AI Analytics Platform

Putting an LLM in front of private data is easy to demo and hard to engineer well. Answers must be grounded in the organization's own documents rather than the model's imagination. Retrieval has to stay accurate as the corpus grows, and access control has to hold across both the chat and dashboard surfaces.

• •Ungrounded LLM output is unusable for analytics: every answer needs traceable citations
• •Pure vector similarity search degrades on tabular and numeric content
• •Long documents break naive chunking and lose cross-section context
• •Streaming responses require careful backpressure between LLM, API, and UI
• •Row- and document-level permissions must be enforced inside retrieval, not after it

We built the pipeline as five explicit stages (ingest, chunk, embed, retrieve, synthesize), each independently testable and swappable, with an evaluation harness measuring retrieval quality on every change.

• Built structure-aware chunking that preserves headings, tables, and section context
• Combined pgvector similarity search with keyword and metadata filters for hybrid retrieval
• Enforced permission filters inside the retrieval query so restricted content never reaches the model
• Streamed LLM responses token-by-token over server-sent events with inline citation markers
• Drove dashboards and chat from the same query layer to keep numbers consistent across both