Datasaur founder and CEO Ivan Lee spoke at IPADE Business School about Private AI moving from preference to necessity.

Datasaur builds and deploys Private AI for organizations that can't send their data to someone else's cloud — banks, hospitals, government agencies, and enterprises handling regulated, proprietary, or competitively sensitive information. Private AI means the model, the data, and the inference run inside the customer's perimeter, on their terms, under their controls. We are an AI-native services firm. Traditional systems integrators learned AI as an add-on; we were built around it. Every engagement starts from the assumption that the harness around the model — the data pipeline, the evaluation loop, the agent scaffolding, the deployment infrastructure — is where the real engineering lives. The model is commoditized. The system around it is not. Our work spans three layers: Data — labeling, curation, evaluation, and the feedback loops that keep models accurate as the world drifts. Model — fine-tuning, distillation, and selection work that matches capability to the customer's constraints (latency, cost, sovereignty, risk). Agent — the orchestration, tool use, and guardrails that turn a model into a system the operations team actually trusts. Customers come to us when they have tried the off-the-shelf approach and hit a wall — usually around compliance, accuracy in their domain, or the integration work no vendor demo prepares them for. We get them from there to production. If you are evaluating Private AI for your organization, we should talk.