Insight · Plateer AI Labs

Article series

LLM Inside

The core ideas you need to make sense of an LLM, taken one at a time.

4 articles

The GS Certification Diary

Three weeks of reading 80 defect reports and rebuilding the code, the errors, and the manual

5 articles

The Harness Diary

Designing the execution environment an AI agent works in, from the ground up

10 articles

The Ontology Diary

Starting from the questions, building a knowledge graph, then working on quality and search

13 articles

All articles

Tech NoteThe Ontology Diary2026.04.24

The questions were deciding the scope of the answers (Part 1)

With only shipping and returns questions, the exception approval process in the source never entered the graph. Moving questions to evaluation.

김진수

Tech NoteThe Harness Diary2026.04.20

Retry counts told us nothing about the run (Part 1)

A run that called tools three times and one that failed quality checks three times both logged 'retry 3'. Splitting one loop into explicit states.

김진수

Field Report2026.04.17

How could AI agents change the way a company operates?

Questions from consulting and commerce operations — which work to hand to AI, how internal and customer-facing AI differ, how to turn legacy data into a knowledge structure, and how to govern a growing fleet of agents.

PPlateer AI Labs

Field Report2026.03.16

What should churn-prediction AI actually look at in B2B food distribution?

Why consumer e-commerce analytics do not transfer directly, how a design carries through from prediction to sales action, and which questions a short PoC should settle first.

PPlateer AI Labs

Field Report2026.02.18

Turning scattered enterprise data into knowledge an AI can actually use

Questions from a manufacturing and retail company evaluating an ontology-based AI platform — extracting a graph from documents, connecting the RDB, where public LLMs draw the data boundary, how often knowledge needs refreshing, and where to start.

PPlateer AI Labs

InsightInsight from our research and the field

The tools our agent built for itself were not canvas nodes

Article series

LLM Inside

The GS Certification Diary

The Harness Diary

The Ontology Diary

All articles

The questions were deciding the scope of the answers (Part 1)

Retry counts told us nothing about the run (Part 1)

How could AI agents change the way a company operates?

What should churn-prediction AI actually look at in B2B food distribution?

Turning scattered enterprise data into knowledge an AI can actually use