Service

Data & AI Strategy

A defensible target architecture, a costed roadmap and a clear sequence — so the next twelve months of investment survive contact with reality.

Who it is for

A CTO holding competing platform proposals with no objective basis to choose between them
A Head of Data who has inherited an estate they did not design and cannot yet defend
A leadership team with an AI mandate, a budget, and no credible delivery path

Problems we solve

What tends to be going wrong.

The strategy is a deck, not a decision: Ambition is documented, options are listed, and nothing is actually chosen. Six months later the same three platforms are still under evaluation and the market has moved.
The roadmap has no cost model: A sequence of initiatives with no run-cost, no capability assumption and no dependency map. It survives until the first invoice, then it is quietly abandoned.
AI ambition without a data foundation: An AI programme sitting on data nobody can classify, access nobody can justify, and quality nobody measures. The pilots work. Nothing after them does.

How we help

What Duspat delivers.

Data maturity assessment: An honest read of where you actually are — platform, pipelines, governance, quality and capability — measured, not self-reported.
AI opportunity discovery and readiness: Which use cases are worth doing, which are worth doing first, and which of them your data and controls can currently support.
Target architecture and platform direction: A target state with the trade-offs named and costed, and a decision log that records why the rejected options were rejected.
Governance model and delivery roadmap: The operating model for data and AI governance, and a sequenced roadmap that respects your actual capability rather than an idealised one.

Typical deliverables

Things you can hold, review and hand to a team.

Typically a three to six week advisory sprint. It ends with an architecture pack, a costed roadmap and a decision — not a presentation.

Current-state assessment and data maturity baseline
AI opportunity register, prioritised by value and readiness
Target-state architecture (HLD and LLD)
Costed, sequenced delivery roadmap with dependencies made explicit
Build-versus-buy and platform selection, with a written decision log
Data and AI governance operating model
Investment case in language a board will accept

Technology fit

What we build this on.

We are independent: no reseller agreements and no partner quotas. If your estate points a different way, we will say so.

Cloud: AWS
Azure
GCP
Data platforms: Databricks
Snowflake
Redshift
BigQuery
AI: Claude
Amazon Bedrock
RAG & embeddings

Governance & production readiness

Designed in, not added later.

Governance is scoped during the strategy, while it is still cheap to decide — not deferred to a delivery team who will inherit it as a constraint.

How we run an engagement

Data classification and ownership defined before platform selection
Access model and RBAC direction set at architecture time
Retention, residency and regulatory obligations mapped to the target state
AI risk controls and acceptable-use boundaries agreed with risk and legal
A governance operating model with named owners, not a policy document

Example outcomes

Evidence, not testimonials.

Anonymised and sector-level. Client names are withheld by design — the constraint and the architecture are the parts that carry any information.

Life sciences
A governed RAG system for sensitive research data
The constraint. Retrieval over sensitive research material, where the model must never surface content the user is not entitled to see.
The control model was designed before the model was chosen: entitlement-aware retrieval, IAM boundaries and audit logging built in from the first commit. The result was a proof of concept that could credibly become production, rather than a demonstration that quietly stalled at the security review.
- Amazon Bedrock
- pgvector
- Lambda
- API Gateway
- IAM
- CloudWatch
Regulated data
Data governance in production, not on paper
The constraint. Governance that had been written down but never enforced, across an estate holding personal data.
Metadata cataloguing, lineage, RBAC, retention and automated data quality checks with named owners and a remediation path — so that a governance claim became something a regulator could be shown rather than told.
- Data quality
- Lineage
- RBAC
- GDPR controls
- Monitoring

Related services

Questions

Common questions about data & ai strategy.

What is a data and AI strategy, and what does it actually produce?

A decision, not a deck. You should end up with a target architecture, a costed and sequenced roadmap, a governance model, and a written decision log recording why the rejected options were rejected. If a strategy engagement produces only a presentation, you have bought a summary of what you already knew.

How do you choose between Databricks and Snowflake?

On your workloads, not on a feature matrix. Query patterns, concurrency, the balance of engineering versus analytics work, existing skills and the cost curve at your real volumes all move the answer. We have delivered on both, hold no partnership with either, and will tell you when the honest answer is that it barely matters.

What is the difference between a data strategy and a platform architecture?

A strategy decides what to build and in what order; an architecture decides how. Strategy without architecture is untestable, and architecture without strategy tends to produce something technically excellent that nobody needed. We do both, which is why the sequencing survives contact with delivery.

How do you build a business case for AI investment?

By tying a prioritised use case to a measurable outcome and an honest cost, including the run cost and the governance work most cases omit. We would rather present a smaller case you can defend at the next budget round than a large one that collapses under its first question.

Get an independent read on your data and AI direction.

A first call is technical, not a sales call. Bring a problem, an architecture, or a stalled initiative.

Book a strategy conversation

30 minutes, no pitch deck.