Sound Familiar?
LLM prototypes that produce incorrect outputs
No vector store or semantic search capability
Pipeline quality too low to trust for automation
No monitoring or observability on data quality
Conflicting metrics mean AI gives conflicting insights
Leadership excited about AI but nothing is working
How your AI agent solves problems
What's Really Happening
AI fails without clean, reliable, modeled data.
Every AI system — whether it's an LLM, a recommendation engine, or predictive analytics — is only as good as
the data it's trained on. If your data is inconsistent, unstructured, or undocumented, AI will amplify those
problems.
This is a foundation problem.
What Needs to Happen
You need three things:
Quality
Clean, validated, monitored data pipelines
Structure
Well-modeled data with clear schemas
Infrastructure
Vector stores, embeddings, semantic layers
Warehouse
A central repository (Snowflake, BigQuery, Databricks) that scales
What We Build
LLMs grounded in your company's data
Natural language interfaces to your data
Data infrastructure designed for ML workloads
Semantic search across documents and data
