Private Model Deployment
AI for your businessYour core data, under your control
Deploy AI for biotech in your designated environment, with clear control over processing locations, access, and model data use.
R&D materials · Experiment data · Internal knowledge
01 / Security and privacy first
Define your data boundaries first
Where is my information processed?Know where each processing step happens
Map uploads, parsing, retrieval, inference, and result storage, placing required steps within the agreed data boundary.
Delivery evidence: data-flow documentation and actual deployment settings.Who can access these materials?Manage access by team and project
Configure file and retrieval permissions by role and project. Separate routine use, result review, and maintenance, and define access changes when personnel change.
Delivery evidence: permission matrix and cross-project access tests.How do models use my data?Agree explicitly on data use
Specify whether data is used for training or fine-tuning, whether conversations and files are stored, and retention, deletion, and backup rules. Include the prohibition on unauthorized training in the project agreement.
Delivery evidence: data-use and retention rules with matching configuration.Does the system need internet access?Set network policies around your requirements
Controlled-network plans specify external services and access scope. Offline plans prepare models and dependencies in advance and test disconnected startup and complete workflows.
Delivery evidence: network access list and offline validation records.02 / See every step
How a document is processed inside your organization
Select a step to see its location and access scope.
Offline plan: prepare models and packages, import updates through agreed methods, and verify disconnected startup, restart, and complete tasks.
Step 01
Start with authorized documents
Define files and project scope available to each task, retaining original sources.
- Processing location
- Agreed project file space
- Access scope
- Project members and designated reviewers
- Records and retention
- Retain originals and upload records under project rules
Interactive illustration with fictional materials. No files are uploaded or models called. Actual permissions and offline operation require deployment configuration and validation.
03 / Biotech use cases
Protect core information while moving work forward
Choose a scenario to explore example inputs and outputs.
04 / Deployment options
Start with what you have
Your deployment assessment
Testing & Quality
Start with a real task
- Define tasks and data requirements first, then compare company hardware and private-cloud options.
- Identify network access required for parsing, model calls, storage, and updates.
- Choose one task and representative examples to assess output quality and processing time.
Models, resources, and costs are determined after sample validation.
05 / Validate with real tasks
Verify data boundaries and practical results together
Example evaluation criteria
Turn data boundaries into verifiable results
Use the same representative tasks and record test conditions and results in a traceable evaluation report.
- Processing scope
- Verify actual parsing, retrieval, model, and storage locations.
- Access permissions
- Test access across roles and projects, including access revocation.
- Data retention
- Check log contents, temporary files, retention periods, and deletion methods.
06 / Delivery & maintenance
From deployment to continuing team use
A service the team can use, with clear owners
Provide agreed entry points, instructions, and training, with clear responsibilities for business and maintenance staff.
- User access and instructions
- Access configuration and run records
- Issue reporting and response
FROM NEED TO DELIVERY
Start model deployment with the business task
What to prepare
Provide anonymized task examples, existing server or compute specifications, expected users and concurrency, and network and data-processing requirements.
Agree on delivery
Define delivery around model APIs, access controls, quality and resource testing, deployment configuration and maintenance and rollback procedures. Verify complete tasks with the team’s own examples.
Questions before we start
Do we need to buy a new server first?
Assess existing resources and real tasks before choosing models, capacity and concurrency. Test answer quality, response time and resource use together, then plan investment from a clear configuration recommendation.
How does the deployed model connect to our systems?
Connect applications through agreed service APIs and authentication, with defined inputs, outputs, timeouts and error handling. Add a knowledge base for internal information or a workflow for multi-step tasks.
How are models and software updated offline?
Prepare model files, software dependencies and update packages in advance. Plan controlled import, version checks and rollback, then use agreed examples to verify offline startup and task execution and retain maintenance records.
Start with one real task
Fit deployment to the business
Keep data boundaries clear
Work with ProteinX engineers to map documents, environments, and usage.
Validate feasibility before agreeing on the deployment scope.
Start with three questions