My client had one person doing data reconciliation by hand. Full time, more than a month in, and still not done. The data was scattered across scanned PDFs, legacy database exports, and Excel files that had accumulated over years. All of it had to be organized, consolidated, and transformed, and that work was holding up a project already badly behind schedule.
If you’ve spent any time at a growing company, you’ve probably seen this scene before. The data lives everywhere. Every system was “the system” at some point. Somebody built a spreadsheet to track the thing, and then that spreadsheet became the thing. A few years later, nobody’s confident what’s current, what’s abandoned, or what was never right in the first place. And somebody, eventually, gets handed the job of making sense of all of it.
That’s where we came in.
What we built
We built a pipeline that pulled every source together, matched records across them, and flagged conflicts for human review. It read scanned documents, pulled structured data out of the legacy systems, and normalized everything into one consistent view. Where the records disagreed, and they did often, the pipeline flagged the ambiguity for a human to resolve instead of guessing.
Four days of work replaced more than a month of manual reconciliation. The output was more accurate too, because the pipeline caught conflicts a human scanning thousands of rows would eventually miss from fatigue alone.
Those numbers are the easy part of the story. The harder part, and the one I keep coming back to, is what happened after the reconciliation was done.
The surprise wasn’t just the speed
For the first time, my client could see every term across every agreement in one view. Not the summary version somebody had typed up years ago and nobody had updated since. The actual terms, from the actual documents, laid out side by side.
That view had never existed before. It couldn’t have. You can’t build a view of something nobody has time to look at, and nobody had time because every hour was getting spent reconciling the raw data just to get to a starting point. The manual process was never going to produce this. Not because the people weren’t capable, but because the effort was unbounded and the deadline wasn’t.
When the pipeline was done, the deadline stopped being the constraint. The question changed. Instead of “can we finish the reconciliation in time?” it became “now that we can see everything, what do we actually want to do with it?”
That’s the part I keep coming back to. AI didn’t just compress the timeline. It made a question answerable that hadn’t been answerable before.
What this means for growing companies
Most of the AI conversations I have with business leaders are about automation. Can AI do this task faster, cheaper, with fewer people. That’s a fine question, but it’s a limited one. It assumes the work you’re already doing is the work worth doing.
The more interesting question is what you’d do if the work you’re stuck on stopped being the bottleneck. What would you ask if asking it wasn’t a six-week project? What would you know if knowing it didn’t require a headcount you don’t have? What decisions would you make differently if the view you needed was a day away instead of never?
Growing companies live with bottlenecks they’ve stopped noticing. The manual grind on disparate data is one of the most common. It gets absorbed into somebody’s job title, and nobody asks whether it should exist, because the alternative looks like a project nobody has time to sponsor.
It doesn’t have to be that way anymore.
If you’re running a growing company and you’ve got people grinding on data work that nobody has the bandwidth to question, there’s probably a question you’ve been putting off asking because the answer felt too expensive to find. That’s worth a conversation. See how we help clients rethink what AI actually changes.