Point of view
AI & GenAI
AI & Generative AI
Turn AI ambition into measurable outcomes — from strategy and use-case discovery to production-grade machine learning, LLM applications, AI agents and intelligent automation.
Overview
Most organizations no longer ask whether to use AI — they ask where it will pay off, how to deliver it safely and how to move beyond pilots. We help you answer all three.
Our AI teams combine business consultants, data scientists and machine learning engineers who have taken models and generative AI applications into production. We start from the decision or workflow you want to improve, prove value quickly, and then engineer solutions that are reliable, secure and maintainable.
Whether you need a complete AI program, a focused proof of value or experienced AI consultants to strengthen your own team, we meet you where you are.
Capabilities
What we deliver
Select a capability to see deliverables, technologies and FAQs.
01
AI strategy & roadmaps
A prioritized AI portfolio tied to business value, an honest readiness assessment and an operating model your teams can run.
Explore 02Use-case discovery & value cases
Structured workshops that surface high-impact opportunities and quantify feasibility, cost and expected return.
Explore 03Machine learning engineering
Forecasting, classification, recommendation and optimization models built for production, not just notebooks.
Explore 04LLM & generative AI applications
Retrieval-augmented generation, copilots, document intelligence and content generation grounded in your data.
Explore 05AI agents & agentic workflows
Agents that use tools, follow policies and hand off to people, with guardrails, evaluation and observability built in.
Explore 06Intelligent automation
Combining AI with workflow and process automation to remove manual effort from high-volume operations.
Explore 07MLOps & LLMOps
Pipelines, evaluation harnesses, monitoring and cost controls that keep models accurate and affordable over time.
Explore 08AI consultants on demand
Experienced data scientists, ML engineers and AI architects who embed with your team for as long as you need them.
ExploreOutcomes
What clients can expect
We measure success by business results, and agree how we will measure them before we start. Open an outcome to see how we deliver it and the measures we track.
A prioritized AI roadmap linked to measurable business outcomes
We tie every initiative on the roadmap to a business owner, a baseline and a target measure. Regular portfolio reviews then shift investment toward what is working and away from what is not.
How we measure it
- Share of AI initiatives with an agreed baseline and owner
- Value realized against approved business cases
- Time from idea approval to funded delivery
Faster time from idea to production
Proven delivery patterns, reusable components and production criteria agreed before building remove much of the friction between pilot and launch. Teams spend less time rebuilding the same foundations for each new use case.
How we measure it
- Elapsed time from proof of value to production release
- Proportion of pilots that reach production
- Reuse of shared components across use cases
Generative AI grounded in your data and governed by design
We build retrieval, access control, evaluation and human oversight into every solution from the first sprint. Users get answers they can verify, and risk teams get the evidence they need.
How we measure it
- Answer accuracy and groundedness on evaluation sets
- User adoption and repeat usage
- Escalations and incidents per thousand interactions
Lower operating costs through intelligent automation
We target high-volume, manual processes and redesign them around automation, AI and focused human review. Operational dashboards show where work flows smoothly and where it still gets stuck.
How we measure it
- Cycle time and cost per transaction
- Straight-through processing rate
- Manual effort hours redeployed to higher-value work
Internal capability that keeps improving after we leave
We pair with your people throughout delivery, document what we build and leave behind standards and reusable assets. The goal is a team that can run and extend AI solutions without us.
How we measure it
- AI solutions owned and maintained in-house
- Internal staff actively contributing to AI delivery
- Release frequency after handover
Approach
How we deliver
Select a step to see the activities, deliverables and typical timing.
Step 1 · 2–4 weeks
Discover
Align on goals, assess data and platform readiness, and select the use cases with the clearest path to value.
We work with business leaders, technology owners and risk stakeholders to understand objectives and constraints. We review data sources, platforms and existing AI efforts, then score candidate use cases on value, feasibility and risk.
Activities
- Stakeholder interviews and discovery workshops
- Data and platform readiness review
- Use-case scoring for value, feasibility and risk
- Definition of production criteria and success measures
Deliverables
- Readiness assessment
- Prioritized use-case shortlist
- Proof-of-value plan with success criteria
Step 2 · 4–8 weeks
Prove
Build a focused proof of value in weeks, with success criteria agreed up front and real users in the loop.
We build a working solution on real data, scoped to answer the questions that matter for an investment decision. Real users test it, and results are measured against the criteria agreed during discovery.
Activities
- Rapid build on representative data
- Evaluation set creation and testing
- User testing and feedback sessions
- Cost, risk and architecture assessment for production
Deliverables
- Working proof of value
- Evaluation results against agreed criteria
- Go or no-go recommendation with a production plan
Step 3 · 6–12 weeks
Productionize
Engineer for security, scale and reliability: integration, evaluation, monitoring and controls.
We harden the solution for real-world use by integrating with source systems and identity, adding automated testing and monitoring, and completing security and governance reviews. Release is staged so issues surface with a small group before wider rollout.
Activities
- Integration with enterprise systems and identity
- Security, privacy and risk reviews
- CI/CD, monitoring and cost controls
- Staged rollout with user training
Deliverables
- Production-ready solution
- Operational runbooks and monitoring dashboards
- Governance and model documentation
Step 4 · Ongoing, typically in quarterly waves
Scale
Roll out across teams, establish reusable components and transfer knowledge to your people.
Once the first solution is proven in production, we extend it to new users and use cases and turn common elements into shared components. We also help you build the skills and operating model to run the AI portfolio yourselves.
Activities
- Rollout to additional teams and regions
- Reusable component and platform development
- Enablement, pairing and training
- Portfolio review and next-wave planning
Deliverables
- Shared AI components and patterns
- Trained internal team and operating model
- Next-wave roadmap
FAQs
Questions we often hear
Can’t find what you’re looking for? Browse all FAQs or ask our team.
We have tried AI pilots before. How is this different?
We define production criteria, including data access, security, evaluation and ownership, before we build. A successful pilot then has a clear, agreed route to go live, and an unsuccessful one is stopped early with the lessons captured.
Do you work with a specific AI platform or model provider?
No. We are technology-agnostic and commonly work with Azure, AWS and Google Cloud, as well as models from OpenAI, Anthropic, Google and open-weight providers. We recommend options based on your requirements, existing investments, data residency and cost.
How long does it take to get an AI solution into production?
A focused proof of value typically takes four to eight weeks, and taking a proven solution into production usually takes a further six to twelve weeks. Timelines depend mostly on data access, integration complexity and the level of risk review required.
How do you price AI engagements?
Discovery and proof-of-value phases are usually fixed in scope and fee, so you know the investment before committing further. Build and scale phases can be priced as fixed-scope work, time and materials or a dedicated team on a monthly basis, depending on how well defined the scope is.
How do you keep our data secure?
We work inside your environment wherever possible, under your access controls and security policies, and follow least-privilege principles. Sensitive data is minimized, masked or kept within your cloud tenancy, and we confirm model providers' data retention and training terms before any data is sent to them. Governance controls are designed in from the start, aligned with frameworks such as the NIST AI RMF and, where relevant, the EU AI Act.
Can you work alongside our in-house data science or engineering team?
Yes, and most engagements do. We can lead delivery, co-deliver in a blended team or provide specialists who join your squads. Pairing and documentation are built in so your team owns the result.
Can you provide AI specialists to join our existing team?
Yes. Through our talent solutions practice we place experienced AI and ML practitioners on a contract, project or permanent basis, each assessed by our own AI engineers.
How do we get started?
Most clients start with a short conversation about goals and constraints, followed by a discovery phase or a use-case workshop. If you already have a clear use case, we can move straight to scoping a proof of value.
Insights
Related thinking
Explore our other services
Let’s build what’s next, together.
Tell us about your goals. We’ll connect you with the right experts, usually within one business day.