Solutions
You do not need to know whether you need an AI agent, automation or a systems integration. Find your situation — each solution covers the problem, how it works, how a human stays in control and what result is realistic.
~70%
of orders handled without a human
Problem
Two people retype orders from emails and attachments into the ERP every day. Errors surface only at dispatch.
How we solve it
The agent reads the email and attachment, extracts line items, matches them to the catalogue, checks stock and prepares the order. Mismatches and unknown items go to a human.
What we use
2–4 h
saved per person per week
Problem
Procedures, standards and safety instructions live in three systems. New technicians ask colleagues, who lose time.
How we solve it
RAG over the documentation directly in Teams. Every answer cites the document and page; when no source exists, the system says so instead of inventing one.
What we use
< 3 months
typical payback period
Problem
Incoming documents arrive as PDFs, scans and phone photos. Transcription is monotonous and error-prone.
How we solve it
Extraction of line items, totals and dates with checksum validation. On low confidence the document goes to manual review, not into the books.
What we use
~40%
shorter first-response time
Problem
Support answers the same questions about shipping, returns and availability. Complex cases queue behind simple ones.
How we solve it
The agent triages tickets, adds order context and drafts a reply. The operator sends or edits it — nothing goes out automatically.
What we use
~80%
of emails sorted and routed without a human
Problem
Hundreds of messages land in info@, orders@ and claims@ every day. Someone reads, forwards and retypes them — and urgent ones get buried under newsletters.
How we solve it
Every email gets a type and urgency, goes to the right person, its data lands in the right system and routine questions get a draft reply. Uncertain cases are decided by a person.
What we use
~75%
of documents with no manual retyping
Problem
Contracts, delivery notes, CMRs, certificates and partner spreadsheets are retyped into the system every day. Field by field, with typos.
How we solve it
AI reads the PDF, scan, photo or spreadsheet, extracts the fields you define, checks them against your ERP or CRM and writes them into the system. Unclear cases go to a person.
What we use
~15 min
from enquiry to draft quote for catalogue items
Problem
Enquiries arrive by email as text, spreadsheets and PDFs. A salesperson breaks them down by hand, searches the price list and writes the quote — enquiries wait for days and some customers buy elsewhere in the meantime.
How we solve it
AI extracts the items from the enquiry, matches them to the catalogue, fills in prices and availability from the ERP and drafts the quote. The salesperson reviews and sends it; anything unclear is flagged.
What we use
~10 h
saved on reporting every week
Problem
Every week someone spends hours downloading exports from the ERP, accounting, the online shop and the CRM, pasting them into Excel and writing a commentary for management.
How we solve it
Data is pulled from the sources automatically and the report builds itself. AI drafts a commentary on the variances, flags anomalies, and management can ask questions about the data in plain language.
What we use
~30 h
a month less retyping between systems
Problem
The same order, customer and product are typed by hand into the shop, the ERP, the CRM, the accounting system and a spreadsheet. The numbers drift apart and nobody knows which one is right.
How we solve it
We connect the systems so data flows automatically in one direction from a clear source of truth. Classic integration usually does the job; AI is used only for emails, PDFs and matching ambiguous records.
What we use
on-prem
data never leaves the plant
Problem
Inspection is visual; the result depends on the shift and fatigue. Defective pieces are sometimes caught only by the customer.
How we solve it
A computer vision model trained on your own images, running locally at the edge. No imagery leaves the plant.
What we use
evaluations
instead of guessing whether it improved
Problem
A working demo from a contractor who left. Nobody knows whether a prompt change helps or hurts, and the token bill keeps growing.
How we solve it
An audit, a test set from real cases, monitoring and cost limits. Only then optimisation — otherwise you are optimising blind.
What we use
Describe the process that slows you down the most. On the first call we will tell you what can be automated and whether it pays off — even if the answer is no.
