Applied AI, by industry.

Startups, SMEs, Enterprise, Manufacturing, Healthcare, Finance, Retail, Education, and Professional Services. Each has a dedicated practice page covering the specific compliance regime, systems, and use cases for that sector — the Healthcare practice covers HIPAA and EHR integration, Finance covers DORA and the EU AI Act, Manufacturing covers the EU Machinery Regulation and Cyber Resilience Act, and so on. Start with the page matching your primary sector.

The applicable framework changes with the sector, and the engineering controls follow it. Healthcare requires a BAA, audit trails, and minimum-necessary access rules under HIPAA. Finance requires model explainability and decision logging under DORA and the EU AI Act. Education requires FERPA and COPPA controls around student data. Manufacturing requires safety and cybersecurity documentation under the EU Machinery Regulation and Cyber Resilience Act. Professional Services requires architecture that preserves privilege and confidentiality rather than relying on a policy memo. We build to the framework that applies to your sector, not a generic compliance checklist.

Yes. A startup or SME engagement is scoped to runway and budget — a narrow, subscription-scale build rather than an enterprise program. An enterprise engagement is scoped to governance: rolling proven AI out beyond a single team or function without inheriting shadow AI risk, and building the architecture, monitoring, and approval gates that scale requires. Same sector, different engagement shape.

Less than most teams expect. An ambient documentation system needs audio-consent workflows and EHR access, not a data warehouse. Denial prediction needs 12–24 months of claims and remittance history most providers already have. Fraud screening needs transaction history you already log. A complete enterprise data platform is not a prerequisite — we build the integration layer as part of the first project, and later use cases reuse it.

By architecture, not policy. Systems that process PHI, financial, or student records run inside a controlled environment under the relevant agreement (a BAA for healthcare, equivalent data-processing terms elsewhere), prompts and outputs are excluded from vendor training by contract, access follows minimum-necessary rules, and every interaction is logged. We also give staff a sanctioned tool that is better than the workaround — the surest way to stop people pasting sensitive data into a consumer chatbot is to deploy a compliant alternative.

Our standard arc is roughly 90 days from kickoff to measured value on one workflow: baseline and risk assessment in the first weeks, integration and shadow-mode validation in the middle, supervised go-live at the end. Shadow mode — where the system runs alongside existing processes while humans work normally — is what makes the timeline safe, because failure modes surface before any output touches a clinical, financial, or safety-relevant decision.

Tell us both dimensions and we scope the combination directly rather than forcing a single page's engagement model. A healthcare SME gets HIPAA-grade controls sized to an SME budget, not a full hospital-system program. Contact the team with your sector and size and we'll tell you which practice and which controls apply.

Yes — that's the default, not an exception. Healthcare deployments run inside the EHR. Finance deployments run against the core banking platform and existing case-management tools. Manufacturing deployments read from the sensors and MES already on the floor. We do not require a migration to a new platform to start; the integration layer to your existing systems is part of the first project's scope.