Use cases
Six scenarios we see most often. For each one you get the problem, our approach and the result we consider realistic.
Let us be straight about this
Grow-AI is a new brand and we do not yet have completed AI projects we could publish with a client name. The scenarios below therefore describe how we design a solution and what results we consider realistic at typical volumes — they are not measured figures from a specific customer. We would rather say so than show you invented references. Real ones will replace them as they arrive.
~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
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
Tell us which one is closest. On the first call we will say whether it makes sense in your case — even if the answer is no.
