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.
Überblick
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.
Kompetenzen
Was wir liefern
Wählen Sie eine Kompetenz, um Arbeitsergebnisse, Technologien und FAQs zu sehen.
01
AI strategy & roadmaps
A prioritized AI portfolio tied to business value, an honest readiness assessment and an operating model your teams can run.
Entdecken 02Use-case discovery & value cases
Structured workshops that surface high-impact opportunities and quantify feasibility, cost and expected return.
Entdecken 03Machine learning engineering
Forecasting, classification, recommendation and optimization models built for production, not just notebooks.
Entdecken 04LLM & generative AI applications
Retrieval-augmented generation, copilots, document intelligence and content generation grounded in your data.
Entdecken 05AI agents & agentic workflows
Agents that use tools, follow policies and hand off to people, with guardrails, evaluation and observability built in.
Entdecken 06Intelligent automation
Combining AI with workflow and process automation to remove manual effort from high-volume operations.
Entdecken 07MLOps & LLMOps
Pipelines, evaluation harnesses, monitoring and cost controls that keep models accurate and affordable over time.
Entdecken 08AI consultants on demand
Experienced data scientists, ML engineers and AI architects who embed with your team for as long as you need them.
EntdeckenErgebnisse
Was Kunden erwarten können
Wir messen Erfolg an Geschäftsergebnissen und vereinbaren vor Projektbeginn, wie wir sie messen. Öffnen Sie ein Ergebnis, um zu sehen, wie wir es erreichen und welche Kennzahlen wir verfolgen.
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.
Wie wir es messen
- 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.
Wie wir es messen
- 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.
Wie wir es messen
- 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.
Wie wir es messen
- 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.
Wie wir es messen
- AI solutions owned and maintained in-house
- Internal staff actively contributing to AI delivery
- Release frequency after handover
Vorgehen
Wie wir es erreichen
Wählen Sie einen Schritt, um Aktivitäten, Arbeitsergebnisse und typische Dauer zu sehen.
Schritt 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.
Aktivitäten
- 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
Arbeitsergebnisse
- Readiness assessment
- Prioritized use-case shortlist
- Proof-of-value plan with success criteria
Schritt 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.
Aktivitäten
- Rapid build on representative data
- Evaluation set creation and testing
- User testing and feedback sessions
- Cost, risk and architecture assessment for production
Arbeitsergebnisse
- Working proof of value
- Evaluation results against agreed criteria
- Go or no-go recommendation with a production plan
Schritt 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.
Aktivitäten
- Integration with enterprise systems and identity
- Security, privacy and risk reviews
- CI/CD, monitoring and cost controls
- Staged rollout with user training
Arbeitsergebnisse
- Production-ready solution
- Operational runbooks and monitoring dashboards
- Governance and model documentation
Schritt 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.
Aktivitäten
- Rollout to additional teams and regions
- Reusable component and platform development
- Enablement, pairing and training
- Portfolio review and next-wave planning
Arbeitsergebnisse
- Shared AI components and patterns
- Trained internal team and operating model
- Next-wave roadmap
Branchen, für die wir arbeiten
FAQs
Fragen, die wir häufig hören
Sie finden nicht, wonach Sie suchen? Alle FAQs ansehen oder fragen Sie unser 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
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