Some data can't leave your environment
Unpublished research, patient-level clinical data, proprietary formulations and confidential agency correspondence often can't go to a third-party AI service, whatever the vendor's terms say. Teams either avoid AI or use it informally, which is worse.
- Data use agreements, IP concerns or internal policy rule out hosted AI
- Staff use consumer AI tools because there's no approved option
- IT and QA need to know exactly where data goes and who can see it
What we build
Self-hosted model deployment
Open-weight models on infrastructure you control, on-premise or in your own cloud account, sized to the workload.
Private document assistants
Search and question-answering over internal documents, entirely inside your environment.
Tiered model routing
Sensitive data goes to your private model. Lower-risk work can use a hosted enterprise model, under rules you define.
Access and usage logging
Role-based access and logs of who asked what and which data was used, for IT, security and QA review.
How it works
Step 1: Classify the data and the use
With IT, security and QA, we agree which data can go where and which tasks need a private model.
Step 2: Choose and test models
We evaluate open-weight models on your actual tasks and tell you where they fall short of hosted ones.
Step 3: Deploy in your environment
Model and application go into your infrastructure with least-privilege access to data sources.
Step 4: Operate and tune
Under our Operate retainer, we monitor performance, update models and add use cases.
Systems we connect
- Your own cloud accounts or on-premise servers
- SharePoint, Microsoft 365 and Google Workspace document stores
- LIMS, ELN and eQMS platforms, scoped during Discovery
- Hosted enterprise AI plans for lower-risk work, where your policy allows
Reference
Our VP of AI Engineering, Michael Fethe, spent 15 years inside publicly traded agriculture and life science corporations. We bring that experience together with the documentation, audit-trail and private-deployment patterns we've built for regulated manufacturing and finance clients.
Related work outside life science: we do self-hosted and open-weight model R&D for clients whose data can't go to hosted models, including government and federal work.
Questions IT and QA teams ask
Are self-hosted models as capable as hosted ones?
Not always. Open-weight models can trail the best hosted models on complex tasks. We test on your actual work and tell you where the gap matters.
Is this HIPAA or GxP compliant?
No certificate makes a tool HIPAA- or GxP-compliant, and we don't claim either. It depends on how a system is deployed, validated and used, which your compliance and QA teams determine.