Data
Data, Analytics & GenAI
Agents are only as trustworthy as the data underneath them. We modernise the foundation — pipelines, governance, a semantic layer agents can query without hallucinating — then layer generative AI and retrieval on top so every agent decision is grounded in something you can point to.
Outcomes
What you get, stated as results.
- A single governed source of truth for agents and analysts
- Generative answers grounded in your data, with citations
- Analytics that drive action, not just dashboards
Capabilities
What this practice covers.
Data modernisation
Consolidate fragmented sources into governed, query-ready foundations agents and analysts can both trust.
Semantic layer
A shared definition of your metrics and entities, so an agent and a dashboard never disagree on what "revenue" means.
Retrieval-grounded GenAI
Generative models answer from your documents and data, with citations — not from training-set guesswork.
Advanced analytics
Forecasting, segmentation, and anomaly detection that feed decisions back into agent workflows.
Data quality & lineage
Automated checks and end-to-end lineage so you know where every number an agent used came from.
Governance & privacy
Access controls, masking, and residency handled at the platform layer, not bolted on per project.
How it works
A sequenced path, not a big bang.
Map the sources
Inventory the data an agent needs and the gaps between it and production quality.
Build the foundation
Pipelines, semantic layer, and governance stood up as reusable platform, not one-off plumbing.
Ground the models
Retrieval and GenAI wired to the governed layer, with citations on every answer.
Feed the loop
Analytics outputs become inputs agents act on, closing the loop from insight to action.
Where it applies
FAQ
Data, Analytics & GenAI — questions, answered.
Why does data quality matter for AI agents?
Agents are only as trustworthy as the data underneath them. Without a governed foundation and a shared semantic layer, agents disagree with dashboards and generative models hallucinate. We modernise the foundation first, so every agent decision is grounded in a source you can point to.
How do you stop generative AI from hallucinating?
We use retrieval-grounded generative AI: models answer from your documents and governed data with citations, not training-set guesswork. Combined with end-to-end lineage, 100% of agent answers stay traceable to a governed source, so you always know where a number came from.
What is a semantic layer and why do we need one?
A semantic layer is a shared definition of your metrics and entities, so an agent and a dashboard never disagree on what 'revenue' means. It gives autonomous systems and analysts one consistent, query-ready source of truth, built as reusable platform rather than one-off plumbing.
How is data governance and privacy handled?
Access controls, masking, and residency are handled at the platform layer, not bolted on per project. Automated data-quality checks and end-to-end lineage sit alongside them, so you can prove where every number an agent used came from and who is permitted to see it.
Can analytics feed back into our agent workflows?
Yes. Forecasting, segmentation, and anomaly detection feed decisions straight back into agent workflows, closing the loop from insight to action. Analytics outputs become inputs agents act on, so the work drives outcomes rather than sitting idle in a dashboard nobody opens.
Put Data, Analytics & GenAI to work.
Tell us the workflow. We'll show you the shortest path to a running agent.
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