What kind of stakes do you take?

Majority and significant minority stakes in established, lower middle-market agentic AI companies. We structure each partnership around what the management team and the business need. We have no rigid ownership target that would block a company we believe in.

Do we need revenue to partner with Fifth Turn Capital?

Yes — we partner with established, revenue-generating agentic AI companies. We're a private equity firm that acquires and grows real businesses, not a pre-revenue backer.

Do we need a large team?

No. We partner with lean, focused teams regularly. We do want to understand the leadership, the next two hires, and why.

How long does your diligence take?

We move with conviction once we know a business. We've moved faster when a process is competitive. We will never string a team along — if it's a no, you'll know why and hear it directly.

What if our product is technically simple — basically a thin layer over GPT?

If the workflow you own is real and the customers are paying, we don't care how thin the layer is. The moat in vertical agents is not the model — it's the workflow integration, the proprietary data, and the customer relationship. "Just a wrapper" companies have built hundreds of millions in enterprise value before.

What We Look for in an Agentic AI Company

Investing in agentic AI is harder than it looks. The technology moves so fast that any thesis you have about a specific architecture or model capability is wrong within six months. The competitive landscape resets every quarter as model labs release new capabilities that vaporize entire product categories overnight. So how do we underwrite a company when the ground is moving under everyone's feet?

We've reviewed hundreds of businesses over the last twelve months. The pattern that separates the companies we partner with from the ones we pass has surprisingly little to do with technical depth. Three things matter more.

1. Workflow obsession

The best agentic AI companies are not run by people who can recite the latest model benchmarks. They are led by teams who have spent years inside a specific industry, watching the same broken workflow play out thousands of times, and have decided they will not tolerate it for one more year. Their conviction is anchored in pain, not in technology. When the model layer changes, their thesis doesn't.

The clearest tell: ask a management team to describe a single customer's day, hour by hour. Workflow-obsessed operators give you the entire shape of the day in five minutes — who calls whom, where the spreadsheet lives, what breaks at 4pm on a Friday. Technology-first teams give you the model architecture and ask you what industry you'd like them to apply it to.

2. Iteration speed

Agentic systems are messy. The first version of any agent fails in surprising ways, and the only path forward is to ship, watch users, and ship again — sometimes twice a day. We partner with teams who treat product development as a continuous experiment, not a quarterly roadmap. Teams that build a beautiful prototype and then disappear for three months to perfect it almost always lose to scrappier operators shipping ugly versions weekly.

What we measure on iteration speed

•Cycle time from customer feedback to shipped fix — the best teams are under 48 hours.
•Number of production deploys in the past 30 days — we want to see double digits, not zero.
•Whether the team watches its own product being used (recordings, logs, sit-alongs).
•Willingness to kill a feature that didn't work, in public, the same week.

3. Commercial maturity

AI companies frequently underprice themselves and overinvest in product polish. We look for businesses that have been charging from day one, even if it started at $500 a month for a half-broken product. The willingness to ask a customer for money — and to listen carefully when they say no — is one of the most reliable predictors of long-term commercial success we've found.

Free pilots are a leading indicator that a team is afraid of the answer. Real customers signal real demand by reaching for their card. If a prospect won't pay $500 to try, they won't pay $5,000 to keep using it. The best businesses learned that in week one, not month nine.

What we don't need

We also pay attention to what we don't need.

•We don't need a perfect story. A working product and real paying customers is enough.
•We don't need an enterprise pipeline. We'd often rather see a base of SMB customers than one logo.
•We don't need a Stanford PhD or an OpenAI alumni badge. Pedigree is uncorrelated with our best returns.
•We don't need a fully built-out org chart. A clear-eyed lean team that knows its next two hires beats a bloated team with no conviction.
•We don't need a polished pitch. A live product and honest numbers work.

What we need is a management team that knows its workflow cold, that has shipped something real, and that has established, paying customers with room to grow.

Our process

First conversation within five business days. We read every inbound note.
A working session within a couple of weeks. We meet the leadership, talk to a customer, and walk through the product live.
A clear decision. Yes is yes; no is a real explanation.
A partnership structured around what the company needs to keep growing.

If that's your company, we move with conviction. No layers of committee theatre. We meet, we diligence, we partner. That's it.