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AI Solutions · Offer B

AI-Powered Data Analytics: From Scattered Data to Forecasts and Answers

Most organizations have the data but not the clarity. We clean up and connect scattered data, build dashboards your teams will use, then add forecasting, anomaly detection and natural-language querying, so anyone can “ask your data” and get a trusted answer.

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Why data alone isn’t giving you answers

  • Data is scattered across systems, spreadsheets and documents with no single trusted view
  • Reporting is manual, slow and different depending on who builds it
  • Dashboards show what happened, not what is likely to happen next
  • Every new question waits in an analyst’s queue
  • Fraud, churn and stock-outs are noticed after the damage is done
Offer B

From scattered data to forecasts, step by step

We clean up first, then build dashboards, then add intelligence, so each layer rests on data you can trust.

  1. Consolidate & clean

    Bring scattered data from databases, spreadsheets and documents into one consistent, quality-checked view.

  2. Model the data

    Define the metrics, entities and rules once so every report and model uses the same trusted numbers.

  3. Build dashboards

    Deliver the dashboards and charts your teams will actually use, tied to decisions rather than vanity metrics.

  4. Add forecasting

    Layer on predictive models, validated against your own history before anyone relies on them.

  5. Detect anomalies

    Monitor key signals and alert people when something unusual happens.

  6. Ask your data

    Add natural-language querying so non-technical users can explore the data safely.

Use cases we deliver

Start with the one that matters most to your business.

Demand forecasting

Predict demand by product, location or period to cut stock-outs, overstock and last-minute firefighting.

Churn prediction

Identify customers likely to leave early enough for your team to act, and understand the drivers.

Fraud detection

Surface unusual transactions, claims or behaviour for review instead of finding them after the loss.

Customer segmentation

Group customers by behaviour and value so marketing, pricing and service match real differences.

Anomaly detection

Spot unexpected shifts in operations, finance or quality data and alert the right person.

Ask your data

Ask questions in plain language and get answers, tables and charts, without waiting for an analyst.

“Ask your data”: natural-language querying with control

  • Questions are asked in plain language, in a chat or search interface
  • The model receives the question and only the structure of the tables you allow, not the entire dataset
  • Generated SQL runs against your database and the results are summarized for the user
  • Charts and dashboards can be produced directly from the question
  • Role-based access and table-level control decide who can see which data
  • Choice of cloud or on-premises deployment where your architecture requires it

Related experience

Reference work in natural-language analytics and data processing.

MultiLLM AI Platform

A governed platform for natural-language database queries, charts, knowledge bases and role-based access.

Read case study →

D-Law

AI-assisted legal data processing and natural-language analytics for a Hong Kong legal intelligence platform.

Read case study →

Educational Publisher

Catalogue extraction, matching and natural-language analytics replacing manual spreadsheet work.

Read case study →
A Low-Risk Way to Start

Prove the value before you commit to the build

AI projects stall when buyers fear wasted spend. So we start small, with fixed-price packages and a clear decision point after each stage.

  1. 1–2 weeks · Fixed price

    AI Readiness Audit

    We review your systems and data, then deliver a ranked list of AI use cases with estimated ROI.

  2. 3–6 weeks · Fixed price

    Proof of Concept

    One working use case built on your real data, so you see results before you commit to scale.

  3. Ongoing

    Scale-Up Contract

    Production rollout, integration with your systems, monitoring and ongoing support.

Book an AI Readiness Audit See how the engagement works

FAQ

Frequently asked questions

Do we need a data warehouse before we start?
Not necessarily. The audit assesses what you have. We usually begin with the one or two sources behind a single high-value use case and connect more as the value is proven.
How accurate will forecasts and predictions be?
That depends on the history and quality of your data. During the proof of concept we test models against your historical data and show you the results before you rely on them.
Does our data get sent to an AI provider?
In the natural-language querying pattern, the model receives the question and the permitted table structure rather than the whole dataset, and access is limited to the tables you allow. On-premises deployment is available where required.
Can non-technical users use it?
Yes. “Ask your data” is designed so business users can query data in plain language, while analysts keep control of definitions, permissions and governance.
We already have BI dashboards. Is this different?
Dashboards show what happened. We build on them, or on your existing BI tools, to add what is likely to happen next, what looks wrong, and a way to ask follow-up questions.

Need the data foundations first?

Our data engineering services cover integration, databases and quality workflows that make analytics trustworthy. Data Engineering & Analytics →   Data Engineering for AI →

Turn Your Data into Forecasts and Answers

Tell us what data you have and which decisions it should support. The AI Readiness Audit will rank the analytics use cases worth building first.

Book an AI Readiness AuditView Case Studies