Ryan Anderson, CEO of Filevine, shared their AI transformation playbook at SaaStr AI London. Here’s the thing: their new AI revenue now exceeds their SaaS revenue on a quarter-over-quarter basis. This is the roadmap.
The Filevine Story: 10 Years of Grinding, Then AI Changed Everything
Ryan Anderson didn’t set out to build a $3 billion legal tech company. He set out to stop waking up at 3am in a cold sweat.
As a young trial lawyer in the early 2010s, Anderson was drowning. Deadlines piled up. Assignments disappeared. He’d lie awake convinced he’d missed something critical. “I’m not a naturally organized individual,” he’s said. “I’m naturally anxious.”
So in 2014, he started building. First a Google spreadsheet — his “PI checklist” — at the law firm he’d founded with Nate Morris. Then a meeting over lunch in Las Vegas with Jim Blake, an engineer who asked the right questions: What’s breaking? Why is it so hard to keep track of work?
That conversation became Filevine.
For the next decade, they ground it out. Started with personal injury firms. Expanded into every legal practice area. Grew from task management to a full legal operating system: document management, demand generation, analytics, the whole lifecycle. By 2022, they’d raised $108M in a Series D — one of the largest legal tech investments ever at the time.
Good company. Solid growth. But not a rocketship.
Then AI happened.
In September 2025, Filevine announced a $400M raise at a $3 billion valuation. The round was led by Insight Partners, Accel, and Ryan Smith’s Halo Fund. Smith — the Qualtrics billionaire and Utah Jazz owner — had been trying to invest for years. Anderson kept saying no. But after Filevine’s strongest quarter in company history, Smith called again: “You’re not getting your due.”
What changed? AI revenue is now growing 130% year-over-year. Their AI chat product is growing 20%+ week over week. And as Anderson shared at SaaStr, their new AI revenue now exceeds their SaaS revenue on a quarter-over-quarter basis.
Today: $200M+ ARR, growing 50-60%, 6,000 customers, 700 employees, 96% GRR, 124% NRR.
This is what it looks like when a decade of building the system of record meets the AI moment.
Top 5 Takeaways
- “Sprinkling AI on top” is fundamentally wrong. You can’t just connect to OpenAI’s APIs and call it an AI product. That won’t cut it in 2026. You have to change your architecture.
- Nothing is sacred. You will have to tear down meaningful components of working, revenue-generating code. Use the 4-quadrant framework: map every system against “competitive advantage” and “speed.”
- Your SaaS is the closet, not the clothes. AI agents need context (your system of record), not just documents. This is your moat against AI-only competitors.
- Protect your data and price to dominate. Move from open APIs to personal access tokens. Your high SaaS gross margins let you undercut AI-only competitors on blended margins. Be savage.
- Obsess over usage, not revenue. No AI product goes beyond beta without audit trail logging. If customers aren’t using it, it doesn’t matter.
The Wake-Up Call: “We Get to Sprinkle AI on Top”
Ryan opened with a story that will sound familiar to many B2B and SaaS leaders:
“I had an engineer say to me just a few months ago with a ton of pride, mind you: ‘We have built an incredible SaaS application that makes tons of money, grows fast, customers never leave it. We have almost 96% gross revenue retention, 124% net revenue retention.’ He has every reason to be prideful. And he said, ‘The great news is now we get to sprinkle AI on top.'”
Ryan’s response? That is fundamentally incorrect.
Connecting to OpenAI’s APIs isn’t going to cut it in 2026. To be AI-native, you have to change the architecture of your system. It has to flip.
The Proof Is in the Numbers
The transformation is real and measurable. As Anderson put it at SaaStr AI London:
“It is very plain to see that the numbers back up that we are now doing far more revenue on a new quarter-by-quarter basis in AI products than in our SaaS product. Now, that’s not to say that the SaaS product is in any way less successful — in fact it’s still growing at 35-40% year-over-year. We are just growing so much faster on the AI side of the house.”
This isn’t a pivot away from SaaS. It’s SaaS + AI compounding together.
Framework #1: The “Nothing Is Sacred” 4-Quadrant Matrix
The hardest part of going AI-native? Telling your teams that some of what they’ve built — things that work, that make money — has to be torn down.
Ryan introduced a simple 2×2 matrix:
Y-Axis: Critical to competitive advantage → Not critical to competitive advantage
X-Axis: Keeps you moving fast → Slows you down
The Four Quadrants:
Upper Right (Keep & Fortify): Critical to your moat AND keeps you fast. This is the cornerstone of your AI-native movement. Don’t tear it down — make it better.
Bottom Left (Tear Down): Not critical to your moat AND slows you down. This is logically easy but emotionally brutal. These have to go.
Upper Left & Bottom Right (Judgment Calls): More nuanced. Evaluate coldly, not based on feelings.
The key insight: Someone who worked 5 years building a microservices architecture that doesn’t serve your AI needs will fight to keep it. You’ll have to be disagreeable as a CEO or technical leader making these calls.
Framework #2: Content to Context (The Clueless Analogy)
Here’s Ryan’s memorable way of explaining why “SaaS is dead” is wrong:
“Imagine I came to you and said, ‘Hey, good news. We have an AI agent that can pick out your outfits in the morning.’ You’d be like, ‘Awesome. Done. Sign me up.’ In fact, Cher in Clueless already had this.
But if you then said, ‘Oh, by the way, now that you have this AI agent, you don’t get to have your closet anymore. We’re not going to show you your closet. You can’t see it. It’s just a bunch of unstructured data and clothes and a mess.’
You’d be like, ‘Hold on. I would actually like to have my closet AND the AI agent. Can I have both?'”
Your SaaS application is Cher’s closet. The agent helps take action based on the content inside the closet.
This is why SaaS companies have a significant advantage over AI-only competitors. You have:
- The system of record
- Audit trails (who did what)
- User identity data
- Deadlines and calendars
- Contact information
- Structured workflows
To answer a simple question like “What should I do next on this case?” — you need ALL of this context. Documents alone give you an incomplete answer. And in most domains, an incomplete answer is actually worse than an inaccurate answer because the customer doesn’t know what they didn’t see.
The Architectural Flip: AI Data Layer
The old architecture: AI layer sits on top of your core services, AWS, data, codebase, calendar, etc.
The new architecture: AI Data Layer sits RIGHT NEXT TO the AI Application Layer.
Why? Because your ML engineers need to tune and change how data flows into AI applications on nearly a daily basis. They can’t be going to your traditional tech team asking “Hey, can you please change how the API provides me this data?”
The AI Data Layer owns:
- How information is prepared for AI
- How you ingest, process documents, emails, messages, and events
- The graph structure of your domain (for legal: people, events, claims, outcomes)
When ML teams own this data layer, Ryan says the results are “dramatically better, dramatically more reliable, higher context, more complete, more accurate.”
This layer powers core AI applications:
- Co-pilot
- Search (semantic + traditional, without forcing users to choose)
- Summarization
- Recommendations
- Reporting
Hiring AI Natives: The Data & Distribution Pitch
Here’s the problem: AI natives don’t want to work for “old SaaS companies.” They want to work for AI companies.
Here’s the good news: The best AI natives actually want to work where they have more access to data.
Ryan’s pitch to AI talent:
- Data Access: “In legal tech, there are hundreds of competitors saying ‘give us your documents and we’ll run AI on them.’ But we know that to actually answer a legal question, you need way more than documents. You need audit trails, user identity, deadlines, calendars, conflict checks, contact information. We have ALL of that.”
- Distribution: Show them what happens when you ship to an existing customer base. Filevine launched a product that went from 5-10 users/day to hundreds of users/day in just a few months. “Your AI team will love building products that absolutely rip because you have distribution and data.”
The Acquisition Option
Ryan’s confession: “We had an ML team. It was fledgling. Now we simply bought a company.”
Filevine acquired Parrot, an AI-native company, and merged the teams. AI natives want to work next to other AI natives. Acquiring gives you critical mass fast.
Rebrand With Intent
Filevine changed their logo from a military/legal vibe to something bolder, “up and to the right.”
But the real audience wasn’t customers — it was internal.
“This change in our mark has told the people who work at Filevine every single day: the old mark is from a traditional SaaS era and the new one is from the AI era. It is highly symbolic. You should have no problem telling your team ‘we are moving’ — and you need to give them a symbolic thing to look at for that change.”
They also created a new category: LOIS — Legal Operating Intelligence System. Not SaaS. Not AI. A blended category.
Obsess Over Usage
“We do not let our teams roll out applications beyond beta without audit trail logging to know exactly who’s doing what.”
Filevine’s real numbers:
- AI Fields product: 150 million actions in just a few months
- Docker View product: Growing extremely quickly
- Chat with your case (co-pilot): Their blockbuster product, growing 10% week over week in usage
The pattern they watch: A customer uses it 5 times, then 8 times the next day, then 20 times. Now they have customers using it 2,000 times a day.
This is how you know if your AI product is any good: Are customers using it?
Leverage Your Data: The API Negotiation
AI-only competitors will come demanding your data “like it’s their moral right”:
“I can’t believe you have all this data and you won’t give it to me for free whenever I want it. It’s your customer’s data. How could you possibly be acting this way?”
Ryan’s response:
- Control your APIs. Filevine moved from open API access to personal access tokens. They know exactly who’s accessing, what they’re doing, and how often.
- Review every request. They’ve never said “no” to a competitor, but they always say “let’s have a conversation about that.”
- Flip the script.
“When they demand access, say ‘Okay sure. But of course it goes both ways, correct? We can take the AI outputs you get from our data and we’ll get them right back into our system. Correct? Isn’t that how it’ll work?’ All of a sudden, the shoe doesn’t feel so good when it’s on the other foot.”
- Watch the API traffic. It reveals promising areas for new development. You’ll see which products are gaining traction. Copy those products and build them into your system. You have the right to do this.
Price to Dominate
Your SaaS application likely has very high gross margins (Filevine: ~80%). Your AI-only competitors struggle with margins badly because of LLM costs.
This means you can sell AI products at a lower price point than competitors.
Why? Blended gross margins. Even if your AI gross margin drops to 30-40%, your blended margin might go from 80% to 60%. That’s still way better than an AI-only competitor whose margin is driven down to 10%.
“Your investors might say ‘Why are we selling cheaper than AI-only competitors?’ Your answer is: because we’re gaining market share. And our blended gross is still higher than their blended gross.”
Be savage on pricing. The AI-only competitors will cede you no ground.
Build One Product, Sell to AI Customers Only
The boldest move Filevine made:
“We no longer sell to customers who won’t buy the AI products.”
Why?
- Architecture simplicity: If you assume AI is implicit in everything you build, you don’t have to maintain two paths.
- Team morale: How do you tell your SaaS engineers “You work on the old stuff while the ML team works on the cool AI stuff”? That doesn’t work. One company, one product.
- Customer quality: “Show me the lawyer that doesn’t want to use AI and I will show you the lawyer that’s about to get his butt kicked.”
They’re also moving from subscription/user-based pricing to usage-based pricing (per “matter” or project). More revenue from usage-based customers than traditional subscription customers.
The 5 Biggest Mistakes SaaS Companies Make Going AI-Native
- “Sprinkling AI on top” — Connecting to APIs without architectural change doesn’t make you AI-native. The AI data layer needs to sit next to the AI application layer, owned by ML engineers who can tune it daily.
- Being too agreeable — You have to be disagreeable as a CEO when telling teams their working code has to go. Evaluate what to tear down logically and coldly, not based on somebody’s feelings.
- Thinking documents are enough — AI-only competitors claim they just need your documents. Wrong. Documents alone give incomplete answers, and incomplete is worse than inaccurate because customers don’t know what they didn’t see.
- Giving away API access freely — Move to personal access tokens. Review every request. Watch the traffic to see which AI products are gaining traction — then copy them and build them into your system.
- Maintaining two products (SaaS + AI) — Build one product. Sell to customers who will come with you on the AI journey. If they won’t buy AI, they’re not worth selling to.
The Bottom Line
Filevine’s story is proof that SaaS companies can win the AI transition — but only if they treat it as a true transformation, not an enhancement.
The companies that succeed will:
- Tear down what doesn’t serve AI, even if it’s working
- Shift from content systems to context systems
- Flip their architecture so ML teams own the AI data layer
- Acquire AI talent fast (even through M&A)
- Obsess over usage, not just revenue
- Protect their data advantage
- Price aggressively using blended margins
- Sell one integrated product to AI-ready customers
As Ryan put it: “At the end of the day, it has always just been about a customer with a problem. That’s what animates us. Can you solve my problem? We can solve it with technology.”
The question isn’t whether you’re a SaaS company or an AI company anymore. It’s whether you can solve customer problems better than anyone else — using everything you’ve built plus everything AI enables.
That’s the new game.
