AI for Data
AI-ready data services: data engineering, semantic layer and analytics
Data your business and your AI can trust.
From fragmented sources to governed answers: one definition for every key metric, data people trust, and AI grounded in your business.
Gartner (Feb 2025): 60% of AI projects without AI-ready data will be abandoned through 2026
AI exposes every gap in your data.
Every system holds part of the answer, the same metric is calculated three ways, and AI can't tell an authoritative source from an old file. Permissions must hold for AI as well as people, and leaders need to see where an answer came from.
- 01Consistent answers
- 02Data people trust
- 03AI grounded in your business
- 04Platforms that earn their cost
Which question needs an answer?
Select a question to see the service that answers it.
Is our data platform right for analytics and AI?
Every workload: keep, consolidate, migrate or optimise.
How we do it
- Workload and cost assessment
- Target platform design
- Migration in controlled waves
What you get
- A clear decision for every workload
- A modern, governed platform
- An optimisation backlog
Can people and AI trust the data?
Data products with quality, lineage, ownership and permission-aware access.
How we do it
- Quality rules and validation
- Master data and lineage
- Data contracts and access controls
What you get
- Trusted, owned data products
- Visible quality and freshness
- Access that respects permissions
Why do reports and AI give different answers?
Business meaning defined once, approved by your owners.
How we do it
- Metric and entity definitions
- Semantic models on your platform
- Owner sign-off for every definition
What you get
- One version of every key metric
- AI grounded in your business
- Reusable context with Aiden K
Can our teams just ask the data a question?
Conversational analytics whose answers show the definition and the source.
How we do it
- Conversational analytics
- Embedded decision views
- Answers with sources and permissions
What you get
- Answers in minutes, not weeks
- Explanations people trust
- Less waiting for reports
Who keeps it reliable once it is live?
Pipelines, quality, usage and cost monitored and traced to the source.
How we do it
- Pipeline and quality monitoring
- Cost and usage tracking
- Root-cause tracing upstream
What you get
- Fewer broken reports
- Controlled platform spend
- Monthly reliability reporting
Business meaning, defined once.
Our AI reads your reports, models and documents and drafts the definitions; your business owners approve them; reports and AI use the same meaning.
- 01DiscoverOur AI reads your reports, models and documents
- 02DraftDefinitions, entities and rules are drafted for you
- 03ApproveYour business owners review and sign off
- 04PublishDefinitions go live on your data platform
- 05Use everywhereReports, dashboards and AI share the same meaning
- definitions
- several versions across teams
- sources
- ERP extracts, spreadsheets, BI models
- owner
- not recorded
- quality
- not checked
- used by
- reports that disagree
FRAGMENTEDthe starting point
- definition
- (net premium − claims − direct costs) ÷ net premium
- grain
- product line · month · business unit
- source
- finance data product (authoritative)
- excludes
- reinsurance recoveries, one-off adjustments
- owner
- commercial finance
- quality
- all checks passing · refreshed daily ✓
- used by
- 14 reports · 2 AI assistants
APPROVEDlast reviewed: September 2026
Shaped by what we learned building Aiden K.
Where to begin
AI data readiness check
One data domain reviewed for quality, meaning and access gaps, and what it takes to support trusted AI answers.
One data domain · Readiness view and next steps
Get startedKnowledge layer pilot
We define the key metrics, entities and sources for one domain and put them to work in reports and AI.
Get startedPlatform assessment
We assess cost, workload fit and the path to a modern, AI-ready platform.
Get startedPlatforms we work with
Databricks
Snowflake- Microsoft Fabric
BigQuery
Power BI
Tableau