Production AI for regulated environments
We deploy engineering teams that build custom AI and ML systems — compliant from architecture through deployment. Not wrappers around language models. Purpose-built intelligence systems designed for the regulatory reality of your industry and jurisdiction.
The Problem We Solve
The market for AI consulting is full of firms that will build you a proof of concept, present it to your board, and leave you with a demo that fails compliance review. The problem isn't the model — it's that AI in regulated industries requires the compliance architecture to be designed into the system from day one, not retrofitted after the model is already in production. When you deploy AI in healthcare or financial services, every inference decision is potentially subject to regulatory scrutiny. Most AI vendors don't know that. We do.
Our AI deployments are different because our teams understand the regulatory environment before they write a single line of training code. A HIPAA-compliant AI system isn't just an AI system with encryption added — it requires audit logging at the inference level, model explainability that satisfies clinical staff and regulators, and data pipelines that enforce PHI minimization throughout the training and inference lifecycle. These requirements shape the architecture from the beginning.
The failure mode in regulated AI is almost always the same: a vendor builds a model that works in the demo environment, gets approval to deploy in production, and then a month after go-live the model produces outputs that cannot be explained, traced, or audited. No one designed the monitoring system to detect model drift before it causes a regulatory event. No one built the explainability layer that lets a clinician understand why the model flagged a particular patient. No one designed the audit trail that lets a regulator trace a denial decision to the specific model version that made it. The AI works. The AI system does not.
NIST AI RMF is the emerging governance standard for AI systems in healthcare, financial services, and federal agencies. It requires organizations to identify AI risks, establish governance structures, map the AI value chain, and measure AI performance against compliance requirements. Most AI deployments are built without any of these structures in place. Our teams build them in. Every model we deploy ships with a model card, a risk assessment mapped to NIST AI RMF categories, drift monitoring configured against validation baselines, and an explainability layer that satisfies both clinical users and regulatory reviewers.
First call is with a senior engineer. No sales rep. No pitch deck. We tell you honestly whether we can help.
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How Our Teams Approach This Differently
We don't start with model selection. We start with compliance mapping. Before a data scientist writes a single line of training code, our compliance engineers map every data source to its regulatory classification, every inference output to its regulatory implications, and every architectural decision to the control requirements it must satisfy. The model architecture is constrained by compliance requirements, not the other way around. This inverts the typical AI project lifecycle — and it's why our AI systems pass compliance review on deployment day.
Our AI teams deploy with the monitoring infrastructure built in. Drift detection is configured during validation — not added after the first production anomaly. Explainability is an architectural component of the model serving layer — not a post-hoc interpretation tool applied to a black-box model. Audit logging captures every inference with the model version, input features, output, and confidence score. When your compliance team needs to produce evidence that a specific AI decision was made correctly, the evidence is a query, not a reconstruction.
Model governance in regulated environments requires version control, validation documentation, and change management processes that most AI platforms treat as optional. We treat them as engineering requirements. Every model version is documented with its training data sources, validation results, and compliance assessment. Every promotion from staging to production requires sign-off from the compliance engineer assigned to the engagement. Every production model is monitored against its validation baseline — when drift exceeds the threshold, the system flags for review before a regulatory event occurs.
What You Get
At the end of an AI platform engineering engagement, you have a production AI system where every model version is documented with its training data sources, validation results, and compliance assessment. You have drift monitoring configured against the validation baseline — the system will tell you when the model's behavior has deviated enough from its validation state to warrant review, before a regulatory event occurs. You have explainability interfaces that allow clinical, financial, or operational staff to understand model outputs in terms relevant to their domain. You have an audit log that captures every inference decision with the information required to respond to a regulatory inquiry. And you own all of it — source code, model weights, documentation, monitoring configuration.
The compliance documentation package at engagement close includes: model cards for every production model, a NIST AI RMF-aligned risk assessment, the validation evidence package that satisfies FDA SaMD documentation requirements where applicable, and the ALICE configuration that will continue to enforce compliance requirements on every future model update. ALICE doesn't leave with us — it stays in your pipeline, enforcing the compliance standards we established during the engagement on every commit your team makes after we're gone.
How Our Engineers Deliver This
Our AI teams come domain-qualified. They understand your regulatory landscape before they write their first line of code. Compliance is enforced automatically through ALICE at every commit.
Relevant Compliance Frameworks
Engagement Models
Where We Deploy
Build vs. Outsource Decision Framework
A structured framework — with scoring — for deciding whether to build in-house, outsource, or adopt a hybrid model. Adapted for regulated industries where the cost of the wrong decision is highest.