We build agentic AI systems for industries where every output is reviewed, regulated, and audited. Pharma advertising. Financial services. Healthcare. The governance layer is the product.
Most AI studios build for speed. Regulated industries require something harder: auditability.
Every output routes through a brand knowledge base loaded with approved language, prior MLR decisions, and regulatory guardrails. The agent does not guess; it retrieves.
We design the governance architecture before we write the first prompt. Risk management framework, not an afterthought.
Our pharma advertising engagement began in 2022 and is still running. We do not pilot and exit.
A full-service pharma advertising agency needed AI across five brand teams without disrupting a single active campaign and without a single MLR failure. We built the governance layer first, then the knowledge bases, then the automations.
NIST AI RMF risk mapping, compliance inventory, stakeholder alignment. We define what the system is allowed to do before it does anything.
Brand voice, approved language, MLR precedent, and regulatory guardrails encoded into retrieval layers. One knowledge base per brand team.
N8N workflows, Claude-powered content generation, RAG-grounded retrieval, review-gate routing. Built to your existing MLR cycle, not around it.
NIST-documented deployment, staff training, ongoing operations. We stay in the loop; we do not hand off a repo and leave.
FDA/MLR/PRC compliant content systems, brand knowledge bases, campaign automation.
See the Heller case study →HIPAA-aware agentic workflows, patient-facing content guardrails, clinical AI systems.
Healthcare AI →SEC/FINRA-aware AI, automated reporting, AES-encrypted intelligence platforms.
Financial AI →We build the compliance architecture before we build the automation. Every content output routes through a brand knowledge base loaded with prior MLR approvals and regulatory guardrails. The agent retrieves approved language — it does not generate from scratch. The result is content that enters MLR review already aligned with what has passed before.
The Heller model: five brand knowledge bases encoding approved language and MLR precedent for each brand team, eight active automations handling content workflows, and a NIST AI RMF governance framework that documents every system decision. Campaign launch timelines compressed from three months to two weeks. The CoE is operated by us as a managed service — not handed off.
Yes — with the right architecture. The failure mode most agencies hit is using AI for generation without grounding it in what has already been approved. When the knowledge base comes first, AI-generated content inherits the brand's own MLR history. Review cycles shorten because reviewers are seeing language they have already approved, not new material to evaluate from scratch.
The NIST AI Risk Management Framework is the US government's standard for governing AI systems — covering identification, measurement, management, and governance of AI risk. For healthcare and pharma, it provides the audit trail that regulators and legal teams require when AI is involved in any patient-facing or regulated content workflow. Enso Labs designs to NIST AI RMF from the start of every regulated engagement.
We scope regulated engagements in two weeks. Tell us the brand, the compliance framework, and the workflow you want to automate.
