Specialist software embodies relationships that a general model may not know: unit conventions, allowed combinations, calculation methods, approval states, and terminology with precise meanings. Bringing AI into that platform requires a representation of those constraints.
The model’s useful role may be explaining a result, helping locate a procedure, or preparing inputs. The authoritative calculation and record lifecycle should remain in software that can be tested against known outcomes.
Selected for the workload—not prescribed as a single mandatory stack. Explore the technology ecosystem ↗
Explaining a formulation without inventing a calculation
An expert asks why a proposed batch differs from a previous one. The assistant retrieves the permitted formulation versions, reads the relevant inputs, and calls the platform’s existing comparison and calculation functions.
It explains the differences using the returned values and source records. If units are inconsistent or a required input is missing, the tool returns a validation error. The assistant should explain that error rather than guess a conversion or fill the missing value.
Any revised formulation remains a draft. Review and approval use the platform’s established roles, preserving a traceable distinction between AI assistance, calculated information, and an approved production record.
Expert question + domain records + validated functions
Inspectable explanation or a draft for qualified review
A domain tool is a contract with meaning
A typed numeric input is not enough. The contract also needs units, permitted ranges, record versions, and the meaning of failure. Exposing a general calculator gives a model too much freedom where a named, validated domain operation would be safer.
Test against expert-created cases, including unusual but valid combinations and cases that must be rejected. The evaluation should examine both the assistant’s explanation and whether it used the trusted function with the correct context.
Start with the domain model
We identify the entities, terminology, constraints, and accepted procedures that define the application. This determines what information is retrieved, what tools are exposed, and which outputs need expert verification.
Keep calculations in trusted code
A model can explain a calculation or gather its inputs, but important numeric operations should use tested functions and validation. The interface should distinguish calculated results from generated commentary.
Choose adaptation deliberately
Retrieval can supply current knowledge; fine-tuning can improve repeatable task behavior; a smaller specialist model may suit a bounded task. We compare approaches rather than assuming a larger general model is the best fit.
Design for expert users
Specialists need evidence, provenance, editable assumptions, and a clear way to challenge an output. We make AI assistance part of the existing workflow so it does not create a parallel process that people must reconcile.
Choose the approach for the constraint
| When this matters | An approach to consider | What not to assume |
|---|---|---|
| The task involves a trusted calculation | Call the validated domain function | Do not use generated arithmetic as the system of record. |
| Specialist knowledge changes | Retrieve versioned source material | Model adaptation does not replace source governance. |
| The outcome affects a consequential workflow | Preserve qualified review and platform approvals | An explanation is not authorization to proceed. |
The boundary we keep explicit
In regulated or safety-sensitive domains, assistance does not substitute for qualified judgment, validated software, or required approvals.
What a useful evaluation should reveal
Evaluate this workload against representative examples and agreed consequences—not just a convincing response. The review should make these dimensions visible:
- Domain-specific task quality
- Correct use of rules and tools
- Expert review effort
- Consistency across difficult cases
Where this approach fits
- Technical knowledge assistance inside specialist software
- Guided preparation of domain-specific records
- Explaining calculations and operational exceptions
A considered first step
Choose a bounded expert task and build an evaluation set with domain specialists. Test assistance against established procedures before extending it into higher-impact decisions.
Serving enterprise teams in California, Atlanta, Georgia, and across the United States.
Discuss your requirementsQuestions worth resolving
Does domain AI require a custom LLM?
Not by default. A well-designed combination of retrieval, application context, and trusted tools may be sufficient. Model adaptation becomes a separate experiment when there is evidence it is needed.
Can AI be added to our existing product?
Yes. We assess the application architecture, data access, identity model, and user journeys to find an integration point that preserves the platform’s established controls.