Deploy AI Systems That Keep Data On Your Infrastructure

Many businesses look at AI SaaS tools but find the data privacy policies unacceptable. A typical vendor's terms of service state they can use your data to train their models. Sending client contracts or patient records to a service with such terms is a major compliance violation.

Using a major API like Claude directly is an improvement, but your data still transits to their servers for processing. For strict compliance, data cannot leave your environment, even if the vendor promises not to store it. This creates a risk that is difficult for a 5-person company to underwrite.

Some teams try to solve this by self-hosting an open-source model. Downloading a Llama 3 model is simple, but running it reliably in production is not. It requires a dedicated GPU server that costs over $1,200 per month, plus significant engineering time to manage drivers, dependencies, and API uptime. For a workflow that runs 200 times a day, this is a financially unviable solution.

Syntora's approach to deploying private AI systems begins with a deep dive into your existing cloud environment and specific security needs. We would start by defining a robust security boundary inside your cloud account. In AWS, this involves building a Virtual Private Cloud (VPC) with private subnets that have no direct internet access. All sensitive credentials and API keys would be stored in AWS Secrets Manager, ensuring they are never hardcoded in the application. This foundational architecture is designed to align with SOC 2 practices.

For the core processing, Syntora would package your chosen AI model and a custom FastAPI application into a single Docker container. We often recommend targeted open-source models like Mistral 7B for specific tasks to optimize performance and reduce computational overhead compared to massive general-purpose models. This container would then be deployed on a scalable serverless platform like AWS Lambda, allowing it to scale efficiently from zero to hundreds of concurrent requests based on demand, resulting in cost-effective operation.

The system would be engineered to process data entirely in-memory during analysis. This means an API would receive a document, perform the analysis, and return the result without writing the source data to disk in the processing environment. Syntora would implement `structlog` to send structured logs for every request to a secure data store like a Supabase table. This creates an immutable audit trail, providing transparency on queries and AI decisions, which is critical for compliance.

Access controls would be custom-designed and implemented using role-based access controls (RBAC), integrated with your existing identity provider, such as Google Workspace, via OAuth2. Syntora would work with your team to define specific user roles and permissions, ensuring that only authorized individuals can perform certain actions or view audit histories, preventing unauthorized data access and providing clear accountability.

The deliverables for such an engagement would include a fully deployed, containerized AI system within your cloud environment, complete with infrastructure as code, comprehensive documentation, and a handover session for your internal teams. Your team would need to provide access to your cloud account for deployment and collaborate on defining security policies and user roles.