Under the Hood

A practical view of the infra-research pipeline that powers how Juris IQ converts legal questions into grounded, reviewable outputs.

Juris IQ infrastructure workflow diagram

Infra Research Flow

The drawing maps a full-stack research pipeline: user query enters through the frontend, context is structured in the FastAPI layer, and retrieval signals are assembled before generation starts. This design keeps output quality tied to source quality.

  1. Entry and Context: query input, document upload, and metadata storage establish the matter context before downstream processing.
  2. Document Intelligence: cloud storage, text extraction, and embeddings transform unstructured legal material into searchable semantic units.
  3. Retrieval Backbone: the vector index and ranking signals prioritize authorities by issue relevance, citation strength, and legal context fit.
  4. LLM Orchestration: query processor and LLM integrator coordinate retrieval-grounded prompts so generation remains anchored to evidentiary sources.
  5. Structured Output Layer: the system returns review-ready summaries and draft blocks that can be validated and refined within legal workflows.

Retrieval is not a single lookup step in this architecture; it is a continuous signal loop connecting storage, extraction, embedding, ranking, and generation. That loop is what improves consistency, traceability, and confidence across research-heavy matters.

“Better legal output starts with better retrieval infrastructure, not prompt tricks alone.”