Use cases
Practical problems. A clear first step.
Three problems we see repeatedly, and how we'd take each one on. These are illustrative approaches rather than past engagements — written so you can judge how we think before you talk to us.
01AI systems
The answers exist, but nobody can find them
The situation
A company has years of documentation, contracts, and support history spread across drives, wikis, and inboxes. Staff know the answer is somewhere. Finding it takes an hour, so most people guess instead.
How we'd approach it
Retrieval before generation. We index the real corpus, get search quality right on its own terms, and only then put a model in front of it. A wrong answer delivered confidently is worse than no answer, so the system cites its sources and says when it doesn't know.
What we'd build first
One document set, one team, measured against questions they actually ask. If retrieval isn't good enough to be useful, that shows up in week two rather than after launch.
02Custom software
The process runs on a spreadsheet nobody trusts
The situation
A workflow critical to the business lives in a shared sheet with manual steps, no history, and one person who understands it. It works until they're on holiday, and every month it takes longer.
How we'd approach it
Model the process as it truly runs, not as the org chart says it does. Build the smallest system that removes the manual steps and records what happened. Keep the spreadsheet as an export so nobody has to change how they work on day one.
What we'd build first
The single step that causes the most rework. One workflow, end to end, in production — not a rebuild of everything at once.
03Data platforms
Every number needs a caveat
The situation
Reporting is assembled by hand from several systems that disagree. Each meeting starts with an argument about whose figure is right, and the answer changes depending on who ran it.
How we'd approach it
Fix definitions before pipelines. Agree what a customer, an order, and a month actually mean, then build ingestion and transformation to that contract with tests that fail loudly when a source drifts.
What we'd build first
The handful of metrics that get argued about most, sourced once and computed the same way everywhere.
Have a different problem in mind? Tell us what your team needs and we’ll discuss a practical way to approach it.
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