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LARGE LANGUAGE MODELS · YOUR BUSINESS DATA

Ask your business
anything.

Answers are often buried in spreadsheets, databases, shared drives and inboxes. Large language models let people ask for what they need in plain English, and can read, summarise and organise text at scale. Here are some of the ways they can help.

01 / IN PRACTICE

Five ways to put
language models to work.

Each example below is a common starting point. Real projects are shaped around your own data, the people using it and how accurate the answers need to be.

01 / QUESTIONS OF YOUR DATA

Get answers without waiting for a report.

Staff ask a question in plain English and the model turns it into a query against your sales, stock or job data, then explains the result. The query is shown alongside the answer, so the figures can be checked rather than taken on trust.

  • Sales, stock & job figures
  • Quick questions from managers
  • Access limited to what each person may see

Answers in seconds, with the working shown.

Questions of your dataILLUSTRATIVE DEMO
SALES_DB / READ-ONLY3 TABLES

Which products sold best in Harrogate last quarter?

SELECT product, SUM(qty) AS unitsFROM sales WHERE store = 'Harrogate'AND quarter = '2026-Q2'GROUP BY 1 ORDER BY 2 DESC
Oat flat white1,284
Sourdough loaf962
Almond croissant811
Granola pot540

Oat flat whites led the quarter with 1,284 sold — 18% more than Q1.

QUERY SHOWN WITH EVERY ANSWER 1 query · 0.9 sec

03 / SUMMARIES & THEMES

Read a thousand comments in a minute.

Reviews, survey responses, support tickets and reports can be read in bulk and grouped into themes, with counts and sentiment. Managers see what people are actually saying, with real quotes to back it up.

  • Customer reviews & surveys
  • Support tickets & complaints
  • Long reports & meeting notes

The big picture, with the evidence attached.

Summaries & themesILLUSTRATIVE DEMO
REVIEWS / LAST 90 DAYS1,248 COMMENTS
  • “Arrived the next day, really impressed.”

    Delivery speed
  • “Box was crushed and one jar broke.”

    Packaging
  • “Lovely staff on the phone, sorted it fast.”

    Staff
  • “Checkout kept rejecting my postcode.”

    Website
THEMES
Delivery speed412
Staff & service355
Packaging damage188
Website checkout97
THEMES WITH EXAMPLE QUOTES 42 sec

04 / TEXT TO STRUCTURED DATA

Turn emails into orders, not typing.

Orders, enquiries and booking requests often arrive as free text. A language model can pick out the details — who, what, how many and when — and pass them into your systems in the right format, asking a person whenever something is missing or unclear.

  • Email orders & enquiries
  • Booking & quote requests
  • CRM & spreadsheet updates

Consistent records from messy messages.

Text to structured dataILLUSTRATIVE DEMO
INBOX / orders@NEW MESSAGE
FROM sarah@dalesgreens.co.ukSUBJECT Order for Friday

Hi, could we get 40 bags of compost and 12 trays of bedding plants delivered Friday morning? Same address as last time. Thanks, Sarah

customer
Dales Greens Ltd
item
Compost 50 L × 40
item
Bedding plants tray × 12
delivery
Fri 26 Sep · AM
address
On file · HG4 2QT
check
Compost bag size assumed
DRAFT ORDER CREATED FOR APPROVAL 1.3 sec

05 / DOCUMENT REVIEW

Check long documents against what matters to you.

A model can read contracts, tenders or supplier terms and highlight clauses worth a closer look — payment terms, renewals, liabilities — against a checklist you define. It doesn’t replace professional advice, but it helps people reach the important parts faster.

  • Supplier & customer contracts
  • Tender & compliance checklists
  • Key dates & obligations

The clauses that matter, found first.

Document reviewILLUSTRATIVE DEMO
SUPPLIER_AGREEMENT.PDFYOUR CHECKLIST
4.2 Renewal
7.1 Payment
9.3 Liability
  • CLAUSE 4.2Auto-renews for 24 months unless cancelled 90 days before
  • CLAUSE 7.1Payment due in 60 days — your standard is 30
  • CLAUSE 9.3Liability cap matches your policy
2 CLAUSES FLAGGED FOR A PERSON TO REVIEW 18 pages

02 / AND MORE

Other ways
LLMs can help.

Report drafting

Turn figures and notes into a first draft of a weekly report, proposal or update for a person to finish.

Translation

Translate documents, product information or customer messages while keeping your terminology consistent.

Sorting messages

Classify incoming emails, forms and tickets by topic and urgency so they reach the right person.

Meeting minutes

Turn transcripts or rough notes into clear minutes with decisions and actions listed.

Tidying records

Match duplicate customers, clean up product descriptions and standardise messy spreadsheet data.

Private deployment

Where data is sensitive, models can run on your own server or in a UK-hosted environment.

03 / GETTING STARTED

What a good project
needs to succeed.

01

Access to the right sources

The documents, databases or inboxes the answers should come from — kept up to date as they change.

02

Permissions that match yours

People should only get answers from information they are already allowed to see.

03

Test questions with known answers

A set of real questions to measure accuracy before launch and to check it stays reliable afterwards.

04

A clear route when it is unsure

The system should say when it doesn’t know, and important outputs should be checked by a person.

04 / YOUR DATA, IN PLAIN ENGLISH

Have questions
for your data?

Tell me where your information lives and what people need to get from it. We can talk through whether an LLM is a good fit and what a small, well-tested first version could look like.

Open to projects in North Yorkshire and nationwide