We build inside your infrastructure, behind your firewall. Your data never touches a shared environment, never trains a public model, and never leaves your control. Here's exactly how.
Your security team has rejected AI tools before. The data has to leave your network to reach the vendor's API. The vendor's privacy policy has vague language about "improving our services" that your legal team can't sign off on. There's no audit trail showing exactly what data was sent, what was returned, and whether anything was cached. The compliance team can't approve it because there's no way to demonstrate data residency or retention controls to auditors.
This is the fundamental reason most enterprise AI initiatives stall. The technology works, but the security architecture doesn't pass review. We designed our entire operating model around this constraint from day one.
Forward-deployed engineering means we build inside your environment, on your cloud instances, using enterprise-tier APIs with contractual zero-data-retention guarantees. Your CISO doesn't have to trust our word — they can verify the architecture directly because it runs on infrastructure they control.
Every deployment runs on your own cloud environment. AWS Bedrock for model access with zero-data-retention guarantees baked into Amazon's enterprise terms. Azure OpenAI Service for organizations on the Microsoft stack, with the same contractual data isolation. GCP Vertex AI for Google Cloud environments. We don't run our own infrastructure — we deploy on yours, which means your security team has full visibility and control over every component.
When an agent needs to call a foundation model, the request goes from your infrastructure to the enterprise API endpoint on your cloud provider — it never transits through our systems or any third-party middleware. Model responses return to your environment and are processed there. Intermediate results, agent state, and all operational data persist in your databases. We have access to your systems during the engagement through your IAM — when the engagement ends, you revoke the access.
For organizations with the strictest requirements, we also deploy fully private model instances using open-source models (Llama, Mistral) running on dedicated GPU instances in your VPC. Zero external API calls. The models run on hardware you control, in a region you choose, with no data leaving your network under any circumstance.
AWS Bedrock, Azure OpenAI Service, and GCP Vertex AI all provide enterprise-tier APIs with explicit, contractual guarantees that your data is not used for model training, not cached beyond the immediate request, and not accessible to other customers. These aren't opt-out toggles buried in a settings page — they're standard terms of the enterprise agreements.
We can provide your legal and compliance teams with the specific contractual language from each provider, mapped to the relevant regulatory frameworks you operate under (SOC 2, HIPAA, GDPR, PCI DSS, CCPA). We've done this for every engagement. Your legal team won't be the first to ask.
Full audit trail coverage, access controls, encryption at rest and in transit, incident response procedures. We can provide our architecture documentation mapped to SOC 2 trust service criteria before the engagement begins.
For healthcare deployments: BAA coverage through cloud provider enterprise agreements, PHI data isolation, minimum necessary access principles enforced at the agent level, and complete audit trails for every access event.
Data residency controls through region-specific cloud deployments. Right-to-deletion support built into the data architecture. No cross-border data transfers unless explicitly configured. Processing agreements and DPIAs available.
For financial data processing: network segmentation, encryption standards, access logging, and cardholder data isolation. Payment processing agents operate in isolated environments with no persistent storage of card data.
For government and government-adjacent organizations: deployments on GovCloud (AWS) or Azure Government with FedRAMP-authorized services. We've architected for these environments and understand the control requirements.
Many enterprises have internal security frameworks that go beyond standard certifications. We work with your security team to map our architecture against your specific control requirements and address gaps before deployment.
If your security, compliance, or legal team wants to review our architecture before you commit to a conversation, that's expected. We'll schedule a separate session with your technical team — no salespeople in the room, just our engineer walking through the data flow diagrams, the access control model, the encryption standards, the audit logging architecture, and the specific contractual guarantees from each model provider.
We can also provide written architecture documentation, completed security questionnaires (SIG, CAIQ, custom), and reference contacts at existing clients who have been through the same review process.