Industry-specific AI

A generic copilot ignores how your industry is allowed to use data. We take the same engineering we use for generative and ML work and constrain it to the workflows, systems, and regulations of your sector. Healthcare gets retrieval inside a permitted boundary. Manufacturing gets models that respect OT change freezes. Education gets student-data rules. GCCs get parent-company identity and residency. SaaS product teams get tenant isolation in the RAG layer.

Use cases are chosen with operators, not from a vendor industry slide. We will drop a fashionable use case that cannot be evaluated or that would fail a customer audit. The first release is still a narrow production path: one job, one corpus, one review step.

Integrations target the systems that sector already runs — EHR-adjacent workflows, MES and quality, SIS, enterprise ITSM, product telemetry — rather than inventing a parallel stack. Evaluation includes sector-specific failure costs (wrong clinical summary versus wrong marketing blurb).

You get an AI capability that your compliance and operations people recognise, with the same documentation bar as the rest of Audmins delivery. Industry flavour is in the constraints and the workflow, not in a different logo on the same chatbot. We will not launch a use case that cannot be evaluated by the people who already own quality, clinical, plant, or student-data risk in that organisation.