Use case discovery
Assess workflows and identify where generative capabilities could serve a defined business need.
- Workflow and opportunity review
- Use case priorities and feasibility
- Prototype scope and success measures
GenAI consulting
Apply generative AI to business workflows.
Identify and evaluate generative AI use cases for knowledge access, drafting, summarization, and other defined tasks. Prototype the application and plan its data connections, integration, and human review.
What we do
Generative AI discovery, prototyping, and integration.
Evaluate the workflow and source information, test a focused application, and review output quality. Define the system connections and human review needed for wider use.
Prototype and evaluation
Test a focused application and examine the relevance, quality, and limits of its outputs.
- Model and application approach
- Prototype with representative examples
- Output review and evaluation findings
Model adaptation
Adapt an approach to the task, including custom development or fine-tuning where justified.
- Task and data suitability review
- Model adaptation where included
- Comparison against evaluation criteria
Workflow integration
Plan and implement how the application connects to existing systems and the people who use it.
- Data and application interfaces
- Human review and workflow requirements
- Integration, testing, and handover
What you get
Generative AI applications evaluated for your workflow.
Turn a defined task into an application your team can evaluate, using relevant information and clear expectations for human review.
A prototype for a defined business use case.
Explore and test a generative workflow for knowledge access, drafting, summarization, or another task with a clear purpose.
- Application scope tied to a workflow need
- A prototype with representative examples
- An approach suited to the task and available data
Relevant context from your business information.
Identify the sources, inputs, and system connections that can make the application useful within your business context.
- Source information and access requirements
- Data and application interfaces
- Model adaptation where justified
Results your team can assess before wider use.
Evaluate generated outputs against the intended task and define where people need to check, approve, or refine them.
- Output review against agreed criteria
- Documented limitations and refinement needs
- Human review and operating responsibilities
How we work
From use case selection to an evaluated application.
Select a workflow, build a prototype, and test outputs against agreed criteria. Refine the approach and integrate the application where included in the engagement.
Choose the application
Review the task, users, source information, and intended outputs.
Prototype
Build a focused version to test the approach in context.
Evaluate and refine
Review output quality, limitations, and human oversight needs.
Integrate and hand over
Connect the agreed application to its workflow and document its use.
Why it matters
Connect generative AI to a defined business workflow.
Generative applications need relevant source information, output evaluation, and human review suited to the intended use.
A focused first scope
Choose a workflow where generated content or answers have a practical purpose.
Relevant information
Determine which approved sources and context the application needs to use.
Reviewable outputs
Define quality checks and human review for the intended task.
Production planning
Consider integration, costs, and operating responsibilities before extending the application.
Where to start
Choose a workflow for generative AI.
Begin with a defined task, accessible source information, and clear criteria for evaluating generated outputs.
Find answers in internal knowledge
Explore a workflow that helps teams retrieve useful answers from approved information sources.
Draft documents for review
Generate first drafts from relevant inputs while keeping people responsible for approval.
Summarize information
Help teams work through lengthy documents or feedback with outputs they can verify.
Explore product concepts
Generate and compare design or content options as inputs to a human creative process.
Questions
Planning your generative AI engagement.
Answers about scope, existing systems, and delivery requirements.
Do we need to train a model from scratch?
No. The approach should follow the task. Existing models, application configuration, or adaptation may be suitable; custom training should have a clear justification.
How do we choose a first use case?
Start with a defined workflow, accessible source information, an owner, and an output that can be evaluated. Discovery helps compare options and identify constraints.
How are generated outputs checked?
Evaluation uses representative examples and agreed criteria such as relevance, completeness, and accuracy. Human review requirements depend on the task and the consequences of an incorrect output.
Can the application use our business information?
The approach depends on the available sources, access permissions, handling requirements, and integration scope. These are reviewed before implementation.
Does a prototype include production deployment?
Not automatically. Prototype, integration, deployment, and operating responsibilities are agreed explicitly so the next stage is clear.
Next steps
Evaluate your generative AI opportunity.
Share the workflow, source information, and intended outputs. We can help define a use case and test its feasibility.