AI Agents and Subject-Matter NLP

The gap

Large language models perform impressively on general tasks and unreliably on specialist ones. In medicine, law, engineering and finance, the depth of domain knowledge, the precision of terminology and the consequence of error all rise sharply — and general-purpose prompting produces answers that are fluent, plausible and sometimes wrong in ways a non-expert cannot detect.

That gap is not closed by better general models alone. It is closed by engineering the interface between the specialist's question and the model's capability.

What we build

MCi develops custom prompt agents — systems that sit between subject-matter experts and large language models, structuring queries, supplying domain context, constraining outputs and validating results, so that responses are accurate, relevant and expressed in the language of the field.

Domain data foundation

Assembly and curation of the domain corpus — literature, research, regulation, internal documentation and expert input — that grounds the agent in the terminology, structures and reasoning patterns of the field.

Prompt engineering and agent design

Prompts and orchestration designed to elicit high-quality, context-aware responses: specialist terminology, appropriate knowledge structures, question framing that matches how experts actually reason, and retrieval that grounds answers in source material.

Feedback loops

Continuous refinement using feedback from subject-matter experts and evaluation of model output. The agent improves with use and keeps pace as the domain moves.

Model integration

Integration with the underlying language models, with the orchestration, guardrails, retrieval and validation layers that make output dependable rather than merely fluent.

What it delivers for experts

• Accuracy — responses grounded in the terminology, frameworks and knowledge base of the specific domain
• Speed — expert-level information retrieved in seconds rather than through manual search
• Efficiency — fewer reformulations, and a usable answer on the first attempt
• Scale — agents deployable across fields and teams without building a bespoke system for each
• Knowledge transfer — accelerated onboarding where senior capacity is scarce

Domains we work in

Healthcare

Diagnostic support, treatment literature, research summarisation.

Finance

Financial modelling, investment analysis, risk assessment.

Legal

Document analysis, contract review, legal research.

Engineering

Design solutions, system analysis, technical troubleshooting.

Public sector

Regulatory interpretation, cross-border process, case handling.

How we keep it honest

Grounding and citation so answers can be traced to source. Explicit uncertainty rather than confident invention. Human review designed into workflows where the stakes require it. Evaluation against expert-labelled benchmarks before deployment and continuously after.