MightyBot

What is MightyBot?

MightyBot is a policy-driven AI agent platform for regulated, document-heavy operations. It turns plain-English policies, source documents, and system-of-record data into compiled execution plans that extract evidence, apply business rules, make or recommend decisions, act across connected systems, and record an audit trail.

The platform targets lending, insurance, payments, real estate, and compliance workflows where every result must be linked to the governing policy and source evidence. Instead of drawing a workflow canvas, subject-matter experts describe policies in plain English, while definitions are versioned and reviewed in Git. Teams can begin in Audit mode, progress to Assist, and increase automation only when review data supports it.

How to Use MightyBot

1. Select a repeatable, high-volume workflow with clear policies, source evidence, and review outcomes.
2. Map governing rules, required documents, systems of record, exceptions, and regulatory obligations.
3. Request a demo and scope integrations, security, data residency, audit, and human-review requirements.
4. Encode policies in plain English, version the definitions, and compile the execution plan.
5. Backtest policy changes against representative historical cases before production use.
6. Start in Audit mode, compare output with expert decisions, and measure overrides and edge cases.
7. Move to Assist or Automate only after approved accuracy, compliance, and operational thresholds are met.

Core Features

• Policy engine: Converts plain-English operating rules into executable, versioned definitions.
• Agent compiler: Builds schemas, workflows, and execution plans without a drag-and-drop canvas.
• Document intelligence: Extracts structured facts and retains pointers to source evidence.
• Deterministic execution: Applies compiled plans across documents, policies, and connected systems.
• Why-trails: Records the policy, evidence, data, and timestamp behind each decision.
• Human review gates: Supports staged autonomy and reviewer intervention.
• Managed backtesting: Evaluates policy changes against historical data before release.
• Enterprise integrations: Connects document repositories, loan systems, compliance databases, and other records.

Use Cases

• Review construction draws and supporting documentation.
• Automate commercial loan underwriting and financial spreading.
• Monitor loan covenants and compliance requirements.
• Analyze merchant statements for payment or lending workflows.
• Process insurance claims and medical-necessity reviews.
• Apply policies to regulated document-processing and back-office decisions.

Pricing

MightyBot does not publish standard subscription tiers on its public platform pages. Pricing is handled through a demo and scoped enterprise engagement. Buyers should confirm implementation, integration, support, infrastructure, model usage, audit, security, and minimum-commitment costs in the proposal.

Frequently Asked Questions

Does deterministic execution eliminate model errors?

No. Compilation, source evidence, and review gates improve control, but extraction, policies, integrations, and edge cases still require testing, monitoring, and expert oversight.

Can policies be reviewed like software?

MightyBot states that workflow definitions are human-readable, versioned in Git, and can be reviewed and deployed through standard engineering tools.

Should a regulated team automate decisions immediately?

No. Start in Audit mode, validate against historical and live cases, document overrides, and raise autonomy only with compliance, risk, and business approval.