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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. Does that mean it can answer a tender in nuclear engineering, a banking prudential filing or a pharmaceutical regulatory dossier? Talking about something is not understanding it: these fields demand the logic of the domain, its reference frameworks, its hierarchies of standards. That is the gap between a syntactically correct answer and a technically accurate one.
What “domain logic” means, concretely
In nuclear: knowing that a safety requirement points to a classification that conditions the entire demonstration chain, and that an approximate word commits you far beyond its own paragraph. In banking: distinguishing what belongs to prudential regulation, to compliance and to internal control — three worlds that everyday language conflates and that the assessor never does. In pharma: understanding that a compliance statement attaches to a precise reference framework, in a precise version, and that documentary traceability is a substantive requirement. On these subjects a generalist produces fluent paragraphs that an expert immediately recognises as coming from outside the profession.
The answer: specialisation plus grounding
Models trained on the lexicons, reference frameworks and logics of a domain, which recognise a sector-specific requirement for what it is. Combined with grounding in the company’s own documents: the specialised model understands the question as a peer, the client’s content library supplies the evidence. One without the other fails: specialisation without your evidence remains generic in its answers; your evidence without specialisation is misinterpreted.
The Optivalue.ai approach
This is Optivalue.ai’s structural choice: 89 AI models specialised by function and by sector, combined with each client’s governed content library. The question is understood with the logic of the domain and answered with evidence from your documents: accurate in substance, defensible in form.
Isn’t a general-purpose LLM properly grounded in our documents enough?
It cites correctly but interprets as a generalist: the sector-specific nuance escapes it precisely where the stakes are highest.
What does a domain-specialised model add?
Recognition of sector requirements for what they are, and their treatment according to the rules of the domain.
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