (entire pipeline inside your cloud). The data boundary is yours to choose.
Every response carries the verbatim quote that produced it, with identity-bound audit logging on every query. Agents never receive raw meetings, emails, or chats — only typed entities with source attribution.
SOC 2 Type II, data residency, and sub-processor disclosure are in scope. The architecture that makes them auditable — access control, identity-bound logging, encryption at rest and in transit — is inside the MVP build. Sub-processor list goes live at launch.
of enterprise GenAI pilots fail to reach production. MIT named the cause: tools that don’t learn from real workflows. The deeper gap: no persistent organizational context for those tools to learn against. Average sunk cost per abandoned initiative: $7.2M.
1·2monthly MCP SDK downloads. Backed by Anthropic, OpenAI, Google, Microsoft. Gartner: 40% of enterprise applicationswill include task-specific AI agents by end of 2026 — up from less than 5% today. The protocol is settled. What plugs into it isn’t.
6·7Today’s tools each capture or query a fragment and stop there — whether built around meetings, documents, or many connectors at once. Ambito is the persistent layer underneath — passively ingesting, building the relationships, and personalizing the context in the background, ready before anyone asks. What your team reads and your agents query is already organized.
Tom Blomfield— “I think every company in the world is going to need one.”4 The missing layer between raw company data and reliable AI automation.
Read the Company Brain RFS →Diana Hu— calls it “The AI Operating System for Companies.”5 Names Slack, Linear, GitHub, Notion, and call recordings as the integration set teams stitch together with custom glue code today, with no single product connecting them.
Read the AI OS for Companies RFS →Born in Argentina, I started working in restaurants before teaching myself to code and entering the workforce as an entry-level developer with no college degree. Over the last 4 years, I’ve gone from entry-level developer to VP, Technical Product Manager at JPMorgan — where I now run a pure-API microservices platform and am actively leading an eBAM (ISO-20022, ACMT message set) modernization for Treasury Services.
I’ve watched the same pattern repeat across every enterprise team: people lose context, projects drift, and AI agents now inherit the same gap at scale.
Over 3 of those 4 years, I built Bootcampr in parallel — went through Antler’s VC accelerator, closed it publicly when product-market fit didn’t materialize. Ambito’s first prototype was a Zoom-to-Jira pipeline that auto-generated meeting emails + Jira tickets from transcripts. Expanded it into an enterprise context layer after seeing agent deployments hit the same wall from the other side.
Currently enrolled in Maven’s AI Product Manager certification, with the Ambito work itself selected for Product Faculty’s 5-week documentary series on building a 0-to-1 AI product (May–June 2026). YC Summer 2026 applicant.
Ambito is in active design through June 2026 — looking for first design partners and research participants now. If you’re deploying agents, leading a coordination-heavy team, or just want to follow the build — here’s how:
