Data

Data, Analytics & Generative AI: agents that answer from your data, never from thin air.

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.

100% agent answers traceable to a governed source

10K+ agent runs a month on governed data

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UAE

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UAE

Singapore

USA

USA / Pakistan

USA

Australia

USA

Denmark

USA

China

USA

Angola

USA

Pakistan

UAE

Pakistan

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.

The stack behind this service.

Real tools our team works in day to day, not a generic capability list.

Python

Snowflake

Hugging Face

LangChain

PostgreSQL

How it works

A sequenced path, not a big bang.

100%

Agent answers traceable to a governed source

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.

Proof, Not Promises

Real engagements, the results attached to each one.

Promotion waste reduced by 17% across 62 stores

Regional grocery chain, Midwest

Summaries sent within 24 hours up from 61% to 89%

NHS acute trust, Midlands

Precedent research down from 3 hours to 20 minutes

County council, East of England

Reporting lag cut from 14 days to 1 hour

Credit union, Ohio

Where it applies

Banking, Financial Services & Insurance Retail & Consumer Packaged Goods Technology

Ten industries. One audited standard.

Different regulations, different systems of record — the same discipline every time: agents that run inside guardrails and leave a record you can read.

View all industries →

BFSI

Retail & CPG

Telecom

Technology

Healthcare

Public Sector

PropTech

Education

FinTech

AgriTech

Startups

Data, Analytics & GenAI case studies

Challenge, solution, results. What this practice has shipped.

Anonymised at the client's request. Each card states the pain point, what we built, and what changed. Plus the service type and country.

04 studies

Web USA

Promotion forecasting portal for a Midwest grocery chain

Regional grocery chain, Midwest

Challenge: Category managers planned weekly promotions from last year's spreadsheets and gut feel. Over-ordering on promoted lines produced markdowns and waste; under-ordering meant empty shelves by Saturday afternoon. Sales, weather and supplier data sat in three separate systems that nobody had joined up.
Solution: We consolidated sales, supplier and weather feeds into a single warehouse and built a web portal where managers see a forecast for each promoted line and store. Planners can override any figure, overrides feed back into the model, and the portal explains which factors drove each forecast.
Results: Promotion waste reduced by 17% across 62 stores
Forecast accuracy up 12 points on the previous method
Portal delivered in 12 weeks on the existing Azure tenant

AI Agents UK

Discharge summary drafting for an NHS trust in the Midlands

NHS acute trust, Midlands

Challenge: Junior doctors wrote discharge summaries at the end of long shifts, often hours after the patient had left. GPs received summaries late or incomplete, medication lists carried transcription errors, and the trust was missing its target for summaries sent within 24 hours of discharge.
Solution: A drafting agent assembles a summary from the electronic record: admission reason, procedures, medication changes and follow-up actions, with each statement linked to its source entry. The doctor reviews, edits and signs; nothing leaves the trust unsigned. The deployment stayed within the trust's own tenancy and data controls.
Results: Summaries sent within 24 hours up from 61% to 89%
Average drafting time down from 25 to 8 minutes
Medication transcription errors cut by 40% in audit

AI MVPs UK

Six-week planning-application search MVP for a county council

County council, East of England

Challenge: Planning officers spent hours per application hunting through decades of decisions, policies and appeal rulings stored as PDFs across three systems. Precedent was found by memory rather than by search, and newer officers took months to build that memory while caseloads kept growing.
Solution: A six-week MVP indexed 40 years of planning documents and delivered a search tool that answers plain-language questions with cited passages. Officers rate each answer; ratings and missed documents were reviewed weekly. The council's own evaluation panel judged accuracy against sample questions before any wider rollout.
Results: Precedent research down from 3 hours to 20 minutes
92% of answers rated accurate by the council panel
Delivered and evaluated in 6 weeks

Apps USA

Loan officer analytics app for an Ohio credit union

Credit union, Ohio

Challenge: Branch managers received a monthly PDF of lending figures two weeks after month-end. By then pipeline problems were old news. Loan officers tracked their own pipelines in personal spreadsheets, so head office could not see which applications were stuck or why.
Solution: AGS built a Power Apps dashboard over the core banking and CRM data, refreshed every hour. Officers see their pipeline by stage and ageing; managers see branch and product views with written summaries of what changed this week. Summaries are generated from the figures and reviewed before publication.
Results: Reporting lag cut from 14 days to 1 hour
Applications stalled over 10 days reduced by 28%
Adopted by 11 branches within 6 weeks

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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