Pragmatic AI
Work

The work, in detail

Three problems taken apart: what made each one hard, what the obvious approach got wrong, and how it was actually built.

The first is a method you can apply to a report you already produce. The second is a system running in production today. The third is the thinking our inference platform came out of.

Application Note Automating a recurring report with AI Your assistant answers questions. It does not run a process. The difference, worked through on a quarterly market analysis report. Read the note Use Case Turning a legacy technical archive into a database Fifty years of handwritten field records in filing cabinets, unsearchable and slowly degrading. What it takes to get them into a system that geologists can actually query. Read the case Origin Note Why RAG breaks down at scale Retrieval answers from a few passages. Extraction needs every instance in the corpus. Reasoning across a corpus needs something above both, which is what our inference platform was built to do. Read the note

What these have in common

All three started the same way. Somebody had a process that worked, ran on people, and would not scale. That is a process problem with documents in the middle of it, not a data problem or an AI problem.

That is the work we take on. If yours looks like this, tell us about it. The first conversation is free.

Bring us the process that will not scale.

We will map how it actually runs today, then show you which parts a machine can take over and which ones need your people.

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