A compelling demo without a dependable job
The experience looks fluent, but users cannot tell when to rely on it, how it fits their workflow, or what to do when the response is weak.
02 · AI capability
Generative AI Solutions
Design useful generative AI experiences around grounded context, clear interaction, evaluation, security, and human judgement.
The context
Begin with the work, evidence, and consequence.
A useful generative AI product is more than a prompt and a model endpoint. It needs an intentional job, trusted context, understandable limits, appropriate controls, and a way to evaluate the complete experience against realistic tasks.
Uncontrolled context
Content is incomplete, outdated, sensitive, or poorly permissioned, making grounded answers and responsible access difficult.
Evaluation is anecdotal
A few favourable examples stand in for representative scenarios, failure analysis, safety checks, and ongoing quality observation.
Areas of attention
Connect AI behaviour to a dependable product and operation.
The appropriate techniques, controls, and delivery depth follow the use case. These areas keep the technical work connected to the people and decisions around it.
01
Use-case and interaction design
Define the task, user intent, context, inputs, outputs, confirmation points, feedback, and fallback behaviour.
02
Grounding and retrieval
Prepare approved knowledge, metadata, permissions, retrieval behaviour, citations, and freshness expectations.
03
Model and application orchestration
Connect model capabilities with tools, structured outputs, application logic, identity, limits, and operational controls.
04
Evaluation and observation
Create representative test scenarios, quality criteria, safety checks, traceability, feedback routes, and review rhythms.
Potential outputs
Create evidence and artefacts the team can use.
Outputs are shaped around the decision and engagement stage. Each should have a clear audience, purpose, owner, review criteria, and stated limitation.
- 01Generative AI use-case and interaction definition
- 02Grounding, retrieval, permission, and content-readiness design
- 03Working prototype or bounded product increment
- 04Evaluation set, criteria, findings, and known limitations
- 05Operational guidance for access, monitoring, feedback, and change
Evaluation and responsibility
Make inputs, useful evidence, and boundaries explicit.
AI quality cannot be separated from its context of use. Representative inputs, subject expertise, realistic scenarios, and accountable owners are part of the engineering work.
What we need to understand
- Representative user tasks, questions, and source material
- Content owners, access rules, privacy needs, and retention requirements
- Expected response quality, latency, volume, and cost constraints
- Examples of unacceptable, sensitive, or high-consequence outcomes
Evidence of a useful direction
- The experience supports a defined task and communicates its limits
- Source access and grounding behaviour follow intended permissions
- Representative evaluations cover useful and problematic scenarios
- Human review and fallback are present where consequence requires them
Important boundary
Generative models can produce plausible but incorrect output. Grounding, evaluation, and guardrails reduce risk but do not make every response complete, current, unbiased, or correct.
Working path
Reduce uncertainty before expanding commitment.
The exact sequence depends on the use case and current evidence. Each stage should leave a reviewable result and an informed choice about the next step.
Define the job
Choose a bounded user task and describe the current workflow, desired assistance, and consequence of error.
Prepare the context
Assess approved sources, access, structure, freshness, retrieval, and content ownership.
Prototype and evaluate
Build the smallest useful experience and test it with representative tasks, users, and failure scenarios.
Operationalise
Integrate the validated direction with security, monitoring, feedback, support, and change controls.
AI capability questions
Important details to clarify.
Early questions should expose the task, evidence, uncertainty, and responsibilities that shape a safe and useful implementation.
Can you build a chatbot for our website or team?+
Yes, where a conversational interface fits the task. We first clarify its audience, knowledge boundaries, actions, escalation path, data handling, and how useful answers will be evaluated.
What is retrieval-augmented generation?+
It is an application pattern that retrieves relevant approved information and provides it to a model as context. Its usefulness depends on source quality, permissions, retrieval, prompting, and evaluation.
Can the system cite its sources?+
Often it can present links or references from retrieved material. Citation behaviour still needs evaluation because a reference does not guarantee that every generated statement is supported.
How do you choose a model?+
We compare models against the task, quality criteria, context needs, privacy, security, latency, cost, provider constraints, and the application's ability to change later.
Start a conversation
Let's discuss generative ai.
Share the workflow, evidence, people affected, and the outcome you want. We can help frame a responsible first question and bounded next step.