AI opportunity discovery
Map workflows and identify tasks where AI may improve speed, quality, access to information, or automation.
- Workflow review
- Use-case discovery
- Feasibility analysis
- Prioritisation
AI Consulting
Turn AI interest into a buildable plan.
Identify where AI can create practical value, determine what data and architecture the use case needs, and turn the opportunity into an implementation plan.
Inside the delivery
Turn an AI opportunity into an implementation decision
Define the user task, current process cost and business owner before choosing a model. The flow below shows the main delivery stages and the evidence produced at each step.
- 01
Opportunity framing
Define the user task, current process cost and business owner before choosing a model.
Output
Prioritized opportunity brief
- 02
Feasibility review
Inspect available data, integration constraints and the quality expected of an acceptable answer.
Output
Data readiness and feasibility assessment
- 03
Focused experiment
Compare a small evaluated prototype with rules, search or the current workflow.
Output
Prototype findings and baseline comparison
- 04
Delivery recommendation
Record build, buy or defer options with dependencies, evaluation gates and operating responsibilities.
Output
Implementation roadmap and decision record
Controls across the workflow
- Business ownership
- Data access boundaries
- Baseline comparison
- Documented assumptions
Decisions that shape the scope
- Does consulting include a production application?
- The core output is an evidence-based implementation decision and roadmap. A prototype can test assumptions; production integration, support and rollout need an agreed implementation scope.
- What does AI consulting include?
- AI consulting can include use-case discovery, feasibility analysis, architecture, data readiness, model strategy, evaluation planning, implementation roadmaps, and technical due diligence.
Before you commit
Is this the right engagement?
You need to decide whether an AI initiative is feasible and worth implementing before committing to delivery.
- What we need from you
- Candidate workflows, business owners, sample data, current process costs and the decisions AI would support.
- How you accept the work
- Review a prioritized use case, feasibility findings, evaluation plan, architecture options and the assumptions behind the delivery estimate.
- Scope & alternatives
- The core output is an evidence-based implementation decision and roadmap. A prototype can test assumptions; production integration, support and rollout need an agreed implementation scope.
Overview
AI strategy should end in a technical decision
Useful consulting connects business opportunities to specific tasks, data, model behaviour, system architecture, evaluation criteria, risks, and an implementation path.
- 01AI opportunity assessment
- 02Use-case prioritisation
- 03Architecture and model strategy
- 04Data readiness assessment
- 05Implementation roadmap
Capabilities
Engineering scope and deliverables
Select the work that addresses your constraint. Responsibilities and acceptance criteria are agreed before delivery.
AI architecture consulting
Define the technical system around models, retrieval, data, APIs, applications, and evaluation.
- Model strategy
- RAG architecture
- Integration design
- Security boundaries
Data readiness
Assess whether the information required by the AI use case is available, accessible, structured, and governed appropriately.
- Source assessment
- Data access
- Knowledge readiness
- Data gaps
Implementation planning
Translate approved use cases into an incremental engineering and evaluation roadmap.
- Prototype scope
- Evaluation plan
- Production roadmap
- Risk controls
Integrations
Selected for your environment
Tools are chosen around your existing systems, access requirements and operating constraints.
LLM platforms
Cloud platforms
Data warehouses
Knowledge systems
Business applications
Internal APIs
Frequently asked questions
Questions to resolve before starting
01Does consulting include a production application?
Does consulting include a production application?
The core output is an evidence-based implementation decision and roadmap. A prototype can test assumptions; production integration, support and rollout need an agreed implementation scope.
02What does AI consulting include?
What does AI consulting include?
AI consulting can include use-case discovery, feasibility analysis, architecture, data readiness, model strategy, evaluation planning, implementation roadmaps, and technical due diligence.
03Can you help identify where AI should be used?
Can you help identify where AI should be used?
Yes. Workflow analysis can identify tasks where AI may be useful and also where conventional software or automation would be more appropriate.
04Do you provide generative AI consulting?
Do you provide generative AI consulting?
Yes. This can include model strategy, retrieval architecture, evaluation, data readiness, integration, security boundaries, and implementation planning.
05Can consulting lead directly into development?
Can consulting lead directly into development?
Yes. Validated use cases can move into prototype and production engineering without requiring a separate discovery process.
06Can you assess an AI project already in progress?
Can you assess an AI project already in progress?
Yes. Existing architecture, model behaviour, retrieval, data, evaluation, cost, latency, security, and production readiness can be reviewed.
07What should we prepare for the first technical discussion?
What should we prepare for the first technical discussion?
Candidate workflows, current process measures, representative data, existing applications and decision-makers for budget and delivery.
08What evidence is available at handover?
What evidence is available at handover?
The agreed delivery includes implementation roadmap and decision record. Record build, buy or defer options with dependencies, evaluation gates and operating responsibilities.
09How is the engagement estimated?
How is the engagement estimated?
We review the available inputs before estimating: Candidate workflows, current process measures, representative data, existing applications and decision-makers for budget and delivery. The proposal identifies dependencies, review milestones and excluded work; the scope determines the schedule.
Related
Related services and delivery options
Start a conversation
Discuss your next technical step
Share your current situation and the constraint you need to resolve. We will use the discovery inputs above to define a practical scope for AI Consulting.