Search, data, and knowledge systems people can actually trust and use.
Organizations often have more data than agreement, more sources than ownership, and more search interfaces than useful results.
Information ends up across relational systems, documents, vendor platforms, APIs, spreadsheets, product catalogs, operational tooling, search indexes, and the memory of whoever gets called when something breaks.
General Applications engineers the models, ingestion pipelines, retrieval strategies, relevance controls, and interfaces that turn fragmented information into dependable systems people and AI can actually use.
What We Build
Search quality starts in the data, not the AI.
Search systems fail when underlying entities are inconsistent, source authority is unclear, the taxonomy is weak, ingestion drifts, schema changes go unnoticed, and relevance becomes a stack of guesses nobody can explain.
An LLM can improve how people ask questions and how results are presented, but it cannot compensate for weak source data, an incoherent canonical model, a poor indexing strategy, the wrong retrieval method, or relevance controls that cannot be measured and defended.