On-premises deployment built for local compliance requirements: models and data stay inside your own boundary, put to work scenario by scenario.
infer.local:8080Healthyvector.local:6333Watchingdata.local:5432RecoveredMatch your scenario, not a feature list. Each one goes from the pain you have today to what's possible after private deployment.
Patient records and imaging data are sensitive and can't leave the hospital network.
After private deployment: After private deployment, structuring, imaging assistance and quality checks all run inside the hospital, with data staying local throughout.
Internal documents can't go into public products, and manual search is slow.
After private deployment: Turn policies, procedures and records into a queryable knowledge base inside your network, with traceable answers.
Production-line images involve proprietary processes and need real-time response.
After private deployment: Run inference locally on the line for defect detection and grading — millisecond feedback, images never leave the site.
Reading crop growth, forest condition and livestock health depends on experts who can't cover everything.
After private deployment: Use local models to read crop growth, forest condition and livestock health, turning expert judgment into a repeatable system.
Contracts and risk-control materials carry strict compliance requirements, and sending data out is risky.
After private deployment: Extraction, comparison and first-pass compliance screening all happen in a controlled environment, with an auditable trail.
For many organizations, keeping data in-house isn't a preference — it's a requirement. Putting the model inside your own boundary is the only path that works.
Adapting to approved local hardware and software, and coordinating compliance assessments, is specialized work that needs someone to own it full-time.
In-house teams are mostly focused on core business systems — they don't have the spare capacity or specialization for this.
Deploying, monitoring and upgrading inference services is ongoing work. Handing it to specialists costs less than building the capability in-house.
Built on domestic open-source models — GLM, DeepSeek, Qwen, Kimi — adapted per publicly released weights, open, controllable, and running entirely on your own infrastructure.
Confirm your scenario and compliance boundaries first
Choose the model foundation and deployment form based on data requirements
Implementation happens entirely within your own environment
Ongoing operations and team training after delivery
Leave your details and we'll confirm your scenario and compliance boundaries first, then follow up with a workable proposal.
Actual services are subject to a joint requirements review and a formal agreement.