Case Study
Enterprise Legal RAG with Retrieval Precision Engineering
Built a multi-tenant legal RAG platform with dynamic chunking, hybrid retrieval, reranking, and metadata-aware search to improve precision and trust in high-stakes legal workflows.
Challenge
The legal workflow required high-confidence answers over large and evolving document corpora with strict tenant isolation. Baseline retrieval approaches were returning broad but noisy context, reducing answer trustworthiness. The platform also needed predictable latency despite multi-step retrieval and strict data-boundary controls.
Solution
Helios implemented a retrieval precision stack combining dynamic chunking, sparse+dense hybrid search, and reranking before context assembly. Metadata-aware retrieval policies incorporated client boundaries, document recency, and structured document attributes. Tenant-level isolation controls were enforced at indexing, retrieval, and query orchestration layers. Evaluation datasets and retrieval quality pipelines were introduced to continuously measure recall, precision, and grounded-answer quality.
Technical Responsibilities
- Designed multi-tenant RAG architecture with strict tenant data isolation
- Implemented dynamic chunking strategies for legal document structure
- Built hybrid retrieval pipelines combining lexical and semantic search
- Integrated reranking to refine context relevance before generation
- Implemented metadata-aware retrieval filters for client and document scope
- Developed retrieval and answer-quality evaluation pipelines
- Optimized latency under production traffic while preserving retrieval quality
Outcomes
- Higher retrieval precision and reduced context noise for legal queries
- Improved trust in generated responses through stronger evidence grounding
- Reliable tenant isolation across clients and document sets
- Production-ready legal AI retrieval workflow with measurable quality controls