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AI 500 · AI

AI Data Insights Manchester

We help businesses ask questions of their own data in plain English and get written summaries of what changed. Our Manchester team connects sales, GA4, accounting exports and spreadsheets, then adds weekly reports and alerts, with every figure checked against the source numbers.

  • Since 2003trading in Manchester
  • 20,000+UK businesses helped
  • Collect M8Unit 3, 116 Bury New Road
  • A personchecks every file
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AI 500

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Answers from your figures without building every report by hand

Most owners and managers have the data they need, scattered across a till system, an online shop, Google Analytics 4 and the accounts package. A language model can sit on top of that data, turn a question such as “which product lines fell last month” into a query, and explain the result in a short paragraph.

There is one serious catch. Language models are good with words and unreliable with arithmetic, and they can produce a confident total that is simply wrong. So the model never does the sums. It writes a database query, the database does the calculation, and the written summary quotes figures taken directly from that result.

Clean inputs matter more than clever prompts. If GA4 events are missing or duplicated, every answer inherits the problem, so we often start with Google Analytics setup fixes.

Who uses plain-English data questions

This suits teams with useful data but no full-time analyst.

  • Retailers comparing shop, marketplace and in-store sales in one place.
  • Service businesses tracking enquiries, bookings and repeat customers.
  • Finance and operations managers who want month-end variances explained in words.
  • Marketing teams joining GA4 traffic with orders and ad spend.
  • Directors who want a short Monday summary rather than a dashboard to interpret.

How we set it up

1

Map your sources

We list where each figure lives, such as the shop database, GA4, Xero or QuickBooks exports and shared spreadsheets, and agree the definitions behind words like revenue and active customer.

2

Build a clean data store

Data is copied on a schedule into one reporting database, with consistent dates, currencies and product codes.

3

Add read-only question answering

The model gets read-only access and a description of each table, so it can query but never change or delete anything.

4

Test against known answers

We ask questions whose answers you already know and compare.

5

Switch on summaries and alerts

Weekly summaries go out by email or Teams, and alerts fire when a figure moves outside its usual range.

What you get

  • A documented data map with agreed metric definitions.
  • Scheduled imports from your sales, analytics and accounting sources.
  • A plain-English question tool with read-only database access.
  • Weekly written summaries with figures quoted from the source data.
  • Anomaly alerts for sales, traffic or costs.
  • A Looker Studio or Power BI dashboard for the core numbers.

Keeping the answers trustworthy

A chart that is wrong gets noticed. A fluent paragraph that is wrong often does not, so the checks matter more here than in ordinary reporting.

  • Read-only credentials: the database user has select rights only, so a badly formed request cannot alter records.
  • Query shown with the answer: anyone can see exactly which tables and filters produced a figure.
  • Reconciliation checks: weekly totals are compared with the accounts or shop admin, and differences are flagged.
  • Personal data kept out: customer names and contact details are excluded unless a question genuinely needs them.
  • Dashboards stay: analytics reporting in Looker Studio or Power BI remains the reference view for core figures.

Frequently asked questions

Can the AI get our numbers wrong?

Yes, which is why it never calculates figures itself. The database runs the query and the model only describes the result. Each answer shows the query behind it, and weekly totals are reconciled with your accounts, so errors in interpretation are visible and traceable.

Which systems can you connect?

Common sources include WooCommerce and Shopify, GA4, Xero and QuickBooks exports, Google Sheets, Excel files and most SQL databases. If a system has an API or a scheduled export, we can usually bring its data in. We confirm each source before quoting.

Do we still need dashboards?

Usually, yes. Dashboards are good for watching the same figures every week, while plain-English questions suit one-off queries and explanations. Many businesses keep both, with the dashboard as the reference view.

Is our financial data sent to an AI provider?

The model receives table descriptions and query results, not your whole database. We keep personal data out, use business API accounts and review the provider’s current data terms with you before anything goes live.

How often is the data refreshed?

That depends on the source. Shop and analytics data can be pulled several times a day, while accounting exports are often weekly or monthly. We agree a schedule that suits how you make decisions, and every summary states when its data was last updated.

Related services

Tell us where your numbers live and what you keep asking, and we will scope it with a fixed quote. Get a data insights quote, phone 0161 315 1151 or WhatsApp us on 07737 902425.

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