Category: AI/ML Architecture / RAG
Client: Federal Infrastructure Agency | 2024
Tagline: Transforming document query capabilities through advanced AI/ML technologies.

Led development of an advanced RAG-based system that upgraded document query and knowledge-management performance for a federal environment.

Outcomes

• Metadata-enhanced RAG architecture: Expanded vector schemas with intelligent metadata filtering to improve response quality.
• Performance optimization: Achieved >50% reduction in token usage while increasing answer quality through semantic-aware filtering.
• Evaluation framework: Built a comprehensive RAG harness with retrieval, response quality, and attribution metrics.
• Enterprise integration: Prototyped a Databricks SQL connector for natural-language queries across structured and unstructured data.
• Operational impact: Shifted retrieval from ad hoc search to measurable, trust-oriented knowledge operations.

Stack / Systems

RAG · LLMs · Databricks · Vector database · Retrieval evaluation