The Problem

Most business AI failures are data failures - poor quality inputs, stale knowledge bases, or no structured connection to the information that actually matters. An AI system that answers questions incorrectly because its data is wrong is worse than no AI system at all.

The Solution

We build and manage the data layer beneath your AI: RAG pipelines, knowledge bases, document ingestion workflows, vector databases, and retrieval tuning. Your AI applications give accurate answers because the information feeding them is accurate and current.

// What's Included

Everything covered in one agreement.

RAG Pipeline Design & Build

Retrieval-augmented generation pipeline designed, built, and deployed - so your AI answers questions from your own documents and institutional knowledge.

Knowledge Base Construction

Structured knowledge bases built from your existing documentation, SOPs, policies, and procedures - organized and indexed for accurate AI retrieval.

Document Ingestion Pipelines

Automated ingestion of business documents into your AI knowledge layer, with chunking and preprocessing strategies optimized for retrieval accuracy.

Vector Database Management

Vector database provisioned, configured, and maintained - including embedding updates, index optimization, and performance monitoring over time.

Data Quality Assessment

Baseline evaluation of your existing data quality - identifying gaps, inconsistencies, and sources that will hurt AI accuracy if left unaddressed.

Retrieval Accuracy Tuning

Retrieval testing against real business queries, with iterative tuning until accuracy meets the standard your team needs before going live.

Data Refresh & Maintenance

Automated pipelines that keep your knowledge base current as your documentation and business information changes over time.

Access Control & Segmentation

Role-based access controls on your AI knowledge layer - so different users access only the information they’re authorized to retrieve.

AI Data & Knowledge Management