MightyBot Transforms Lending with Industry's First Autonomous Agentic AI.
December 30, 2025
•
Company
.png)
In November 2025, CNBC reported that Built, a fintech unicorn, had launched an AI agent to handle billions of dollars in commercial real estate lending. That agent was Draw Agent. That platform was MightyBot.
The headline captured a moment, but behind it lies a year of relentless engineering: 304 releases, over one million lines of code, and a team of 10 engineers who believed that AI agents could do more than demo well—they could run mission-critical financial operations with 99%+ accuracy.
This is the story of how we proved that on-policy automation works.
MightyBot exists because of a simple observation: enterprises don't need AI that improvises—they need AI that executes policy with precision.
Financial institutions have rules. Compliance requirements. Standard operating procedures. For decades, these policies lived in documents, enforced by humans who read them, interpreted them, and applied them—inconsistently, slowly, and expensively.
We asked: what if we could turn those policies into living, executable agents?
2025 was the year we answered that question. We built the infrastructure that transforms institutional knowledge into autonomous execution. We created the feedback loops that make agents learn from every decision. We shipped the tools that let domain experts—not just engineers—define, test, and deploy policy-driven automation.
Our platform isn't a chatbot, a copilot, or an LLM wrapper. It's infrastructure for automating decision-making processes that currently require humans to apply business rules to messy data.
Business users define policies in natural language, not code. The engine handles versioning, priority ordering, and dependencies between policies. Changes deploy without engineering involvement.
We processed thousands of policy-related updates in 2025, building a framework that transforms business rules into executable agent logic—including templatized policy definitions, automated policy creation from feedback patterns, and AI-generated validation prompts. The policy framework now powers both our lending and payments products, proving that policy-driven automation generalizes across domains.
The platform ingests messy inputs: PDFs, spreadsheets, CSVs, images, and event streams. It classifies documents, extracts structured data, and assigns confidence scores to every extraction. Low-confidence extractions route automatically to human review—no manual triage required.
This pipeline is integrated, not assembled from separate services. You don't connect a document AI API to an orchestration layer to a policy engine. It's one system.
Our agents run on webhooks or schedules, not user prompts. When new data arrives, the agent wakes up, runs the full workflow, and takes action—no human initiation required.
This is how our production deployments work. A request arrives, the agent processes it end-to-end in minutes, and outputs a decision with supporting evidence.
Three modes let customers start conservative and increase autonomy as they build confidence:
The platform tracks edit distance—how much humans change agent outputs—so you can measure when you're ready to move between modes.
Perhaps our most significant architectural achievement: a closed-loop system that makes our agents smarter with every execution.
Here's how it works: agents execute → humans review edge cases → feedback generates policy suggestions → approved suggestions update agent behavior → agents execute better.
We shipped 60 feedback-related updates in 2025, including a comprehensive dashboard for reviewing agent decisions, automated evaluation of agent outputs, the ability to replay historical decisions against updated policies, and human-in-the-loop refinement workflows.
This isn't just machine learning—it's on-policy learning where human expertise continuously shapes agent behavior.
The Workflow Builder represents our bet on democratizing agent development. Domain experts—not just engineers—should be able to create, test, and deploy policy-driven agents.
We shipped the UI foundation, end-to-end workflow demos, Git-based version control for workflows, AI-assisted policy creation, and comprehensive monitoring dashboards. The Workflow Builder integrates with our feedback system: feedback from production agents generates policy suggestions that can be approved and deployed as new workflow steps—all without writing code.
Draw Agent launched publicly in November 2025. Construction loan draws require reviewing invoices, lien waivers, inspection reports, and contractor applications against approved budgets and compliance requirements. A single draw can involve dozens of documents and hundreds of validation rules. Lenders spent hours on each draw, creating bottlenecks that delayed funding to borrowers.
Our solution: an AI agent that ingests documents from any source, extracts structured data, validates against 50+ configurable compliance rules, generates audit-ready reports with evidence trails, and learns from human feedback to improve accuracy.
The results speak for themselves:
Draw Agent represented our largest engineering investment of the year, evolving from basic budget checks in February through SOP-based validation, multi-source document validation, performance optimization, caching, and AI-powered anomaly detection.
RocketFee applies the same on-policy architecture to a different domain: analyzing merchant payment statements to extract fee structures, identify discrepancies, and classify transactions.
In 2025, we delivered tiered statement type support, program and card type matching, custom rate handling, line item editing with drag-and-drop, and flat rate PDF processing. RocketFee shares the same feedback infrastructure as Draw Agent, proving that our platform generalizes. The same dashboard that helps lenders review draw decisions helps payment analysts review fee classifications.
MightyBot achieved SOC 2 Type II certification in 2025, validating our security, confidentiality, and availability controls. We implemented hybrid authentication supporting enterprise friendly authentications, role-based access control for enterprise customers and comprehensive input validation and injection prevention.
The thesis is proven. Policy-driven agentic AI works for mission-critical finance.
Our 2026 priorities include general availability of the Workflow Builder for all customers, expanding the platform beyond lending into new domains, automated policy deployment from approved feedback suggestions, comprehensive test automation, and multi-region deployment for enterprise requirements.
2025 was the year MightyBot proved that agentic AI isn't just a demo—it's production infrastructure for mission-critical operations.
We built a platform where policies become agents, agents learn from feedback, domain experts create automation without code, and enterprise requirements are met with SOC 2 certification and hybrid deployment.
Draw Agent and RocketFee are proof points for on-policy automation. The same architecture, the same feedback loops, the same platform.
The accuracy is measured: 99%+. The ROI is proven: 300-500%.
But the real achievement is what those numbers enable: AI agents that financial institutions trust with billions of dollars in lending decisions.
That's what we built in 2025. That's why 2026 will be even bigger.
