A year ago, WARE was three people in a co-working space with an idea and a prototype. Today, we're processing thousands of tenant conversations monthly across a dozen properties, and trying very hard not to screw it up.

This is our year-in-review: the wins, the embarrassments, and the lessons we're carrying into 2025.

By the Numbers

Let's start with the metrics that matter:

  • Conversations processed: 47,000+ tenant inquiries handled by our Leasing Logic Engine
  • Average response time: 42 seconds (down from 4+ hours for email-based handling)
  • Accuracy rate: 95.2% on routine inquiries, up from 81% at launch
  • Properties served: 14 multifamily communities across Texas
  • Team size: 3 → 7 people

Numbers tell part of the story. The more interesting stuff is everything that happened around them.

What We Got Right

Going Deep Instead of Wide

Early on, we had to make a choice: build a general-purpose AI chatbot that could work for any industry, or build something specifically for real estate leasing.

We chose depth over breadth. Every feature we've built is optimized for the specific challenges of tenant communication—pet policies, fair housing compliance, lease terms, property amenities. A generic chatbot couldn't handle the "pitbull problem" we wrote about earlier. Our system can.

This focus cost us some potential customers who wanted broader automation. But the customers we do have trust us deeply because our tool does exactly what they need.

Partnering Instead of Selling

We don't think of our early customers as customers. We think of them as partners. They give us access to real conversations, real edge cases, and real feedback. We give them free pilots and dedicated support.

Three of our first five customers became case studies. Two refer us to everyone they meet. That's not because we're great at sales—we're not—but because we treated them like collaborators from day one.

Shipping Before Perfect

Our V1 product had problems. Response accuracy was too low. The tone was sometimes robotic. Edge cases weren't handled gracefully.

We shipped it anyway. Not because we didn't care about quality, but because real-world feedback was worth more than another month of internal testing. Every problem we found in production taught us something that simulation couldn't.

V2, launched in January, was vastly better because V1 showed us exactly where to focus.

What We Got Wrong

Underestimating Compliance Complexity

Fair Housing. ADA. State-specific landlord-tenant laws. ESA vs. service animal distinctions. Breed-specific legislation.

We knew compliance was important. We didn't realize how complicated it was. Our early engine gave technically-correct-but-contextually-dangerous answers more often than we'd like to admit.

It took us three months to build proper compliance guardrails—work we should have done before launch, not after.

Ignoring the Integration Burden

We built great AI, but we underinvested in connecting it to property management systems. Our first customers had to manually import property data. One had to copy-paste conversation logs. It was embarrassing.

We've since built integrations with AppFolio, Yardi, and RealPage. But we learned the hard way that AI without integrations is a demo, not a product.

Hiring Too Slow

For most of 2024, we were understaffed. Three people can build a prototype. Three people cannot simultaneously build product, support customers, handle sales, and maintain sanity.

We hired four people in Q4. We should have started six months earlier. The lesson: hire ahead of need, not behind it.

What We Learned

Beyond the tactical wins and losses, a few bigger lessons emerged:

Speed to first response matters more than we thought. Our data shows inquiry-to-lease conversion drops 50% for every hour of delay. That's not intuitive—you'd think a thoughtful response would beat a fast one. But in a market where renters are inquiring at multiple properties, first-mover advantage is decisive.

Property managers are desperate for help. We expected skepticism about AI. Instead, we found exhausted leasing teams who can't keep up with inquiry volume, turnover, and administrative burden. They don't want a robot—they want relief.

Trust is earned at the edge. Customers don't judge you on the easy cases. They judge you on the hard ones—the angry tenant, the compliance gray area, the 11pm inquiry. Every edge case we handle well builds more trust than ten routine conversations.

What's Next

2025 is about scaling what works:

  • Geographic expansion: Moving beyond Texas to Colorado, Arizona, and eventually nationally
  • Voice integration: Our AI answering phone calls, not just texts and chats
  • Proactive outreach: Following up with leads automatically instead of waiting for them to reach out
  • Deeper integrations: Making setup take hours instead of weeks

We're also thinking about what's beyond leasing. Maintenance requests? Rent collection? Renewal negotiations? The same AI that handles tenant inquiries could handle a lot more. But that's a 2026 conversation.

Thank You

To our customers who trusted a three-person startup with their tenant communications: thank you. Your patience, feedback, and referrals built this company.

To our team: thank you for betting on this when it was just an idea. Watching it become real has been the privilege of my career.

2025, let's go.

— Bryan Thorne, Founder

Want to be part of what's next? Get in touch.