Your Data Platform Wasn't Built for AI
AI agents query 100x more than humans. They need context, not just columns. Your warehouse was designed for dashboards — we make it agent-ready.
Context is the difference between data and intelligence
Data platforms built for humans break under AI workloads
Your data warehouse handles 100 analysts running queries. AI agents run 10,000 queries per hour. Different scale, different patterns, different requirements.
Context gets stripped
Traditional ETL focuses on structure. The business context, relationships, and semantic meaning that AI needs to reason correctly gets lost in translation.
AI fills gaps with hallucination
Without context, AI models guess. They miss nuance, invent relationships, and require constant human correction. Garbage in, confident garbage out.
Platforms can't handle agent scale
Query patterns from AI agents are fundamentally different. Higher volume, more complex joins, real-time requirements. Traditional architectures buckle.
Governance becomes impossible
When AI agents access data at scale, you need semantic access control, not just row-level security. Who can access what meaning, not just what tables.
"Your AI is only as good as the context it has. Most AI is flying blind."
Three components of Data Intelligence
A complete approach to making your data platform AI-ready. Methodology for processing, framework for infrastructure, product for delivery.
ETL-C Framework
Context-First Data Processing
Traditional ETL strips context. ETL-C preserves and enriches it. Extract, Transform, Load, Contextualize — adding semantic understanding, contextual joins, and adaptive pipelines to your data processing.
SARP Framework
Agent-Ready Data Platforms
Prepare your data infrastructure for AI agent workloads. Semantic APIs, intelligent caching, query optimization, and governance — incrementally, without ripping and replacing your existing stack.
Context Engine
ETL-C as a Service
Managed platform for contextual data processing. Contextual joins, adaptive pipelines, semantic data access, and context store — without building the infrastructure yourself.
From raw data to AI-ready intelligence
Contextual Processing
ETL-C pipelines capture not just what happened, but why and how. Business context, relationships, and semantic meaning travel with the data through every transformation.
Semantic Understanding
Vector embeddings enable meaning-based retrieval. "J. Smith" and "Jane Smith" resolve to the same entity. AI queries by concept, not just keywords.
Agent-Scale Infrastructure
SARP-compliant infrastructure handles AI query patterns. Intelligent caching, semantic routing, and query optimization keep response times sub-second at 100x scale.
Semantic Governance
Access control by meaning, not just tables. Define who can access what concepts, with full audit trails for regulatory compliance.
Data Intelligence in practice
RAG Applications
Retrieval-Augmented Generation that actually works. Context-rich data means fewer hallucinations, more accurate answers, and citations you can trust.
AI Agent Data Access
Agents that can query your data at scale without breaking your infrastructure or your governance model. Sub-second responses, full audit trails.
Customer Intelligence
360-degree customer view with context. Not just what customers did, but why — enabling personalization that actually makes sense.
How we help
Data Intelligence Assessment
$25K
2 weeks. Current state audit, context gap analysis, AI readiness scoring, prioritized roadmap.
Architecture Design
$50K
4 weeks. Target architecture using ETL-C and SARP, technology selection, implementation plan.
Full Implementation
$150K+
8-16 weeks. ETL-C pipeline implementation, Context Engine deployment, SARP infrastructure upgrades, team training.
Ready to make your data AI-ready?
Request a Data Intelligence assessment to understand your context gaps and opportunities.