LLM Data Foundation

Establish a Production‑Ready Data Backbone for LLMs

Large Language Models only perform as well as the data foundation they run on. We design and implement the core building blocks that make LLM systems accurate, performant, and governable.

What We Deliver

• Data ingestion + quality: streaming/batch pipelines with schema validation, PII detection, and lineage.
• Embedding generation: scalable workers for text, image, and tabular embeddings with versioning.
• Vector storage: production‑grade vector DB setup (Milvus, Pinecone, Weaviate, Neo4j) with HNSW/IVF/PQ.
• Feature + context stores: reusable features, prompt context repositories, and semantic indexes.
• Governance: metadata catalogs, access control, retention, and audit trails (DataHub/Atlas).
• Observability + evals: tracing, metrics, drift monitors, and continuous LLM evaluation harnesses.

Reference Architecture

1. Raw sources → validated bronze → curated silver → application gold layers.
2. Embedding services with GPU/CPU autoscaling and caching.
3. Vector indexes optimized per use case (recall/latency/cost).
4. Policy enforcement and redaction at ingest and retrieval time.

Outcomes

• Higher answer accuracy with consistent retrieval quality.
• Lower latency and cost via right‑sized pipelines and indexes.
• Auditability and safety for regulated environments.

Ready to design your foundation? Contact us to schedule an architecture review.