A useful generative-AI system depends on more than a model. The workflow connects a defined user need with governed knowledge, controlled generation, human review and measurable performance.
01
Need
Users and decisions
→
02
Knowledge
Approved sources
→
03
AI workflow
Retrieval and generation
→
04
Review
Evidence and safeguards
→
05
Result
Evaluated assistance
Figure: Responsible generative-AI workflow
The work begins with the people who will use the system and the decision it must support. Approved sources and access rules determine what the system may use. Retrieval and generation then produce an answer that remains linked to evidence. Human review and evaluation test accuracy, usefulness and unacceptable failure modes before wider adoption.
Who this is for
Research groups, universities and organisations exploring a justified AI use case.
The problem we help solve
A promising demonstration is not yet a reliable solution. The real work is defining the decision, evidence, data, human controls and evaluation criteria.
How KCS approaches the work
Work progresses through four connected stages, from clarifying the need to producing a result that can be reviewed and used.
Clarify the users, decision and unacceptable failure modes.
Review data, knowledge sources, privacy and technical constraints.
Design the AI workflow, human verification and evaluation plan.
Prototype, test and document limitations before adoption.
Concrete examples
A research assistant that answers from an approved document collection and shows its sources.
A multilingual assistant evaluated separately for each language and use context.
What you can receive
A defensible AI use case
An evaluation and risk plan
A prototype or implementation roadmap
ENGAGE WITH KCS
Could this expertise help your organisation?
Tell KCS about the situation, the people affected and the result you need. We will determine whether this expertise fits and recommend a sensible first step.
Your enquiry will reference: Generative AI and Large Language Models.
