Deploy AI Systems That Keep Data On Your Infrastructure
You deploy AI systems by running them on servers you control, such as an AWS Virtual Private Cloud. This ensures sensitive data like customer information never leaves your own infrastructure.
This approach is for businesses that handle regulated data (HIPAA, SOC 2) or sensitive IP and cannot send it to third-party APIs. The scope involves containerizing the AI model and deploying it into an isolated cloud environment you own, with strict access controls and a complete audit trail on every action.
We recently built a private document analysis system for a 7-person law firm. They needed to analyze contracts without uploading them to a public service. We deployed a model inside their AWS account in 2 weeks, processing 500 documents per month with full ai-security-governance controls.
What Problem Does This Solve?
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.
How Does It Work?
We start by defining a security boundary inside your existing cloud account. In AWS, this means building a Virtual Private Cloud (VPC) with private subnets that have no direct internet access. All credentials and API keys are stored in AWS Secrets Manager, ensuring they are never hardcoded in the application. This architecture provides the foundation for SOC 2 aligned practices.
We package the AI model and a FastAPI application into a single Docker container. For most tasks, we use a targeted open-source model like Mistral 7B, not a massive general-purpose one. This container is deployed on AWS Lambda, which scales from zero to hundreds of concurrent requests. This means your compute cost for processing 1,000 documents a month is under $30, not the $1,200/month for an always-on GPU server.
Data is processed entirely in-memory. The API receives a document, performs the analysis, and returns the result in under 900ms. The source data is never written to disk in the processing environment. We use `structlog` to send structured logs for every request to a Supabase table. This creates an immutable audit trail showing who ran a query and what the AI decided, which is critical for compliance.
Access is controlled via role-based access controls (RBAC) integrated with your existing identity provider, such as Google Workspace, using OAuth2. A paralegal can run contract summaries, but only a partner can view the full audit history. This prevents unauthorized data access and provides clear accountability for every action taken by the system.
What Are the Key Benefits?
Production-Ready in 3 Weeks
From infrastructure setup to a live endpoint in 15 business days. Your team can start using the system immediately, not after a long implementation project.
Pay for Execution, Not Idle Time
Our serverless architecture means you pay only for the milliseconds the code runs. Hosting costs are often under $50/month, with no fixed server fees.
You Receive All the Source Code
We deliver the complete application code, Docker files, and infrastructure scripts in your private GitHub repository. You are never locked into our service.
Alerts on Any Operational Anomaly
We configure CloudWatch alarms to notify a Slack channel if error rates exceed 1% or if processing time spikes. You find out about issues before your users do.
Connects to Your Private Data
The system runs inside your cloud and can be granted secure, direct access to your S3 buckets or internal databases without exposing them to the internet.
What Does the Process Look Like?
Week 1: Architecture and Security Review
You grant us limited IAM access to your cloud account. We deliver a detailed architecture diagram and security policy defining all resources and permissions.
Week 2: Core Application Build
We develop the FastAPI application and containerize the AI model. You receive access to the private GitHub repository to review the code as it's written.
Week 3: Staging Deployment and UAT
We deploy the system to a staging environment within your account. You receive an API endpoint and documentation to perform user acceptance testing.
Week 4: Handoff and Monitoring
After your approval, we deploy to production. You receive a final runbook, and we begin a 30-day period of active monitoring and support.
Frequently Asked Questions
- What factors determine the project cost?
- The primary factors are the complexity of the AI task and the number of data integrations. A single document summarizer pulling from an S3 bucket is straightforward. A multi-step workflow that needs to read from a database, call an external service, and write to a CRM requires more development time. We provide a fixed-price quote after our initial discovery call.
- What happens if the AI system goes down?
- The system is deployed across multiple AWS Availability Zones for high availability. If the Lambda function produces an unhandled error, it fails gracefully and logs the full traceback to CloudWatch for debugging. We set up PagerDuty alerts for critical failures, with a 2-hour response time included in our initial 30-day support period.
- How is this different from using AWS SageMaker?
- SageMaker is a platform for data science teams to manage complex training jobs and model deployments. Our approach uses serverless tools like AWS Lambda to package a specific model for a specific task. This dramatically simplifies the architecture, reduces operational overhead, and eliminates the cost of idle, dedicated model-hosting endpoints, which is more suitable for SMBs.
- Will you use our data to train the model?
- No. For most tasks, we use powerful pre-trained open-source models that do not require training on your data. If a project does require fine-tuning a model for your specific needs, that process happens entirely within your own cloud environment. The resulting custom model is your intellectual property, and your data is never sent to us or any third party.
- How do we make changes to the system later?
- You own the complete source code in your GitHub repository. The system is deployed via an automated CI/CD pipeline. Any Python developer can make changes by submitting a pull request. We document the entire process in the runbook delivered at the end of the project. We also offer monthly retainers for ongoing development and maintenance.
- What kind of performance can we expect?
- Performance depends on the model and data size. For a typical 2-page document analysis on AWS Lambda, the initial 'cold start' request takes about 4 seconds. Subsequent 'warm' requests process in under 800 milliseconds. For workflows requiring consistent, low-latency responses, we can configure provisioned concurrency to eliminate cold starts entirely.
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