AI-ready Data Infrastructure

AI agents hallucinate because your data has no context. Your warehouse was built for humans who can "figure it out" on their own - no semantic layer, no proper documentation, no clear metric definitions.

That's why you burn tokens and get shallow answers. The AI simply doesn't have what it needs to give you a real answer.

The problem

You ask a simple analytics question and it takes days, sometimes weeks, for an analyst to even understand the data before they can answer it

AI agents give wrong or vague answers because your warehouse has no semantic layer or documentation

Your analysts spend most of their time writing SQL and answering repeat requests instead of doing actual analysis

You're paying for AI tools but getting minimal value because the data context isn't there

Stakeholders stop asking questions because the turnaround is too slow, so decisions happen without data

How we solve it

We make your data infrastructure work for AI, not just humans. That means semantic layers, proper metadata, clear metric definitions, and documentation that both people and agents can rely on.

Once your warehouse is agent-friendly, you can ask questions directly in Slack and get answers in minutes. Your analysts stop drowning in ad-hoc requests and focus on the bigger picture instead. We don't make them obsolete, we elevate their impact.

We implement the latest flagship models in your data stack - open source (GLM, Kimi, DeepSeek, Llama, Mistral, Qwen, Gemma) or proprietary (Claude, OpenAI GPT, Gemini, Grok), whichever fits your needs and constraints.