Can a general-purpose AI understand nuclear, banking or pharma?

In brief.
A general-purpose generative AI has read most of the web: it knows the vocabulary of every sector and produces plausible text about each. But can it answer a tender in nuclear engineering, a banking prudential file or a pharmaceutical regulatory dossier? Talking about something is not understanding it: these domains demand the logic of the profession, its frameworks, its hierarchies of standards. That is the gap between a syntactically correct answer and a technically accurate one.

What “the logic of the domain” means in practice

In nuclear: knowing that a safety requirement refers to a classification that governs the entire chain of demonstration, and that an approximate word commits you far beyond the paragraph. In banking: distinguishing what falls under prudential rules, compliance and internal control, three worlds that everyday language confuses and that the evaluator never does. In pharma: understanding that a compliance claim is tied to a specific framework, in a specific version, and that document traceability is a fundamental requirement there. On these subjects, a generalist produces fluent paragraphs that an expert immediately sees come from outside the profession.

The answer: specialisation plus grounding

Models trained on the vocabularies, frameworks and logic of a domain, which recognise a sector requirement for what it is. Combined with grounding in the company’s own documents: the specialised model understands the question as a peer would, the customer’s repository provides the evidence. One without the other fails: specialisation without your evidence stays generic in its answers; your evidence without specialisation is misinterpreted.

The Optivalue.ai approach

This is the structural choice made by Optivalue.ai: 89 AI models specialised by function and by sector, combined with each customer’s governed repository. The answer is understood with the logic of the domain and proven with your documents: accurate in substance, defensible in form.

Isn’t a general-purpose LLM well grounded in our documents enough?
It cites correctly but interprets like a generalist: the sector nuance escapes it precisely where it matters most.

‍What does a model specialised by function bring?
Recognition of sector requirements for what they are, and their handling according to the rules of the domain.