The memory layer for artificial intelligence.
01 — The Problem
Billions of people now talk to AI every day. Every one of them hits the same wall: the model has no idea who they are. Context dies when the session closes, and the user starts over — forever.
Every conversation begins at zero. The user re-explains their business, their people, their preferences, their history — again.
What one model learns is trapped inside it. Switch tools and the relationship is gone. Nothing travels with the user.
Autonomous agents can't build on yesterday's work. Without persistent memory, every run is the first run.
02 — The Product
MIND gives every AI model a permanent memory of who you are, what you know, and how you think — identity, decisions, people, projects, and context that survive across every session, every tool, and every agent.
It is model-agnostic by construction. MIND plugs into Claude, GPT, Gemini, and local models through the Model Context Protocol, plus a native web app, mobile app, and developer API. We win regardless of which lab wins.
03 — Traction
Revenue is live and contracted, anchored by a signed enterprise agreement. We have spent roughly $2,000 acquiring customers to date — a blended CAC of about $4, with no growth team and no agency.
Contracted and live, anchored by a signed enterprise agreement.
About $2,000 in total ad spend to date. No growth team, no agency.
Every line of the platform built and shipped without institutional funding.
The entire stack designed, built, and shipped by the founder.
The capital efficiency is the signal. $1.2M in recurring revenue, built by one person on roughly $2,000 of lifetime ad spend — because the product solves a problem every AI user already has.
04 — Market
Let OpenAI, Anthropic, and Google spend billions fighting each other over models. Every model they ship has the same blind spot, and every one of them needs the thing we sell. We are not betting on a winner at the model layer — we are selling the infrastructure every winner runs on.
The application layer is a knife fight. The infrastructure underneath it is a toll road.
Memory is the stickiest product category in software. A user's graph gets more valuable every single day they use it — and it cannot be recreated by a competitor, because it is their history.
05 — Why We Win
Every session deepens the graph. Year two is exponentially more valuable than day one, and none of it is portable to a competitor.
We sit above the model layer, not inside it. Frontier labs commoditizing each other is a tailwind for us, not a threat.
The graph runs in production under paying enterprise load today. The infrastructure is validated by real usage and real revenue, not by a pilot.
Core graph and memory-retrieval architecture is filed and pending, covering how context is structured, ranked, and served to models.
06 — Business Model
A self-serve funnel that acquires at near-zero cost, and enterprise deployments that convert an entire company's operating budget into contracted platform revenue.
Personal memory across every AI tool the user touches. The organic top of funnel — and the wedge into their company.
Shared organizational memory plus a developer API and MCP server for teams building their own agents on the graph.
Full deployment with SLAs and purpose-built platforms on top of MIND. This is where the anchor revenue lives.
07 — Founder
Anthony Conti — Founder & CEO, Astra AI. Technical founder, sole owner, and the person who wrote the stack: the graph engine, the apps, the MCP server, the API, and the products running on top of it.
$1.2M in recurring revenue and a signed enterprise anchor client — with no engineering team, roughly $2,000 of lifetime ad spend, and no outside capital. The seed is the first institutional dollar into the company.
08 — The Ask
The hard part is done. The engine is built, the platforms are live, the revenue is contracted, and the acquisition motion works at zero cost. This round buys distribution and depth — not discovery.
The first paid acquisition engine, enterprise sales, and developer relations for the MCP and API surface.
Scale the graph, harden the platform for enterprise load, and expand the model and integration surface.
Infrastructure, security and compliance posture, and the IP portfolio behind the memory architecture.
Astra AI · MIND
We're raising the round that puts distribution behind infrastructure that already works, already has customers, and already generates revenue. If the memory layer is a bet you want to be on, I'd welcome the conversation.