When an automation project stalls or produces unreliable results, the conversation usually turns to the vendor, the technology stack, or the complexity of the workflow. Rarely does it turn to the thing most often actually responsible: the quality and consistency of the data the bot is running against.
TrueFocus Automation, a Dallas-based RPA and AI automation firm specializing in title insurance and mortgage operations, encountered this directly. After building a bot for a client’s proprietary title plant, the automation worked until it did not. The process appeared to run correctly. The client’s team did not raise concerns. Then, a new operations team came in and started asking why certain documents were not showing up in searches.
The answer was not a flaw in the bot. It was decades of inconsistent data indexing.
The Hidden Problem Inside Legacy Systems
Inconsistent historical data is one of the most common failure modes in automation projects involving legacy platforms, proprietary databases, or long-running internal records systems, and one of the least discussed.
The client in question had been manually indexing title plant records for roughly thirty years. Over that time, the way those records were entered had changed. The document naming conventions were not consistent and had different references to a Deed, such as DEED22W/1774, and others were indexed as 22/1774, D22-1774, D22W-1774. When TrueFocus built a bot that searched by document number, it returned only the records indexed a certain way and missed everything entered under a different schema years earlier.
Sridhar Loganathan, COO of TrueFocus Automation, described the pattern: “The data behind it is not consistent. Over those thirty years, they changed the way they indexed documents. When we built a bot searching using the document number, only those documents came up, not the others.”
The bot was doing exactly what it was built to do. The data underneath it was never consistent to begin with.
What Data Readiness Actually Means Before You Automate
Data readiness is not about whether your data is digital. It is about whether your data is consistent enough for a rules-based system to query it reliably. A bot cannot make judgment calls. If a document is indexed one way in 2003 and a different way over the years, the bot will find one and miss the other, every time, at scale.
The questions worth asking before any automation project begins include: How long has this data been in this system? Has the indexing approach changed over time? Were different teams responsible for data entry at different points, and were they working from the same standards? Is there any process that validates consistency across the data set?
These are not comfortable questions, and the answers are often worse than expected. But surfacing them during discovery costs far less than discovering them after six months of development.
TrueFocus now treats this kind of data review as a standard part of its discovery process, particularly for clients working with proprietary or long-running internal platforms. The logic is straightforward: automation amplifies whatever is already in your data. If the data is inconsistent, automation surfaces that inconsistency at speed and volume.
The Client Engagement Problem That Makes It Worse
The TrueFocus case carries a second lesson. The automation was live and running for some time before the data quality issue surfaced. The original team using the system did not report the missing documents – whether they did not notice, did not connect the gaps to the automation, or did not escalate, the result was the same: the problem grew silently.
When a new team came in and started asking questions, the gaps became visible. By then, TrueFocus had been operating on assumptions about system performance that the client had never corrected.
Loganathan identified the root cause directly: the client lacked a single point of contact with a deep understanding of how their own platform worked, and there was no regular feedback loop between the people using the outputs and the team managing the system. The team was using the solution but never reporting that anything was missing. Without that iterative communication, problems accumulated until they became expensive to fix.
Automation requires ongoing oversight. Bots need to be monitored, exceptions reviewed, and the people relying on the outputs need to stay in regular contact with whoever manages the system.
What an Honest Discovery Process Looks Like
For companies evaluating automation vendors, the willingness to surface problems before a project begins, rather than after delivery, is one of the most useful things a vendor can offer.
TrueFocus takes the position that identifying a data quality problem in week one is better than delivering automation that works intermittently and erodes trust in the technology. That means shadow sessions where the team doing the actual work walks through the process live. It means asking not just how the workflow functions today, but how it has changed over time and whether the underlying data reflects those changes consistently. And it means being willing to tell a prospective client that their data is not ready, even when that is not what they want to hear.
In the case that ultimately ended TrueFocus’s engagement with this client, co-founder Jimmy Lewis made the call directly: “If we can’t do it, let’s tell them now. I don’t want to drag this out.” That conversation came later than it should have, but the principle is sound. A vendor willing to tell you a project is not ready is more valuable than one who takes the work and delivers something unreliable.
Jimmy Lewis is the co-founder of TrueFocus Automation, a specialist in RPA and AI-driven workflow automation for the title insurance, mortgage, and real estate industries. TrueFocus has developed 840+ automation bots supporting more than 2,500 workflows and has returned over 1.3 million production hours to clients.
This article is based on information provided by the expert source cited above. It is intended for general informational purposes only and does not constitute legal, financial, or real estate advice. Readers should conduct their own research and consult qualified professionals before making any real estate or financial decisions.
