We design, deploy, secure, and operate production AI systems inside your infrastructure — including fully private and air-gapped environments where your data never leaves your control.
Healthcare · Financial Services · Manufacturing · Defense · Government · Legal
Deployed on-premise, in private cloud, and in air-gapped environments
Vendor-neutral — we resell no models or platforms
Security architecture from week one, not at the review gate
Engagements scoped to production systems, not pilots
The demo works. Then it meets the organization.
The blocker is rarely the model. It is everything the model has to survive: the data it needs, the systems it has to reach, the review it has to pass, and the team that has to run it at two in the morning. These are engineering and organizational problems, and they are where AI programs go quiet.
Traditional application review assumes deterministic behavior and a known data path. A system that reads privileged documents and generates novel output fits none of that. With no framework to evaluate it, the safe answer is no.
Retrieval quality is a data engineering outcome, not a model outcome. Permissions spread across six systems, documents with no consistent structure, and no source of truth will cap accuracy regardless of which model you choose.
Most pilots are evaluated by demo. Without an evaluation set, acceptance criteria, and regression testing, "is it good enough to deploy?" has no answer — so it does not get deployed.
A system that cannot write back to the EHR, the ERP, the case management system, or the ticketing queue creates work instead of removing it. Users abandon it within weeks.
When regulated data would be processed by a third party, procurement and legal timelines outlast the project's political capital — and the architecture is usually the thing that should have changed.
Models deprecate. Prompts drift. Costs move. Without ownership, observability, and a lifecycle process, a working system quietly degrades until it is switched off.
Every one of these is solvable. None of them is solved by picking a better model.
Most organizations do not need another model. They need someone accountable for making AI work inside the business — the architecture, the infrastructure, the integrations, the security posture, and the operations that keep it running once it is live.
That is the entire scope of our practice. We are an engineering firm. We are measured by whether the system is in production, being used, and passing audit.
Data residency, latency, regulatory exposure, and cost profile all change depending on where inference happens. We model the options against your actual constraints and recommend one — including recommending a managed API when that is genuinely the right answer.
| Managed API | Private Cloud | On-Premise | Edge / Air-Gapped | |
|---|---|---|---|---|
| Data boundary | Third-party processor | Your VPC / tenant | Your datacenter | Your device or facility |
| Regulated data | Requires DPA & review | Often approvable | Fully controlled | Fully controlled |
| Latency | Network-dependent | Low | Low | Lowest |
| Cost profile | Per token | Reserved + usage | Capital + operating | Capital |
| Ops burden | Minimal | Moderate | High | Moderate |
| Best fit | Non-sensitive, variable volume | Most enterprise workloads | Regulated, high sustained volume | Disconnected, real-time, classified |
The analysis decides, not the practice area — a managed API is the right answer for plenty of workloads, and we will say so.
AI readiness assessment, portfolio prioritization, deployment model selection, business case and TCO modeling, and roadmap development.
Reference architecture, model selection, retrieval and knowledge system design, agent architecture, and integration with the enterprise systems you already run.
Model serving on your hardware or in your tenant, GPU infrastructure, network isolation, air-gapped deployment, and edge inference.
Kubernetes and OpenShift AI platforms, inference optimization, multi-tenancy, autoscaling, cost governance, and internal developer platforms for AI.
Threat modeling, AI security review, data isolation, access control, red teaming, audit evidence, and governance aligned to NIST AI RMF and ISO/IEC 42001.
Evaluation harnesses, observability, drift and regression monitoring, model lifecycle management, incident response, and cost control.
Five phases. Defined artifacts. A gate before anything reaches production.
| Phase | What happens | Artifact |
|---|---|---|
| 01 Assess | Readiness, data, security constraints, use case prioritization | Readiness report & prioritized roadmap |
| 02 Architect | Reference architecture, deployment model, model selection, threat model | Architecture package & security design |
| 03 Prove | Bounded build against an evaluation harness with pre-agreed acceptance criteria | Working system & measured results |
| 04 Productionize | Hardening, integration, CI/CD, security review, documentation | Production system & audit evidence |
| 05 Operate | Observability, evaluation in production, lifecycle, governance reporting | Runbooks & operating cadence |
Acceptance criteria are agreed in phase 02, before development starts. If a use case cannot meet them, we say so then — not after the budget is spent.
The organizations we build for operate under HIPAA, GLBA, CMMC, FedRAMP, ITAR, and attorney-client privilege. For them, "send it to an API and see what happens" was never available. We design from the data boundary outward: where data lives, who can reach it, what crosses the perimeter, and what evidence the audit will require.
On a fully private deployment, there is no third-party data processor in the architecture. That does not make the security conversation easier — it makes it answerable.
Inference inside your network boundary. No training on your data, no third-party retention, no egress you did not approve.
Retrieval respects existing entitlements. A user cannot retrieve through AI what they could not open directly.
Prompt injection, data exfiltration, tool abuse, and jailbreak testing against the deployed system, not the model in isolation.
Logging, lineage, and decision records structured for the frameworks your auditors actually use.
PHI-safe clinical and operational AI under HIPAA.
Model risk, explainability, and controls that survive examination.
Edge inference on the plant floor, connected or not.
CMMC and ITAR-aligned deployment, including air-gapped.
FedRAMP-aligned architectures and public accountability.
Privilege-preserving retrieval across matter files.
We hold no reseller agreements and take no vendor commissions. We work fluently across the enterprise AI stack and recommend against your requirements, not our margin.
We do not run pilots with no path to production. We do not do staff augmentation. We are not an AI content agency and we do not build chatbots as a product.
A structured evaluation of your data, infrastructure, security constraints, and candidate use cases — delivered as a prioritized roadmap with a recommended deployment architecture. You will know what is viable, what it costs, and what it takes to run it, before committing to a build.
Direct response from an engineer, not a sales queue. Typically within one business day.