MightyBot Transforms Lending with Industry's First Autonomous Agentic AI.
February 25, 2026
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AI Thinking
RAG (Retrieval-Augmented Generation) retrieves relevant information to improve AI responses — but in regulated industries, retrieval is not enough. When an AI agent accesses sensitive financial data, the question is not just "did it find the right information?" but "what did it do with that information, was the action compliant, and can you prove it?" RAG answers the first question. Policy-driven AI answers all three.
RAG has become the default architecture for enterprise AI deployments. It solves a real problem: LLMs hallucinate less when they can reference relevant documents. Platforms like Glean, Moveworks, and dozens of startups have built successful products around the RAG pattern — connect enterprise data, retrieve relevant chunks, and generate better answers.
But the RAG pattern has a fundamental gap when applied to regulated industries. It optimizes for knowledge retrieval without addressing what happens next: the decisions made, the actions taken, and the evidence required to prove compliance. For financial services, healthcare, insurance, and other regulated domains, this gap is the difference between a useful search tool and a production automation platform.
RAG is a genuine architectural advance. Before RAG, LLMs relied entirely on training data — which could be outdated, incomplete, or wrong for enterprise contexts. RAG solves this by injecting relevant context at inference time.
A RAG-based enterprise search system can find the relevant policy document when an employee asks a question, surface the right procedure manual section for a compliance inquiry, retrieve past case files that resemble a current situation, and aggregate information from multiple data sources into a coherent answer.
For knowledge retrieval — helping people find and synthesize information — RAG works well. The problem starts when organizations try to use RAG as the foundation for automated decision-making in regulated workflows.
Five specific gaps make RAG insufficient for regulated automation.
RAG retrieves information. It does not enforce rules about what the AI does with that information. If a RAG system retrieves a lending policy document, it can answer questions about the policy — but it cannot evaluate whether a specific loan application complies with that policy, produce deterministic pass/fail results, or generate evidence of the evaluation.
Policy-driven AI adds the enforcement layer: business rules encoded as executable logic that governs agent behavior. The policy engine evaluates each rule against extracted data and produces deterministic outcomes — not generated text that might be right.
RAG systems can tell you which documents were retrieved to generate a response. But they cannot link specific facts in the response to specific locations in specific documents — the page, paragraph, and character boundaries where the information was found.
In regulated industries, "the AI referenced this document" is not sufficient. Regulators need "the AI extracted the insurance coverage amount of $2.5M from page 7, paragraph 3 of the certificate dated February 12, 2026, and evaluated it against Policy 4.2 which requires minimum coverage of $2M." That level of evidence linking requires a document intelligence pipeline, not just retrieval.
RAG retrieves text chunks. But regulated workflows require structured data — normalized fields, cross-document entities, and reconciled values. A construction lending draw request needs the contractor's name normalized across all documents, the insurance coverage amount extracted as a number (not embedded in prose), and the lien waiver amount reconciled against the AIA form line item.
MightyBot's document intelligence pipeline converts documents into structured, evidence-linked data at three levels: full document records (L0), page and section records (L1), and normalized entities across documents (L2). This is fundamentally different from text chunk retrieval.
RAG generates text responses. Policy-driven AI generates decisions with audit trails. The difference matters when a regulator asks "why did your system approve this draw request?" A RAG system can only show the documents it retrieved. A policy-driven system shows the exact data extracted, the exact policies evaluated, the exact results, and the exact evidence — the complete why-trail.
As enterprise AI evolves from search (RAG) to agents (autonomous action), the governance gap widens. An AI agent that can approve transactions, send communications, or modify records needs more than good retrieval — it needs policy boundaries, evidence requirements, and human oversight mechanisms.
RAG platforms that add agent capabilities without adding governance create risk. The agent retrieves the right information and then takes an action — but there is no policy governing whether that action is appropriate, no evidence trail proving compliance, and no mechanism for graduated human oversight.
The architectural difference between RAG and policy-driven AI is the data engine. RAG treats documents as text to be retrieved. Policy-driven AI treats documents as structured records to be evaluated.
When a document enters MightyBot's pipeline, it goes through classification (what type of document is this?), extraction (what are the specific fields and values?), normalization (how do we reconcile "Metro Plumbing LLC" on the lien waiver with "Metro Plumb." on the insurance cert?), and evidence linking (where exactly in the source document does each value come from?).
The output is not text chunks — it is structured data with evidence pointers. A search for "Metro Plumbing" returns the normalized entity record (L2), linked to every page where that entity appears (L1), linked to the original documents (L0). This search-ready structure supports both human queries and automated policy evaluation.
Even at the retrieval layer, MightyBot's approach differs from standard RAG. Most RAG systems use either keyword search (BM25) or semantic search (vector embeddings). Each has strengths and weaknesses.
Keyword search finds exact matches but misses semantic equivalents ("lien waiver" vs. "mechanic's lien release"). Semantic search understands meaning but can miss critical exact terms (a specific policy number or a precise dollar amount). In regulated workflows where both precision and recall matter, neither alone is sufficient.
MightyBot uses hybrid search — BM25 and k-NN vector search together with optional reranking — ensuring that both exact matches (policy numbers, dollar amounts, dates) and semantic equivalents (different terms for the same concept) are retrieved. This hybrid approach, combined with faceted filtering and structured data, delivers significantly better results than text-chunk RAG for regulated workflows.
| Use Case | RAG Sufficient? | Why / Why Not |
|---|---|---|
| Employee knowledge search | Yes | Retrieval and synthesis is the core need |
| IT helpdesk automation | Mostly | Low-risk actions, clear resolution paths |
| Document Q&A (non-regulated) | Yes | Answers do not require audit trails |
| Loan document review | No | Requires structured extraction, policy evaluation, evidence trails |
| Compliance verification | No | Requires deterministic pass/fail with proof |
| Insurance claims processing | No | Requires cross-document reconciliation and audit |
| Autonomous financial decisions | No | Requires policy governance and why-trail |
The dividing line is clear: if the outcome is a text response, RAG may be sufficient. If the outcome is a decision with regulatory consequences, you need the full stack — document intelligence, policy enforcement, evidence trails, and governance.
Horizontal RAG platforms (Glean, Moveworks, Guru) excel at enterprise knowledge management. They make it easier for employees to find information across scattered data sources. This is valuable, and these products are successful.
But they completely ignore regulated industries. None of them provide policy enforcement, evidence-linked extraction, deterministic decision-making, why-trail auditing, or compliance exports. They were not designed for these requirements, and adding them would fundamentally change their architecture.
This is why MightyBot does not compete with RAG platforms — it serves a different market with different requirements. The Built Technologies deployment demonstrates what that difference means in production: 99%+ accuracy, full auditability, and autonomous decision-making in mission-critical financial workflows. No RAG platform delivers that.
Is RAG enough for regulated industries?
No. RAG retrieves information to improve AI responses, but regulated industries require policy enforcement over what agents do with retrieved data, evidence chains linking decisions to source documents, structured data extraction, and auditable decision trails. RAG addresses retrieval; policy-driven AI addresses the full decision lifecycle.
What is the difference between RAG and policy-driven AI?
RAG retrieves relevant text chunks and uses them to generate better responses. Policy-driven AI extracts structured data from documents, evaluates business rules against that data, takes governed actions, and produces evidence-linked audit trails. RAG optimizes for information retrieval; policy-driven AI optimizes for auditable decision-making.
Can you use RAG and policy-driven AI together?
Yes. MightyBot uses hybrid search (BM25 + vector search) as part of its retrieval layer, but adds structured extraction, policy evaluation, and why-trail auditing on top. The retrieval component is one piece of a larger pipeline that converts documents into governed, auditable decisions.
Why do RAG platforms ignore regulated industries?
Horizontal RAG platforms are designed for knowledge management — helping employees find information. Regulated industry requirements (policy enforcement, evidence chains, deterministic decisions, compliance exports) would fundamentally change their architecture. These are different products for different markets.
