Report drafting
Turn figures and notes into a first draft of a production report, 8D or investigation for a person to finish.
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UNLOCK YOUR BUSINESS DATA · LANGUAGE MODELS
Ask your business
a question.
Engineers and managers spend too much time finding and interpreting information — in manuals, reports, maintenance logs, ERP screens and inboxes, or in one experienced person’s head. Language models can turn that information into something people can ask questions of and use.
01 / IN PRACTICE
Five examples,
problem to result.
Each example is a demonstration on illustrative data, laid out the way a real project runs: the business problem, the data, the AI, the result and the potential value.
01 / TECHNICAL DOCUMENTS
Could your engineers ask the manual a question?
- BUSINESS PROBLEM
- Answers sit in hundreds of manuals, SOPs and service bulletins. Finding the right page takes time, so people ask whoever has been there longest.
- DATA
- Your manuals, SOPs, drawing notes and past fault reports, left in the folders they already live in.
- AI
- Documents are indexed by meaning, and a language model answers from the passages it finds — citing each one, and saying so when the documents don’t cover the question.
- RESULT
- An answer in seconds, with links to the exact pages it came from.
POTENTIAL VALUELess time searching, and knowledge that stays when people leave.
Technical documentsDEMONSTRATION · ILLUSTRATIVE DATA
What torque does the P-104 impeller nut need?
- P-104 Manual§6.41
- SOP-117 Rebuildstep 122
- Shift log, 3 Marchentry 6
ANSWER
65 Nm, with thread-locker on clean threads 1. The rebuild SOP adds a check with calibrated wrench T-07 and a signature 2.
EVERY ANSWER LINKS TO ITS SOURCE 2 citations
02 / PRODUCTION DATA QUESTIONS
Could a manager get an answer without waiting for a report?
- BUSINESS PROBLEM
- Simple questions about downtime, scrap or output need someone to pull a report, so they often go unasked.
- DATA
- Read-only access to the production, downtime and quality tables in your MES, ERP or historian.
- AI
- A language model turns the question into a database query, runs it and explains the result, with the query shown alongside.
- RESULT
- An answer in seconds with the working shown, limited to the data each person is allowed to see.
POTENTIAL VALUEDecisions made on the figures rather than a hunch.
Production data questionsDEMONSTRATION · ILLUSTRATIVE DATA
Which stoppages cost Line 2 the most time last month?
SELECT reason, SUM(minutes) AS minsFROM downtime WHERE line = 'L2'AND month = '2026-09'GROUP BY 1 ORDER BY 2 DESC
Filler jams1,284 min
Changeovers962 min
Label misfeeds811 min
Waiting for material540 min
Filler jams cost the most: 1,284 minutes, over 21 hours — almost double August.
QUERY SHOWN WITH EVERY ANSWER 1 query · 0.9 sec
03 / FAULT LOGS & NCRS
What are a year of fault logs actually telling you?
- BUSINESS PROBLEM
- Thousands of free-text maintenance entries and NCRs are written but never read together, so recurring problems hide in the noise.
- DATA
- Exports from your maintenance system, NCR log or complaints database.
- AI
- A language model reads every entry, groups them into themes and counts them over time.
- RESULT
- A ranked list of recurring problems, with real entries behind each one.
POTENTIAL VALUEEvidence for where engineering time will make the most difference.
Fault logs & NCRsDEMONSTRATION · ILLUSTRATIVE DATA
“Labeller stopped, sensor dirty again. Cleaned + reset.”
Label sensor“Conveyor 3 belt running off, re-tracked.”
Belt tracking“Glue nozzle blocked on case erector, swapped.”
Glue system“Label sensor fault, wiped lens, OK now.”
Label sensor
THEMES
Label sensor faults412
Belt tracking355
Glue system blockages188
Guard interlock trips97
RECURRING FAULTS WITH EXAMPLE ENTRIES 42 sec
04 / ORDERS & RFQS INTO YOUR ERP
Could emailed orders go straight into the ERP?
- BUSINESS PROBLEM
- Orders and schedule changes arrive as emails and PDFs and are retyped into the ERP, with occasional mistakes on revisions or quantities.
- DATA
- The order inbox, plus your customer and part master data.
- AI
- A language model picks out part numbers, revisions, quantities and dates and checks them against the master data.
- RESULT
- A draft sales order ready for approval, with anything missing or mismatched flagged.
POTENTIAL VALUELess retyping, and fewer orders made to the wrong revision.
Orders & RFQs into your ERPDEMONSTRATION · ILLUSTRATIVE DATA
FROM buyer@northfab.co.ukSUBJECT PO 7781 · brackets
Hi, please supply 500 off bracket BR-220, rev C and 200 off spacer SP-14, needed by Friday 26th. Usual delivery address. Thanks
- customer
- North Fab Ltd
- line
- BR-220C × 500
- line
- SP-14 × 200
- due
- Fri 26 Sep
- ship_to
- On file · DL7 8QE
- check
- Rev C unreleased
DRAFT SALES ORDER CREATED FOR APPROVAL 1.3 sec
05 / CUSTOMER SPEC REVIEW
Could a long customer spec be checked against what you can do?
- BUSINESS PROBLEM
- Customer specifications and quality manuals run to dozens of pages, and a demanding clause can be missed until production.
- DATA
- The customer’s documents, and a checklist of your capabilities and standard terms.
- AI
- A language model reads the document and highlights clauses that differ from your checklist — tolerances, certification, packaging, penalties.
- RESULT
- Flagged clauses with page references for an engineer to review.
POTENTIAL VALUERisks spotted at quotation rather than on the shop floor.
Customer spec reviewDEMONSTRATION · ILLUSTRATIVE DATA
4.2 Tolerances
7.1 Certification
9.3 Packaging
- CLAUSE 4.2Flatness 0.02 mm — tighter than your usual 0.05 mm
- CLAUSE 7.13.1 material certs required for every batch
- CLAUSE 9.3Packaging requirements match your standard
2 CLAUSES FLAGGED FOR ENGINEERING REVIEW 18 pages
02 / AND MORE
Other ways to
use your information.
Translating SOPs
Translate work instructions and training for multilingual teams, keeping your technical terms consistent.
Triaging service requests
Sort incoming fault reports and service requests by asset and urgency so they reach the right engineer.
Handovers & minutes
Turn rough notes or transcripts into clear shift handovers and meeting minutes with actions listed.
Cleaning part & asset data
Match duplicate part numbers, standardise descriptions and tidy messy ERP and maintenance records.
Private deployment
Where data is sensitive, models can run on your own server on site or in a UK-hosted environment.
03 / BEFORE WE BUILD
What we look at
before building anything.
Access to the right sources
The manuals, logs, databases or inboxes the answers should come from, kept up to date as they change.
Permissions that match yours
People should only get answers from information they are already allowed to see.
Test questions with known answers
Real questions from engineers and operators, to measure accuracy before launch and check it stays reliable.
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 / HOW WE'D START
Idea → evidence
→ investment.
You never commit to a large project on a hunch. Each step answers whether the next one is worth taking — and “no” is a useful answer too.
- 01
Free conversation
30 minutes about a process, machine or business problem. No technical sales pitch.
- 02
AI Opportunity Review
A short paid review of the problem, your data and systems, ending in a roadmap and a recommendation.
- 03
Proof of concept
One problem tested on your real data, with a clear Go, Modify or Stop at the end.
- 04
Build & improve
Only if it works: a practical system, deployed, monitored and refined.
05 / UNLOCK YOUR BUSINESS DATA
Too much time
finding information?
Tell me where your information lives and what people need from it. In a free 30-minute conversation we can talk through whether a language model is a good fit, and how to test it on your own documents first.
Independent · North Yorkshire · working across the UK