ATLAS MYND

We build the brain your company runs on.

Atlas Mynd makes mid-market companies AI-native — not another AI-enabled tool, the whole business. We build you a brain that holds everything your company knows, ship the workflows that run on it, and everything we build is yours to keep.

Tell us the problem you’d want solved first

The problem

Most companies have integrated AI. Few have changed because of it.

The standard playbook is switching on the copilot inside every tool you already pay for. Each one helps a person for a few minutes a day — and none of them changes the company, because each is sealed inside its own walled garden, blind to everything your business knows outside it. That isn’t a new way of working. It’s the old way with a faster engine.

Where we’re going

We take companies to Level 4 today. True Level 5 — a company that improves itself — almost no one has reached. We intend to get there first, and to bring every client with us.

  1. L1

    Personal productivity

    Individuals drafting with chatbots. The gains are personal — they leave with the person.

    ~60% of companies

  2. L2

    Team coordination

    Shared prompts, copilots inside your SaaS. Helpful, sealed off, gone when the champion leaves.

    ~25% of companies

  3. L3

    Organizational infrastructure

    AI wired into real systems, with governance and shared context. Knowledge starts to persist.

    ~10% of companies

  4. We take you here L4

    Company operating system

    Context flows across functions. The company runs through AI, not alongside it.

    ~4% of companies

  5. Then here L5

    Self-improving organization

    Your corrections teach the system. The improvements compound.

    <1% a handful

Percentages are directional estimates.

The data

The companies treating AI as an operating system are pulling away.

This isn’t a hunch. Ramp publishes spend data drawn from more than 50,000 companies on its platform, and the pattern is blunt: the top quartile of AI spenders is breaking away from everyone else on revenue growth. The gap isn’t between companies that have AI and companies that don’t — almost everyone has it. It’s between companies run on it and companies that bolted it on.

Ramp itself has built on the order of a hundred internal AI tools. Not one platform — a hundred specific solutions to a hundred specific problems, built by people who know the business. That kind of org-wide AI culture is a pipe dream for 99% of companies, because you can’t hire for it; the people who can build it already work somewhere that has it.

That gap is our job. We deliver each of those hundred internal builds to you as a working module, on demand — the same compounding, without staffing a lab.

2023 Today Revenue growth, indexed Top-quartile AI spenders Everyone else
Source: Ramp Economics Lab — card and bill-pay spend data across 50,000+ U.S. businesses. Chart is a stylized rendering of the published trend, not Ramp’s figures.

How it works

One brain underneath. Modules on top — one expensive problem at a time.

The brain

Always-on ingestion of everything your company already produces: meetings, Slack, email, Drive, your systems of record. It all lands in one place and gets structured — people, decisions, history, relationships — so AI can retrieve it and reason over it. We call this context engineering, and it’s most of the work. It reads in place: nothing to migrate, no exports, no new system of record.

The modules

On top of the brain we ship modules: custom workflows that attack one named, expensive problem each. Not a platform your team has to learn — a result that shows up. There’s no fixed catalog and no roadmap vote: we build whatever your business needs next. And because every module draws on the same brain, each one is cheaper to build than the last.

Sources — read where they live · each mention lands in the layer that owns it

Upkeep layers — automatically captured, synthesized, and updated

Modules — one expensive problem each

One moment, six layers, six places updated — and several sources can corroborate the same fact. Hover a layer to see every source feeding it; hover a module to see what it draws on.

Detail
Sources are read where they already live — nothing migrates. Each mention is written into the layer that owns it, structured into the brain, and turned into the modules that run on top.

Sources — read where they live

Transcriptmeetings & calls
DocumentsNotion, Google Docs, Linear
External signalsalerts, regulatory scans, press

“We’re pausing the EU launch — eng time goes to enterprise onboarding.”

Upkeep layers — captured, synthesized, updated

Decisions→ decisions log
Action items→ task queue
Contacts→ directory
Lessons→ playbook
Memory→ state-of-company
Research→ research file

Your brain

semantic · graph · raw, over MCP

Reached through Slack, email, published docs, and Claude. Upkeep and synthesis jobs keep it correct as it grows.

Modules — one expensive problem each

Decision trace“Why did we pause EU?” — the full chain, cited.
Pre-meeting briefsWho, the history, what to push for.
Board-pack prepReforecast + narrative from the same numbers.
Signal triageThe flood of inbound, narrowed to the few.
Onboarding watchAt-risk accounts flagged early.
Your next moduleThe problem you name first.

What a module can be

Pre-call briefs
Who you’re meeting, the full history, what to push for — before every call.
Invoice pre-audit
Every invoice checked on arrival against every correction your team has made.
Signal triage
The daily flood of alerts and inbound, narrowed to the few worth a call.
Meeting flow
Action items assigned, contacts filed, follow-ups drafted — minutes after the call.
RFP first drafts
A first pass assembled from every answer you’ve already written.
Collections follow-up
Overdue invoices chased on schedule, with the account history in hand.
New-hire context
The first ninety days of “who do I ask?” answered from company history.
Compliance watch
Regulatory changes tracked against the contracts and policies they touch.
Your first module
The one you name in the form below. That’s where we start.

Where we sit

AI assistants and team tools are coming either way. We build the layer above them.

Over the next year, your inbox will fill with AI brains: a personal assistant for every employee, a context tool for sales, another one for support. Some of them are good. And every one of them lives inside its own slice of the company — none of them can see the whole.

What we build

The company layer: one brain that ties what sales hears to what ops schedules to what finance signs. Assistants cover one person; function tools cover one slice. The layer above them has to be built around your business — nobody sells it off the shelf. That’s the work we do.

In practice

What this looks like in production.

Three of the modules we build, with the specifics sanded off.

Signal-to-noise

Hundreds of signals a week, narrowed to the handful worth a call.

At a lot of companies, one executive’s judgment is the only filter that can spot a real opportunity in the daily flood of news alerts, industry chatter, and inbound — so reading it all stays that person’s job, forever. A brain learns that judgment: the history, the relationships, the patterns behind every signal they’ve ever flagged. It reads everything, surfaces the handful worth acting on, and drafts the outreach. The flood reads itself.

Invoice pre-audit

Every invoice checked against every correction your team has ever made.

Every accounting team knows the pattern: the same vendors miscoding and overbilling, caught two months late during the close — if they’re caught at all. With a brain, every invoice gets a pre-audit on arrival against the full history of corrections your team has ever made. Miscodings and overbillings are flagged the day the invoice lands, not sixty days later in reconciliation.

Meeting flow

The call ends. Minutes later, the follow-up work is done.

A call wraps. Within minutes the action items are assigned, the contacts are filed, the decisions are logged, and the follow-up email is drafted for review. Before the next meeting with the same people, a pre-call brief shows up with everything the company knows about them. Nobody typed any of it.

The engagement

A named win inside the first month — and it compounds from there.

  1. 01

    Connect

    We wire in your sources — meetings, email, Slack, Drive, systems of record — on infrastructure registered to you, with sensitivity screening on before the first file lands.

  2. 02

    Ship

    Together we pick the first problem — the one with a name and a number attached — and build the module that attacks it. Every module ships with its own scoreboard, so you’ll know whether it moved.

  3. 03

    Compound

    Each module after that is cheaper than the last, because the brain already knows your business. Your team’s corrections teach it. The list of solved problems gets longer every month.

Ownership

If you fire us, you keep everything.

Your brain runs on infrastructure registered to you — your accounts, your name. Every file we ingest, every module and skill we build, lives there. Think of us the way you think of a cloud provider: you built your company on AWS and GitHub, and if you left AWS tomorrow you’d still own every line of your source code. Same deal here. Walk away, and the brain, the files, the modules — all of it stays yours.

Most AI knowledge products work the other way: hosted platforms, your company’s memory on someone else’s servers, behind someone else’s login. We think the memory of your business is the last thing you should rent.

Runs in your name

Accounts, infrastructure, and access registered to you. We hold keys you can revoke.

Blocked by default

Sensitivity scanning at ingestion. Compensation, legal, and personal matters stay out unless an executive opts them in.

Never training data

Nothing your brain ingests is used to train models — not by us, not by the model providers we build on.

Leave with everything

The brain, the files, the modules, the history. Fire us and every piece stays in your accounts — yours to wire into whatever you run next.

Tell us the problem you’d want solved first.

Tell us about the workflow that costs your team the most — a name and a rough number help. We’ll think it through and give you a straight read, even if the honest answer is that a brain wouldn’t move it. Notes come to the two of us and we reply ourselves. No funnel, no SDR, no newsletter.