LLM Data Architecture

Retrieval, embedding, and evaluation pipelines for production LLM applications — from RAG to fine-tuning.

AI Product Teams SaaS Companies Growing Startups

Overview

Production LLM systems are data systems first and model systems second. The hard problems — retrieval quality, evaluation, drift, cost, and reproducibility — are all upstream of the model itself. We design the data architecture that makes those problems tractable, whether you’re running RAG over an internal knowledge base or fine-tuning on proprietary data.

Reference Architecture

flowchart LR
Docs[("Documents<br/>& Knowledge")] --> Chunk["Chunking<br/>+ Cleaning"]
Chunk --> Embed["Embedding<br/>Pipeline"]
Embed --> Vector["Vector DB<br/>(Pinecone / Weaviate / pgvector)"]
Query["User Query"] --> Retrieve["Retriever"]
Vector --> Retrieve
Retrieve --> Prompt["Prompt Assembly"]
Prompt --> LLM["LLM Inference"]
LLM --> Response["Response"]
Response --> Eval["Evaluation<br/>(Golden Sets, LLM-as-Judge)"]
Eval -.-> Chunk
Eval -.-> Embed

Engagement Model

Engagements typically begin with an evaluation harness so every subsequent change — new embedding model, new chunking strategy, new retriever — can be measured against a fixed benchmark. From there we iterate on retrieval and prompt design with confidence that improvements are real rather than anecdotal.

What's Included

Retrieval-augmented generation (RAG) architecture and chunking strategy
Embedding pipelines with reproducible model + version tracking
Vector database selection (Pinecone, Weaviate, pgvector) and tuning
Evaluation harness with golden datasets and offline scoring
Prompt, model, and dataset versioning for safe rollouts

Technologies

PostgreSQL
Snowflake
Databricks
Apache Airflow
Python
AWS
Microsoft Azure

Related Services

AI Data Infrastructure
The data plane behind AI products — feature stores, vector databases, and pipelines that keep models fed with fresh, trustworthy data.
Analytics Engineering
Turn raw warehouse data into trusted, well-modeled datasets that analysts, executives, and downstream apps can rely on.
Data Integration Services
Connect disparate systems into a unified data ecosystem — APIs, streaming, master data, and cross-platform sync.

Ready to discuss your llm data architecture needs?

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