How to automate the manual work of title search

And every state plays by different rules. Water rights in Nevada work nothing like Pennsylvania. Mobile home regulations shift county by county. A red flag in Florida might mean nothing in Ohio. There are hundreds of exam codes, and the right one depends on the document type, the state, and the transaction.

The operation processes thousands of orders per day in Pennsylvania alone.

Before AI, all of this was manual. An examiner would open a massive vendor package, a single PDF sometimes running over 100 pages, and split it by hand. They would read every document against a checklist. Compare legal descriptions. Plot boundary coordinates using an external tool called a deed plotter. Assign exam codes by browsing a library of hundreds. Flag anything suspicious.

Every order. Every day. A staggering amount of skilled but repetitive work.

They had already calculated the number. They wanted AI to handle the repetitive work so the team could focus on judgment calls that actually require human expertise.

The ambition was matched by discipline. They did not want to flip a switch and automate everything overnight.

The approach here was different. Keep the human in the loop. Let AI do the work, but let the examiner verify. Track the accuracy. If 99 out of 100 results get approved, that step can be fully automated.

Experiment. Measure. Adjust. Expand only when the data says it is safe.

This philosophy shaped every decision that followed.

The logic was straightforward: we built this in two weeks, you are professionals, you should be able to do it in two days. That was the expectation.

The reality required explaining that a prototype is not a product. Production-ready means security, scalability, a real database, and integration with live order data. It means the tool works when thousands of people rely on it daily.

Then came the harder part: proving it by delivering fast.

The UX designer gathered requirements, pushed them into Figma Make, and generated concept screens in hours. By the next day, designs were ready. The front-end team used Cursor, Claude, and Copilot for code generation. The backend team used Claude Sonnet and Cursor for feature development. A custom Rovo AI agent in Jira drafted and refined user stories from project documentation.

The first production-ready product shipped in three weeks.

AI was not just the product being built. It was how the product was built.

This was a new domain for the people being interviewed. They were not AI enthusiasts. They had never used AI tools. They had no idea what to expect, what AI could do, or what they wanted from it.

Discovery took longer than building the first prototype. The output was a set of vague points that leadership never confirmed or prioritized.

So the team stopped trying to extract requirements from people who could not yet imagine the product. Reduce time to market. Cover the critical functionality without overcomplicating it. Launch into production so real users start using the tool. Then gather feedback and improve based on real behavior, not hypothetical interviews.

That became the operating principle. Leadership provided a feature list, the team helped prioritize by business value, and delivery moved in focused increments.

For quality checks, decision trees sit inside the prompts. The model does not freely reason about what might be a problem. It follows a path. Does this document mention mobile homes? If yes, check these rules for this document type. If no, move on. Every branch ends in a clear decision. The model asks the same questions every time. Predictable beats clever.

Prompt research started with the two highest-volume states, Pennsylvania and Florida, and used those as the baseline. Covering every edge case in every state from day one would have drowned the work in noise.

Tone mattered as much as accuracy. When the AI flagged issues by saying “this is incorrect,” examiners pushed back and negative feedback spiked. The fix was to rewrite everything as a suggestion. “We see this, but your title report mentions X. Worth a look.” Same information. A completely different reaction.

How AI communicates determines whether users adopt it or fight it.

TitleFlow connects to it. The help desk is being integrated. Another internal project, SDA, connects next. One engine serves multiple platforms without duplicating the infrastructure.

For data access, the team built an MCP server. The model needs order details, delivery history, exam codes, and the rest of the business data that lives in TitleFlow. The server exposes those as tools. Adding a tool is a configuration change, not a code change.

Security uses a delegation token. The MCP server is on the public internet, and the data behind it is sensitive legal information. Instead of handing real credentials to the model, the system issues short-lived tokens with limited permissions. Read-only access to one folder, not the keys to the account. If a token leaks, the damage stays contained.

Some chains take 10 to 15 minutes. Execution moved from synchronous to asynchronous, so people keep working while the AI runs in the background.

When the primary provider had a four-hour outage, failover switched to a backup model and the service stayed up. The primary model was chosen for its strength in the legal domain. Resilience still means a fallback is always ready.

A small team delivered at the pace of a much larger one. Three weeks instead of six months for the first product, then a steady stream of features after that.

The client has already reached 30% of the annual cost-saving target with what is in production.

The engine is still expanding. Help desk integration is underway. SDA is next. New prompt chains keep shipping from real user feedback.

The system was not rebuilt. An AI layer was added carefully, and users were moved toward it step by step, with proof at every stage.