MightyBot Transforms Lending with Industry's First Autonomous Agentic AI.
February 25, 2026
•
AI Thinking
AI agents are transforming construction lending by automating the document-heavy draw request process that has bottlenecked financial institutions for decades. Built Technologies partnered with MightyBot to deploy the industry's first autonomous AI agent for construction loan administration, achieving 99%+ accuracy, 95% time reduction on draw reviews, and 10x increase in loan administrator throughput across 200+ financial institutions.
Construction lending is one of the most complex corners of financial services. Every construction loan generates dozens of draw requests over its lifetime. Each draw is a mini-investigation: lien waivers from every subcontractor, inspection reports, AIA payment applications, insurance certificates, change orders, and compliance documents — all arriving as multi-page PDF packets that must be validated against lender-specific policies before funds are released.
Before AI, this process was entirely manual. A single draw review took 90 minutes on average. Loan administrators toggled between documents, spreadsheets, and loan management systems, checking each line item against lending policies, calculating retainage, verifying insurance coverage dates, and cross-referencing contractor information. The work was repetitive but high-stakes — a missed lien waiver could expose a lender to millions in liability.
Draw requests are uniquely challenging for automation. Unlike standardized invoices or purchase orders, construction draw packages are multi-document packets with variable formats. A single draw might include 15-30 pages spanning 5-8 distinct document types, each requiring different validation logic.
The documents themselves are messy. Lien waivers come in conditional and unconditional forms with state-specific legal requirements. AIA G702/G703 forms contain nested line items with mathematical relationships (original contract + change orders = current contract value). Inspection reports mix narrative observations with percentage-complete assessments. Insurance certificates require cross-referencing policy numbers, coverage amounts, and expiration dates against loan requirements.
Traditional automation approaches — RPA, basic OCR, rule-based workflows — fail here. RPA breaks when document layouts change. Basic OCR cannot distinguish between a conditional and unconditional lien waiver or interpret handwritten inspection notes. Rule-based systems cannot handle the judgment calls that loan administrators make dozens of times per review.
This is exactly the kind of problem policy-driven AI was designed to solve.
When Built Technologies and MightyBot designed Draw Agent, they rejected the common approach of replacing existing systems. Instead, they built what Thomas Schlegel, Built's VP of Engineering, calls an "AI exoskeleton" — intelligent automation that wraps around Built's existing loan management platform without requiring re-architecture.
The exoskeleton pattern works because it respects enterprise reality. Built's platform already handles loan creation, draw submissions, funding workflows, and lender integrations for 200+ financial institutions. Ripping out and replacing that infrastructure was not an option. Instead, Draw Agent consumes Built's existing APIs, processes draw documents through MightyBot's intelligence layer, and writes results back through the same interfaces.
This architecture delivered two critical advantages: deployment speed (weeks instead of months) and reversibility (the system could be turned off at any point without disrupting core operations). For a company handling billions in construction loan value, that risk profile was essential.
At the core of Draw Agent is MightyBot's document intelligence pipeline — a multi-stage system purpose-built for the complexity of construction lending documents.
Every page in a draw package is classified independently with confidence scores. A 25-page draw PDF might contain pages 1-3 as an AIA G702 application, pages 4-12 as a continuation sheet (G703), pages 13-18 as conditional lien waivers from three subcontractors, page 19 as an inspection report, and pages 20-25 as insurance certificates. The classifier handles mixed-format documents, rotated pages, and poor scan quality.
Each classified page routes to extraction logic tailored to its document type. AIA forms require understanding the mathematical hierarchy: original contract value, approved change orders, work completed to date, retainage, and current payment due. Lien waivers require identifying the waiver type (conditional vs. unconditional), the claimant, the amount, and the through-date. Insurance certificates require extracting policy numbers, coverage types, limits, and expiration dates.
Extracted fields are normalized to a canonical schema that eliminates naming inconsistency. A contractor might appear as "Metro Plumbing LLC" on the lien waiver, "Metro Plumbing" on the AIA form, and "Metro Plumb." on the insurance certificate. The normalization layer resolves these to a single entity, linking every reference back to its source page and character position for auditability.
Cross-document reconciliation then validates relationships: Does the lien waiver amount match the corresponding AIA line item? Does the insurance certificate cover the contractor listed on the draw? Is the inspection report date within the required window? Every cross-reference check produces an evidence pointer linking the finding to its source data.
With structured data extracted and normalized, Draw Agent applies the lender's specific policies. Construction lending policies vary significantly between institutions — what one lender requires as documentation, another may waive under certain conditions.
Policies are encoded in plain English and converted to executable logic. Examples from production:
Each policy evaluation produces a deterministic result: pass, fail, or insufficient data. There are no probability scores or "likely compliant" results. When a policy check fails, the system identifies exactly what is missing or non-compliant, which document was checked, and what the lender's specific requirement states. This precision is what gives loan administrators confidence to act on the agent's findings.
Draw Agent did not launch as a fully autonomous system. Built and MightyBot followed a progressive automation path that earned trust through demonstrated accuracy.
Phase 1 — Audit mode: Draw Agent processed every draw request and generated findings, but loan administrators reviewed and approved every decision. This phase validated accuracy against human judgment and identified edge cases the policy library needed to address. The team ran daily engineering sprints — not two-week cycles — with subject matter experts recording feedback via video and engineers shipping fixes overnight.
Phase 2 — Assist mode: Routine draws (complete documentation, no policy violations, below complexity thresholds) were processed with minimal human oversight. Exceptions — missing documents, policy violations, unusual amounts — were flagged for human review. This phase proved that the system could reliably distinguish routine from exceptional cases.
Phase 3 — Full autonomy: For qualifying workflows, Draw Agent operates end-to-end. Documents are classified, extracted, validated, and reconciled without human intervention. The why-trail records every decision. Loan administrators focus on exceptions and complex cases rather than routine processing.
This progressive path was critical for adoption. Financial institutions managing billions in loan value will not flip a switch to full automation on day one. But when they can observe the AI's accuracy across thousands of draws in audit mode, the case for increased autonomy builds itself.
Draw Agent's production metrics demonstrate what policy-driven AI delivers when deployed in mission-critical financial workflows.
| Metric | Before AI | After Draw Agent | Improvement |
|---|---|---|---|
| Average draw review time | 90 minutes | 3 minutes | 95% reduction |
| Document processing accuracy | Human baseline | 99%+ | Exceeds manual review |
| Loan administrator throughput | Baseline | 10x increase | 10x more draws per person |
| Risk issues detected | Baseline | 400% more | 4x better risk coverage |
| Funding speed to borrowers | Baseline | 30-60% faster | Days saved per draw |
| Manual interaction reduction | Baseline | 80% fewer steps | Routine work eliminated |
The 400% increase in risk issues detected is particularly significant. Human reviewers under time pressure miss things — especially in high-volume periods. Draw Agent checks every policy against every document in every draw, every time, without fatigue or shortcuts. It found compliance issues that human reviewers had been consistently overlooking.
These metrics come from production deployments across Built's customer base of 200+ financial institutions managing over $100 billion in construction loan value. They are not demo metrics or pilot results.
Construction lending is a template for how AI agents will transform regulated industries. The pattern applies wherever document-heavy workflows, complex policies, and auditability requirements converge.
The key lessons from Built's deployment transfer directly to other regulated domains: insurance claims processing, mortgage underwriting, trade finance, and compliance review all share the same fundamental structure — complex document packets that must be validated against specific policies with full audit trails.
What makes this deployment different from the AI pilots that Gartner predicts 40% will cancel by 2027 is the architecture. Policy-driven AI provides the governance layer that turns an impressive demo into a production system financial institutions trust with real money.
Draw Agent builds on MightyBot's core platform capabilities, each purpose-built for regulated financial workflows.
LLMCompiler execution engine: Unlike sequential reasoning loops used by most AI agent platforms, MightyBot compiles goals into parallel execution plans. A draw with 25 pages and 8 document types is processed concurrently, not sequentially — dramatically reducing processing time while maintaining deterministic behavior.
Multi-agent orchestration: Specialist agents handle different aspects of the workflow. A Gatekeeper Agent manages ingestion and classification. A Reconciliation Agent handles policy evaluation and cross-document checks. This division of labor reduces errors and makes the system easier to audit and govern.
Why-trail auditing: Every finding links to its source evidence — the specific page, the specific field, the specific policy version. Regulators and auditors can verify any decision by following the evidence chain from outcome to source document. Learn how the policy agent eliminates hallucinations.
Megastore search layer: Every processed draw becomes a searchable, structured record at three levels of granularity — the full document package, individual pages and sections, and normalized entities that span across documents. This means a reviewer can search for a specific contractor and instantly see every lien waiver, insurance certificate, and payment record associated with them across all draws.
What is AI construction lending?
AI construction lending uses artificial intelligence agents to automate the document-heavy processes in construction loan administration — particularly draw request review, which involves validating multi-document packets against lender-specific policies. MightyBot's Draw Agent is the first autonomous AI system deployed for this workflow at scale.
How does AI process construction lending documents?
AI processes construction documents through a multi-stage pipeline: page-by-page classification, type-specific extraction (tailored to lien waivers, AIA forms, inspection reports, etc.), field normalization to resolve entity variations, and cross-document reconciliation to validate relationships between documents.
What accuracy does AI achieve in draw request processing?
MightyBot's Draw Agent achieves 99%+ accuracy in production across thousands of construction loan draw requests, exceeding human baseline accuracy. The system also detects 400% more risk issues than manual review because it checks every policy against every document without fatigue.
How long does it take to deploy AI for construction lending?
Built Technologies and MightyBot went from concept to production deployment in approximately three months using daily engineering sprints and MightyBot's existing platform infrastructure. The progressive automation approach (audit, assist, full auto) allows lenders to start seeing value immediately while building trust over time.
Can AI handle the complexity of construction loan documents?
Yes, when purpose-built for the domain. Generic document processing tools fail on construction lending because draw packages are multi-document packets with variable formats, not standardized single-page documents. MightyBot's document intelligence pipeline was specifically designed for this complexity, with type-specific extraction for AIA forms, lien waivers, inspection reports, and insurance certificates.
