Your AI doesn't know
your company yet.
Most AI projects don't fail because the models aren't good enough. They fail because the model is blind to the business. We build your company knowledge base first — one organized, queryable record of how your business actually works, that your people and your agents both read from. Then we build on top of it.
Adoption is universal.
Execution isn't.
Nearly every company in the middle market is using AI in some form. Almost none of them have made it pay. MIT put a number on the gap: 95% of organizations investing in generative AI are seeing no measurable return on it4.
And the middle market is furthest behind.
Share of companies that have actually reached a scaling or fully scaled AI deployment — not a pilot, not an experiment:
Enterprises aren't better at AI. They have staff whose entire job is landing it. That is the gap we exist to close.
You wouldn't hand a brilliant new hire a complex job on their first day with access to nothing.
Don't expect a blind agent to perform any differently.
Why implementations fail
It is almost never the model. It is that the model has never seen the business.
A model with no access to your business invents the parts it cannot look up. Every wrong answer costs you more trust than ten right ones earn.
Contracts in one drive, decisions in Slack, pricing logic in a spreadsheet, the real reasoning in someone’s head. No system can reason across what it cannot see.
A new hire spends months learning how the company actually works. Agents are handed the same job on day one, with none of the same access — and then blamed for the results.
There is a pattern in where AI actually pays. Gains are largest in structured, measurable work — 14–15% in customer support, 26% in software development, 50% in marketing output — and they shrink in work requiring deeper reasoning9.
Which is the argument for a knowledge base in one line: it is how you turn what your company knows into something structured enough for AI to be good at.
A company knowledge base
An organized, queryable collection of your organization's intelligence — the factual foundation that both your employees and your AI agents work from.
Not a wiki nobody updates. A live substrate that reads from the systems your business already runs on, and answers questions in plain language.
This isn't our hunch about where to start. Across every industry and business function surveyed, the single highest reported AI usage was knowledge management in business, legal, and professional services, at 58%10.
The firms closest to this problem — the ones who sell expertise for a living — went here first.
Worth being straight about: knowledge management is one of the few functions where the survey shows cost savings but no measurable top-line lift. Its return is compounding rather than immediate — better decisions, faster onboarding, and every AI project after it starting from a company that its tools can actually read.
How we build it
Your business is more complicated than any one system captures. The work is making that complexity legible — to your people and to your agents at the same time.
Ingest
We connect the systems your business already runs on — Drive, OneDrive, Dropbox, Slack, Notion, Atlassian, Linear, GitHub, your ad accounts, your public site — and pull the knowledge out of them continuously, not once.
Structure
Everything lands in three stores at once: relational for the record of truth, vector for semantic search, and a graph that maps how themes, entities, and decisions relate. Three, because no single one answers every kind of question well.
Ask
A plain-language layer over the whole thing, serving humans and agents from the same substrate. Your team asks it questions. Your agents read from it before they act. Neither is guessing.
Then everything else gets easier
Once your AI can see the whole company, the projects that used to stall stop stalling. Each of these gets dramatically cheaper to build once the foundation is there — and is usually where the visible return shows up.
Marketing and sales is where that return tends to show up first — 67% of organizations using analytical AI there report revenue rising, the highest of any business function13. It is also where the measured output gains are largest: studies put the lift in marketing output at 50%9.
Content and paid social
Agents that already know your voice, your claims, and what you are not allowed to say — creating, publishing, and then reading performance back to decide what runs next.
One-to-one customer lifecycle
Stop sending the same email to everyone. Email, SMS, and chat that reference what this customer actually bought, asked, and cares about.
Quoting and proposals
Pricing logic, past deals, and delivery constraints in one place, so a quote takes minutes and reflects how you actually price.
Outbound and account research
Prospect analysis that starts from your own win history rather than a generic firmographic filter.
Best tool wins
We don't resell one vendor's stack. Every client has different systems, constraints, and budget, and the right answer changes accordingly — including when the right answer is something you already own.
This isn't only philosophy. In MIT's interview sample, external partnerships built on customized, learning-capable tools reached deployment about 67% of the time, against 33% for tools built in-house14. The authors call that correlation rather than proof — but the gap held across every organization they interviewed. Building everything yourself is the most common way this goes wrong.
Where we do build, we build on Reeve, our own agentic toolkit — knowledge base, model routing, memory and chat, CX agents, social and paid ads.
Among others. The list is not the point — matching the tool to your constraints is.
How an engagement runs
We talk to the people doing the work and get into the systems. The output is a ranked list of use cases scored on expected return, difficulty, and adoption risk — not a wish list. Where you land in that range is mostly your call: a business that knows what it wants and moves quickly on access has us building in about a week.
We select the tools, build the integrations, and stand up the knowledge base against your real data.
Your team learns the workflows and we write the SOPs that make them stick. This is where the project ends — with your people running it rather than us.
Arranged separately, once the project is done. Some clients keep us on to tune what the first months of real use reveal. Others take it from here, which is a perfectly good outcome.
The part nobody puts on a slide
The largest investment in any of this is not the budget. It is your organization's willingness to change how it works. Adoption is a change-management problem wearing a technology costume, and engagements that skip that part produce very impressive systems nobody uses.
Every engagement is scoped to the business and quoted custom.
We run our own companies
on what we build
MindFortress is a private AI holding company. We built Reeve, a multi-agent operating system for running a company — marketing, comms, commerce, analytics, support, and code on one substrate with shared memory — and then we used it to operate the businesses we own.
It is built the way it operates: specs go in, agent-authored pull requests come out, and every merge is gated by automated review, CI, and a human. Reeve is in early beta, onboarding its initial cohort — starting with our own companies.
That is the difference between us and a consultancy that read the same reports you did. We have made these decisions with our own money on the line, and we live with the results.
AI assistance for novelists — it reads the manuscript as it is written and catches the continuity drift no editor can hold in their head. Early paying customers.
meetfreya.comCommercial real estate intelligence, built in partnership with Bill Staniford — a repeat founder and CEO in New York real estate data.
cadasense.comWho you work with
Matt Rhodes
Founder & CEO
Matt spent seven years in private equity at Ares Management before co-founding Foundry, a multi-brand e-commerce portfolio he ran as CFO and then CEO to ~$60M in annual sales. He founded MindFortress to build the operating system he wanted as an operator — then run companies on it. Wharton, summa cum laude.
He led AI implementation from the buyer's side before he built any of this, which is why these engagements start with what the business needs rather than what the technology can do.
Private equity. Youngest VP in firm history; $1B+ deployed; sponsor lead on the Mytheresa IPO.
Co-founder. CFO for three years, then CEO — a multi-brand e-commerce portfolio run to ~$60M in annual sales, profitably.
Founder & CEO. Building Reeve and the companies that run on it.
Serial technology CEO. Built a PropTech company to $10M ARR and grew another from $0 to $1.1M ARR in six months. $9M+ raised.
10+ years engineering across global technology companies. Leads architecture and the internal development team.
Experienced AI engineer, shipping daily across a range of industries and use cases.
10+ years in organic and paid marketing across Meta, Google, TikTok, and ChatGPT.
A bench of technical and business specialists brought into engagements when the work calls for expertise we do not carry in-house.
Tell us about your business
What you have tried, where it stalled, and what you wish your team could stop doing by hand. We will tell you honestly whether this is worth doing and where we would start.
contact@mindfortress.comSources
- 191% of middle market executives say their organizations are using AI, either formally or informally. RSM US, Middle market AI trends (Published 2026-02-10). Retrieved 2026-07-27.
- 292% of executives experienced challenges implementing it. RSM US, Middle market AI trends (Published 2026-02-10). Retrieved 2026-07-27.
- 370% reported needing outside help to get the most out of their AI solutions. RSM US, Middle market AI trends (Published 2026-02-10). Retrieved 2026-07-27.
- 4MIT found that 95% of organizations investing in generative AI are seeing no measurable return on it. MIT NANDA, The GenAI Divide: State of AI in Business 2025. Linked copy hosted by mlq.ai. Retrieved 2026-08-26.
- 562% said generative AI has been harder to implement than they expected. RSM US, Middle market AI trends (Published 2026-02-10). Retrieved 2026-07-27.
- 653% of organizations that implemented generative AI believe they were only somewhat prepared to do so. RSM US, Middle market AI trends (Published 2026-02-10). Retrieved 2026-07-27.
- 7Only 30% of companies under $100M in revenue, and 27% of those between $100M and $499M, have AI in a scaling or fully scaled deployment. Stanford HAI, 2026 AI Index Report, Chapter 4: Economy (Figure 4.3.6; data: McKinsey & Company Survey, 2025). Retrieved 2026-07-27.
- 8Among enterprises above $5B in revenue, 49% have reached scaling or full deployment. Stanford HAI, 2026 AI Index Report, Chapter 4: Economy (Figure 4.3.6; data: McKinsey & Company Survey, 2025). Retrieved 2026-07-27.
- 9Productivity gains from AI are largest in structured, measurable work where outputs are easy to monitor: studies report 14% to 15% in customer support, 26% in software development, and 50% in marketing output. Gains are smaller in tasks requiring deeper reasoning. Stanford HAI, 2026 AI Index Report, Chapter 4: Economy (Chapter Highlights, item 9, p. 174). Retrieved 2026-07-27.
- 10The highest reported AI usage of any industry and function pairing was knowledge management in business, legal, and professional services, at 58%. Stanford HAI, 2026 AI Index Report, Chapter 4: Economy (Figure 4.3.3, p.194). Retrieved 2026-07-27.
- 1144% of organizations using analytical AI for knowledge management report a decrease in costs. Stanford HAI, 2026 AI Index Report, Chapter 4: Economy (Figure 4.3.4, "Cost decrease and revenue increase from analytical AI use by function, 2025"; data: McKinsey & Company Survey, 2025). Retrieved 2026-07-27.
- 1217% report analytical-AI knowledge-management cost reductions of 10% or more. Stanford HAI, 2026 AI Index Report, Chapter 4: Economy (Figure 4.3.4, "Cost decrease and revenue increase from analytical AI use by function, 2025"; data: McKinsey & Company Survey, 2025). Retrieved 2026-07-27.
- 1367% of organizations using analytical AI in marketing and sales report an increase in revenue — the highest of any business function. Stanford HAI, 2026 AI Index Report, Chapter 4: Economy (Figure 4.3.4, marketing and sales row; data: McKinsey & Company Survey, 2025). Retrieved 2026-07-27.
- 14In MIT NANDA’s interview sample, external partnerships built on learning-capable, customized tools reached deployment about 67% of the time, against about 33% for tools built in-house. MIT NANDA, The GenAI Divide: State of AI in Business 2025 (p. 19; interview sample of 52 organizations). Linked copy hosted by mlq.ai. Retrieved 2026-08-26.