Who This Is For
You operate in a regulated industry, and sending customer, patient, or financial data to a third-party model API is a conversation your compliance team does not want to have.
Your intellectual property is the business, and the prompts and documents flowing through AI are the most sensitive thing you own.
Your AI usage has reached the volume where per-token pricing is a line item leadership notices, and a fixed-cost deployment would pay for itself.
You need models that know your domain: fine-tuned on your terminology, your documents, your history, and kept current on your schedule.
You want the option to switch models and providers without rewriting every application that depends on them.
Common in financial services, healthcare and life sciences, legal, defense-adjacent manufacturing, and any company whose product is its data.
What You Get
Production model serving inside your perimeter, with the open-weight or licensed models that fit your workload.
Retrieval and fine-tuning pipelines over your own data, so answers are grounded in your documents, not the public internet.
A gateway layer your applications call, so teams build against one interface while you control which model runs behind it.
Data-governance guardrails: access control, redaction, retention, and audit logging designed with your compliance requirements.
Capacity planning and cost modeling you can defend, whether that means GPUs you own, reserved private-cloud capacity, or both.
Architecture Options
Three proven patterns. The right one depends on your data classification, your volume, and what your team can operate.
Dedicated capacity inside your own cloud account and network.
- •Managed or self-hosted inference on reserved GPU instances in your VPC
- •Private networking to your data stores and identity provider
- •Fastest path to a first workload, typically 4 to 6 weeks
Best fit: Best when you already run production in a major cloud and need data residency and isolation without buying hardware.
Models running on hardware in your data center or colocation facility.
- •Hardware sizing, procurement guidance, and cluster setup
- •Inference serving, model registry, and observability on your own stack
- •Operations runbooks for the team that will run it
Best fit: Best for strict sovereignty requirements, air-gapped environments, or sustained high volume where owned hardware is cheaper.
Sensitive workloads stay private; everything else routes where it is cheapest or best.
- •Policy-based routing by data classification and workload
- •One gateway for applications, multiple backends behind it
- •Graceful fallback and cost controls across providers
Best fit: Best when most work can use a frontier API but specific data classes or workloads must never leave your control.
Not sure which pattern fits?
Bring your data-classification rules and your current AI spend to a working session. We will sketch the architecture and the cost model with you.
Example Outcomes
Anonymized scenarios that show the shape of the work and the kind of results to expect.
A private AI stack for regulatory-document analysis replaced three manual review stages, reduced compliance turnaround by 60%, and kept all data inside the firm’s sovereign cloud environment.
Clinical-documentation models were deployed on dedicated cloud-private capacity with de-identified training data and audit logging, giving the security team the controls they needed to approve rollout.
Moving high-volume document extraction from a metered API to on-prem inference cut the per-document cost by an order of magnitude and removed an external dependency from the production line.
What We Deliver
Architecture decision record covering model choice, hosting pattern, and cost model
Deployed inference environment with serving, scaling, and monitoring
Retrieval and fine-tuning pipelines over your data, with evaluation harness
Application gateway and access controls integrated with your identity provider
Data-governance controls: redaction, retention, and audit logging
Operations runbooks, capacity plan, and upgrade path
Training and hand-off for your platform team
How We Collaborate
A private stack is infrastructure your team will run for years. We build it with your platform and security engineers so the hand-off is real.
Typical stakeholders: a platform or infrastructure lead, a security or compliance owner, and the application team that will be the first consumer.
Sensitive data: we design with synthetic or de-identified data wherever possible and follow your data-handling rules from the first day. Where a workload involves protected health information, we work under the boundaries described in our AI transparency and healthcare policies.
After launch: you own the environment, the models, and the runbooks. We stay available for upgrades, new workloads, and model refreshes.
Keep the data. Keep the control.
Start with one workload that cannot leave your perimeter. In a few weeks it will be running on infrastructure you own, with the governance your auditors expect.
Book a Working Session