Problems we address
- Many use cases, unclear value priorities
- A promising proof of concept that cannot scale into production
- Unreliable data or overly complex governance
- Models that are difficult to integrate, monitor or explain
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Data & AI
From proof of concept to value.
We scope use cases, improve data reliability and bring Data & AI products into production where they need to deliver real value.
Talk to an expertEngagements
- AI Use Case Sprint and portfolio scoping
- Data architecture, modernisation and migration
- MLOps, LLMOps, observability and evaluation
- Data products, pragmatic governance and quality
Expert profiles
- Data Lead
- Data Architect
- ML Engineer
- Data Product Manager
Deliverables
- Prioritisation with a clear rationale
- Target architecture
- Production-ready product
- Measurement framework
What we safeguard
Risks are named before they are managed.
- Hidden data debt
- Uncontrolled inference costs
- Model drift
- Late compliance checks
Expected outcomes
Observable progress. Documented results.
- Faster decisions
- A more stable data pipeline
- A controlled move into production
- Measurable value
Related experience
Context.
Choices. Results.
Selected documented experiences drawing on this area of expertise.
How to get started
A scoping conversation. An honest assessment of the context. A clear next step.
We assess the challenge, urgency, constraints and skills already in place. If Stranovia is not the right answer, we say so.
Define my needsFrequently asked questions
Answers to move forward.
Your context and objectives shape the engagement.
When should you bring in Data & AI expertise?
When use cases need prioritising, data quality holds teams back or an AI prototype needs to become an operational product. Scoping connects expected value, feasibility, costs and risks.
What deliverables can a Data & AI engagement cover?
Depending on the agreed scope: prioritisation of use cases with a clear rationale, a target architecture, a production-ready product or a measurement framework. Engagements can cover data quality, governance, MLOps, LLMOps and evaluation.