Customer-Owned LQMs
Put your proprietary data to work.
- Deploy models trained specifically on your data.
- Close the blind spots in your data. Guide decisions where you have none today.
- Unlock your data's value without exposing it.
Intuizi engineers private LQMs custom-built to your data structure, deploys agents with embedded intelligence directly into your existing workflows, and provides the compute and infrastructure you need to scale.
Businesses got good at collecting it. The problem is everything that happens after: build it yourself, and your data team becomes a report factory. Buy more data, and it sits in isolation from everything else you own. Buy more tools, a CDP, a clean room, a measurement platform, and you're left stitching together point solutions that were never built to talk to each other. Every path leaves you supplying the glue: the connections, the models, the decisions.
Intuizi connects a diverse set of signals to a private quantitative model, binds every response to evidence and policy, and delivers it directly into your workflows, from LLM harnesses to MCPs, so you get answers and take action inside the systems you already run.
Put your proprietary data to work.
AI access to your data, tuned to how you work.
The infrastructure you need to compete, without the burden of building, maintaining, and processing it yourself.
Implementation begins with credentialed access to your datasets. Intuizi starts with a base LQM for your vertical, then customizes it to your data and your needs. The model is useful immediately and fine-tuned over time.
Yes. You own and control the model and its artifacts, and it runs without touching the Living Behavioral Model. You control what is exposed to the model. No PII enters the system, and all data is encrypted in transit.
Your model runs in Intuizi's own data centers, on hardware we own and operate on your behalf.
A clean room governs matching and collaboration between parties. It does not create the purpose-built model or the evaluation loop. A CDP organizes customer records. It is not a cross-modal quantitative model environment.