MightyBot Transforms Lending with Industry's First Autonomous Agentic AI.
November 4, 2025
•
Company
"Once you see Draw Agent in action, you can’t unsee it. This is undeniably the future as it’s truly better, faster, and cheaper than the alternative." — Chase Gilbert, CEO of Built
By Stefan Fox (MightyBot) with Thomas Schlegel (Built Technologies)
Thomas leads a “new bets” team at Built, which manages a significant portion of U.S. construction lending. The problem was well understood: loan admins review draw packages with hundreds of documents, reconcile data, check compliance, and make funding decisions. It's slow, repetitive, and high‑stakes.
From day one, our shared goal wasn't a demo; it was production. Built had the data, the workflows, and real customer demand. MightyBot brought a platform for repeatable, reliable Agentic AI workflows and an operating cadence designed for speed.
The search for the right partner was extensive—I evaluated close to 20 different AI solutions. What I was looking for was specific: a small, scrappy team with forward-deployed engineering capabilities, shared incentives, and the ability to be nimble. Once I saw the AI technology landscape clearly, I realized there was a new existential threat. We could either lead this transformation or watch someone else disrupt our industry.
What struck me most about MightyBot's approach was how their platform met us exactly where our technology was. We didn't need to re-architect our entire system. Instead, we created what I call an AI exoskeleton for our existing platform—wrapping our existing infrastructure with intelligent automation. The result? We went from concept to production with customers in about three months, which would have been impossible starting from scratch.
We operated on daily sprints rather than traditional two-week cycles. Within the first week, we had a working version of the product. This wasn't just about moving fast—it was about a fundamentally different way of building software that brought joy back to the development process.
MightyBot's platform is built on policy‑driven automation—a design philosophy where AI agents execute clearly defined business rules rather than making autonomous decisions. Construction lending turned out to be an ideal application for this approach.
In real estate finance, every lender has their own policies: specific insurance requirements, permit validations, inspection protocols, and disbursement rules. Traditional approaches would either force everyone into the same rigid box or create an unmaintainable forest of if-then rules.
With MightyBot's approach, lenders define their policies in natural language, and the AI consistently enforces those policies across every draw. The agent understands and applies each lender's specific business rules—no rigid templates, no unmaintainable code branches.
This approach proved ideal for construction lending:
The MightyBot platform philosophy—that AI agents should be transparent executors of well-defined business policies rather than black boxes making autonomous decisions—turned out to be exactly what the lending industry needed.
We designed the agent for the people doing the work every day—grounded in interviews, workflow reviews, and hands‑on testing with loan admins. The result is a system of action that feels familiar, trustworthy, and helpful from the first use.
Outcome: Users felt enabled—not threatened. Early demos consistently landed as "this makes my job easier," accelerating adoption and paving the way to Assist and Full Auto.
One of the things that can be intimidating when you're going to build your first agentic product is that every company has some amount of technical debt. We created an AI exoskeleton around Built's existing platform. This insight shaped everything. Instead of forcing a rebuild, we wrapped our existing systems:
Our loop was simple and relentless:
This cadence turned into an expectation: if something wasn't right, it was fixed the next day. That rhythm—paired with production data—drove accuracy and trust.
What made this possible was MightyBot's AI‑first approach to everything. From meetings to requirements documentation to design feedback sessions, every aspect of the process leveraged AI. This wasn't just about using AI tools—it was a fundamentally different way of working that made software development enjoyable again.
What really validated this approach was seeing operations teams invite risk teams to calls—not to solve problems, but to marvel at issues the agent discovered that had never been uncovered before. These were incredibly nuanced checks buried deep in PDFs: a builder's risk policy missing the mortgagee clause on page five in tiny print, building permits with almost-but-not-quite matching addresses, duplicate line items across multiple invoices.
The reality is that humans simply don't have time to perform 100% of these checks on every draw. They're reading through hundreds of pages, and it's impossible to catch everything. The agent doesn't get tired, doesn't miss the fine print, and checks everything, every time.
While we moved fast on development, customer adoption followed a deliberate progression:
Audit Mode → Assist Mode → Full Auto
This phased approach allowed lenders to:
One of my favorite moments came with a customer who was known for having a tried and true way of doing things. This was someone with decades of experience, having managed this process manually with great success. I went in expecting a healthy dose of skepticism.
After a 15 minute demo, their reaction completely surprised me: “I’ve been driving the Honda Civic. Please give me the keys to the Ferrari.” That’s when I knew we had built something truly transformative, when a seasoned expert who has perfected their manual process becomes your biggest champion in minutes.
July–October 2025 results across design partners:
Beyond these metrics, we created an entirely new data asset. The policy-driven automation generates rich data on policy adherence, human feedback patterns, and opportunities for process improvement. This data becomes more valuable over time, enabling continuous refinement of both policies and agent performance.
When we started, our initial target was 80% accuracy—that's what we thought human-level performance looked like based on our data. We figured if we could hit 80%, we'd be saving significant time even with some errors to correct.
The reality blew us away: we're at 99%+ on everything, all the time! Not only is the agent more accurate than human reviewers, it's doing a more exhaustive review in a fraction of the time. It's checking things that humans simply don't have bandwidth to verify on every single draw.
What I appreciated most about MightyBot was finding a partner who could go deep and fast while keeping our options open. The platform approach gave us both—immediate value on draw processing while maintaining flexibility for future use cases. This isn't a one-trick pony; it's infrastructure we can build on for years to come.
The Draw Agent is a workflow running on the MightyBot platform. That matters because the next use cases don't require re‑inventing the stack. The same primitives—tools registry, data/indexing, workflow orchestration, evaluation, steerability—power future use cases.
If you're evaluating your first agentic product and wrestling with current infrastructure, our biggest lesson is simple: you don't need to re‑architect to get value. Start where you are. Wrap the core workflows with an AI exoskeleton. Ship, learn, and iterate—but always with your customers' trust at the center.
— Stefan & Thomas
