AI that doesn't leak your data.
Real agent capability without your customer data, contracts, IP, or PHI ever leaving your environment. Open-source models running in your VPC. Frontier models via your own cloud account. Fully air-gapped where you need it.
The default question
Where does your data go when it talks to AI?
When you use ChatGPT, Claude.ai, GitHub Copilot, or any direct SaaS AI product, every prompt (and everything in it) travels to that provider's servers. For consumer use that's fine. For regulated data (PHI, FERPA records, financial detail), proprietary IP (manuscripts, source code, contracts), or anything you've promised customers won't leave your environment, it's a non-starter.
Quantilus only deploys agents in environments that meet your data-handling policy. If your data can't leave your VPC, neither do our agents. If you need air-gapped, we ship air-gapped. Period.
Three ways
AI inside your perimeter, three ways.
Pick the deployment model that matches your data-handling policy and your model-quality needs. We've shipped all three.
Open-weight models (LLaMA, Gemma, Mistral, Qwen, Phi) deployed directly inside your VPC or on-prem cluster. Model weights live on your hardware. No external API calls. No telemetry. Nothing leaves your network.
Best for: highly regulated workloads, air-gapped environments, full data control.
Anthropic Claude via AWS Bedrock. OpenAI GPT-class via Azure OpenAI Service. Google Gemini via Vertex AI. Your contract is with AWS, Microsoft, or Google. Their data terms apply, and your data stays inside your cloud account.
Best for: enterprises that want frontier model quality without direct SaaS exposure to model providers.
For defense, classified, and the most sensitive private deployments. Agents running entirely offline. No internet egress. No model-provider relationship at all. Open-weight models, your hardware, your network only.
Best for: government, defense, regulated infrastructure, fully isolated workloads.
You can also mix: high-volume / low-sensitivity workloads on a hyperscaler-hosted frontier model, sensitive workloads on a self-hosted open-weight model. The agent picks the right one per request, transparently.
Compliance
Built for the standards your regulators care about.
Every private-AI deployment ships with the trust layer your industry expects.
Healthcare
HIPAA-aligned deployments. BAAs available. PHI handling, redaction, full audit trail. Inference inside HIPAA-aligned cloud or on-prem.
Education
FERPA-aware. Student records stay scoped to the institution. Disclosure controls. Records access logged and reviewable.
EU & Global
GDPR. Data subject rights, EU residency, processor agreements. Deploy in your region, never cross-border by accident.
Enterprise
SOC 2 Type 2 controls and audits. Customer-side KMS, BYOK, HSM integration. Per-tool permission boundaries.
Government
FedRAMP-aligned deployment patterns. GovCloud regions. Air-gapped configurations for controlled and classified work.
Audit & Review
Full audit trail. Every prompt, every tool call, every agent decision logged with reasoning. Reviewable by your compliance team, exportable for regulators.
What we handle
From model selection through monthly operations.
You don't need an in-house ML platform team. We bring the model layer.
- Model selection. Which open-source or hosted model fits each workflow, tuned for cost vs. quality vs. data sensitivity
- Deployment in your environment. VPC, on-prem, GovCloud, or air-gapped. We ship the infrastructure, you own it
- Inference infrastructure. GPU sizing, autoscaling, request queueing, fail-over to a fallback model
- Monitoring & alerting. Uptime, latency, cost, output quality, drift detection
- Security operations. Access control, secret rotation, audit log retention, vulnerability response
- Model upgrades. Move to newer open-source models or new Bedrock / Azure OpenAI / Vertex versions when they ship
- Cost optimization. Route easier requests to cheaper models, harder requests to frontier models, cache where safe
- Compliance evidence. The artifacts your auditors and customers will ask for
Who this is for
Industries where this isn't optional.
If any of these describe your business, the SaaS-AI default isn't an option for you. Private deployment is the only deployment.
Healthcare
PHI must stay in HIPAA-aligned environments. Patient records, clinical notes, prior authorization: nothing should travel to a model provider.
Financial Services
Customer records, contracts, advisor notes, trading positions, KYC documents. All material non-public information stays in the bank's environment.
Government & Defense
Classified, controlled-unclassified, or sensitive-but-unclassified workloads. Often requires GovCloud or fully air-gapped deployment.
Publishing
Manuscripts, contracts, royalty data, pre-publication editorial: intellectual property that authors and publishers expect to stay private.
Legal & Professional Services
Privileged communications, case material, M&A diligence, client work product. None of it should ever flow to a third-party AI provider.
Education
FERPA-protected student records, transcripts, financial aid, IEP documentation: institutional data that must stay scoped to the institution.
Pharma & Life Sciences
Clinical trial data, IP, formulation work, regulatory submissions. Industry confidentiality rules require strict environment control.
Critical Infrastructure
Energy, utilities, transportation, telecom: operational data tied to physical-world systems where leakage has real safety implications.
Any company under audit
SOC 2, ISO 27001, customer-imposed data agreements. If you've contractually committed to keeping data scoped, you can't quietly send it to a SaaS LLM.
One firm, one approach
Same agents. Private deployment.
Private AI isn't a separate product. It's how we deploy the agent for clients who need it. Every Strategy, Build, Integration, and Operations engagement can be delivered inside your environment, on the model layer that fits your data-handling policy. The agent's behavior is the same. Where it runs is the difference.