A retrieval platform that turns unstructured enterprise documents into an answerable knowledge layer. Ingestion, extraction, embedding and retrieval are engineered as independent stages so each can be tuned, monitored and replaced without rewriting the system.
Critical operational knowledge lives inside thousands of long, inconsistently formatted documents. Keyword search returns documents, not answers, and generic LLM interfaces hallucinate when they are not grounded in the organization's own content.
- —Transforms unstructured enterprise documents into searchable organizational intelligence
- —Answers are traceable to their source passages
- —Retrieval quality is measurable rather than anecdotal
Structure-aware chunking
Chunks follow document structure — sections, tables and headings — instead of fixed character windows, which measurably improves retrieval precision.
Metadata-first retrieval
Metadata filters narrow the candidate set before semantic similarity runs, keeping retrieval fast and scoped to the right document class.
Grounding over fluency
Every generated answer carries its source passages. If retrieval confidence is low, the system says so rather than generating.
Stateless orchestration
Retrieval and generation run behind stateless APIs so throughput scales horizontally and failures are isolated per request.
Table-heavy documents
Tables lose meaning when flattened to text. Extraction preserves row/column relationships and serializes them into retrieval-friendly representations.
Retrieval quality drift
An evaluation harness scores retrieval on a curated question set so chunking and embedding changes can be compared objectively.
Cost control
Caching, candidate re-ranking and context budgeting keep token spend predictable at scale.
- →Retrieval quality — not model choice — is the dominant factor in RAG output quality
- →Evaluation infrastructure must exist before tuning begins
- →Document structure is signal; discarding it discards accuracy