AI Strategy & Advisory
Decide where AI pays off, then deliver it.
We help leadership teams separate promising AI ideas from expensive distractions. Our advisory work is grounded in what we build, so every recommendation comes with a realistic path to production.
Sound familiar?
The problems we solve
Too many ideas, no priorities
Every department wants AI, and there is no shared way to choose.
Unclear readiness
Nobody knows whether the data and platform can support the ambition.
Governance gaps
Boards and regulators asking questions that policies do not yet answer.
Outcomes
What you get
- A prioritized portfolio of AI use cases with value estimates
- A clear view of data and platform readiness
- Responsible AI policies your board and regulators can review
Approach
How we deliver it
- 1
Listen
Interviews with leadership and teams to understand goals and constraints.
- 2
Assess
Data, platform, skills and governance measured against the ambition.
- 3
Prioritize
Use cases scored on value, feasibility and risk, then sequenced.
- 4
Plan
A roadmap with budget, team and the first project ready to start.
Deliverables
What we hand over
Working software, tests and documentation in your repositories and cloud accounts.
Technologies
- EU AI Act
- NIST AI RMF
- ISO/IEC 42001
- AI readiness assessment
- Data, platform, skills and governance assessed against your goals.
- Use-case portfolio
- Scored and sequenced use cases with business cases.
- Roadmap
- A 12–18 month plan covering platform, people and delivery.
- Responsible AI framework
- Risk tiers, review processes and policies aligned to emerging regulation.
Use cases
Where this makes a difference
Board-level AI strategy
A clear position, investment case and risk posture for the board.
Generative AI acceleration
Moving from scattered pilots to a governed portfolio.
AI regulation readiness
Inventory, risk classification and controls for AI systems in use.
FAQ
Common questions
Is this just a slide deck?
No. Every engagement ends with a first project scoped and ready to build, and we are happy to build it.
How long does it take?
Typically three to six weeks, depending on the number of business units involved.
Related services
All services →Machine Learning & MLOps
Forecasting, scoring and optimization models taken from notebook to production, with monitoring, retraining and governance.
Models from notebook to production
Generative AI & Agents
Retrieval-augmented assistants and AI agents built on your own data and systems, evaluated rigorously and kept under human control.
RAG assistants and agents on your data
Data Engineering
Reliable batch and streaming pipelines that move data from every source into your platform, tested and observable from day one.
Batch and streaming pipelines you can trust
Tell us where your data is today.
A 45-minute conversation with a senior engineer. You leave with an honest view of what to build first, whether or not we work together.