Applied AI Systems
Capture how the work is actually done before encoding it into a system.
Work with subject-matter experts to turn tacit judgment into structured knowledge a system can act on.
Every organization has people who know how the work is really done. Which exceptions matter. Which signals are noise. What a good outcome looks like when the documented process and the actual process disagree. That knowledge is rarely written down, and almost never written down in a form a system can use.
This is where applied AI stalls. A model is pointed at the documents and produces answers that are fluent and wrong in ways only the expert can see. The team blames retrieval, then the model, then the prompt. The real gap is that nobody captured the expertise the system was supposed to apply.
Knowledge Engineering treats that capture as the first build step. It gives the team a structured way to elicit expert judgment and represent it as decision rules, worked examples, taxonomies, and boundaries that downstream systems can reach. What matters next is fidelity: whether the system reasons the way the expert would, and whether anyone can tell when it does not.
Let’s get going
- Start with one expert and one decision — Pick a single recurring judgment call where the expert is the bottleneck, and work through real cases rather than abstractions.
- Capture the exceptions, not the happy path — Documented process describes the common case. Expertise lives in what the expert does when the case is unusual.
- Represent it where the system can reach it — Turn captured judgment into structured context, examples, and constraints that feed retrieval, behavior, and evaluation rather than a document nobody loads.
Outcomes
- Expertise that outlives the expert — Judgment that lived in one person becomes organizational knowledge that can be reviewed, corrected, and reused.
- Systems that reason in domain terms — Models operate on the distinctions the work actually turns on rather than on generic language patterns.
- A shared standard for correct — Captured knowledge becomes the reference that evaluations, reviews, and disagreements resolve against.