AI Solutions
Not a chatbot on your homepage. The tedious, expensive, repetitive work inside your company, done by a system that reads your own documents and knows where it is not allowed to decide.
Book your Diagnostic ↗Everyone is selling you AI. Nobody is telling you where it breaks.
AI development at Sell with Marketing means systems built against your own documents and processes: agents that read technical specifications, price lists and email threads, automation of classification and quoting work measured in hours per week, integration with the software you already run, and a written boundary defining what the model may decide on its own and what always goes to a person.
The honest problem with AI in an industrial company is not capability. It is confidence. A model that invents a tolerance, quotes a price that does not exist or promises a delivery date nobody can meet does more damage in one message than it saves in a month, and it does it in your name.
So the boundary is written before the system is built, and it is part of the deliverable rather than a disclaimer. Where a mistake is cheap and reversible, the system acts. Where a mistake costs money or credibility, the system prepares the answer and a person signs it. That line is drawn with you, in writing, before anyone builds anything.
Built on your documents, not on a demo.
- Agents that read what your company already wrote: Technical sheets, price lists, catalogues, historical quotes and email threads become a source the system can answer from, with the citation attached, so an answer can be checked instead of trusted.
- Automation of the work measured in hours per week: Classifying incoming enquiries, extracting data from a purchase order, drafting the first version of a technical response, reconciling a price list against an ERP: the tasks that are expensive because they are repetitive, not because they are hard.
- Integration with the software you already run: The system lives against your CRM, your ERP and your inbox. An AI that produces a good answer in a window nobody has open has not saved anyone a minute.
- The written boundary on autonomy: Which decisions the model takes alone, which it prepares for a person, and which it is forbidden to touch. Signed before the build, because the time to define this is not after a model has quoted a price that does not exist.
- Evaluation against your own cases: A fixed set of real questions from your business, with the correct answers written by your people, run against the system before it goes live and again whenever the model changes underneath it.
- Handover and documentation: What it does, where it runs, what it costs to operate per month, and how to turn it off. Software the client cannot operate without us is not a deliverable, it is a dependency.
When the bottleneck is a person doing the same thing 40 times a week.
- Manufacturers whose technical team answers the same specification questions every week
- Companies where quoting depends on one person who knows where everything is
- Businesses with years of documents nobody can search when it matters
- Teams re-keying data between an ERP, a CRM and a spreadsheet by hand
- Companies that bought an AI tool nobody uses because it does not know their catalogue
- Operations where a wrong answer costs real money and the risk has never been written down
This is a build, not a licence. When an off-the-shelf tool already solves your case, the Diagnostic says so and what it costs, because charging you to build something you could subscribe to is not a service. What gets built here is what no licence on the market covers.
Before you sign.
Will the AI invent things about our products?
A model with no source will, which is exactly why these systems are built against your own documents with the citation attached to every answer. The remaining risk is handled by the autonomy boundary: where a mistake is expensive, the system drafts and a person signs. Any supplier who tells you a language model never invents anything is either not measuring it or not telling you.
Does our data end up training somebody else's model?
Not under the architectures we use, and this is stated in the contract rather than assumed. The build specifies which provider processes what, under which data-retention terms, and which data never leaves your infrastructure at all. For sensitive documents the answer is often a model running against a store you control, and that decision is made in the Diagnostic, not afterwards.
How long until it is running?
The build is a project with a defined scope and end, unlike a retainer. What sets the length is the state of your documents: a company with a clean catalogue and structured price lists starts producing far sooner than one whose specifications live in scanned PDFs. The Diagnostic looks at the real material before anyone quotes a timeline, because a timeline quoted without seeing the documents is a guess.
What does it cost to run every month?
There is a recurring operating cost, it is not hidden, and it is stated as a range in the proposal with what drives it: volume of queries, size of the document base, and which model the case actually needs. Cheaper models are enough for a lot of the work and we say when. Any proposal that shows a build price with no monthly operating cost is leaving out a bill you will still receive.
Do we need AI at all, or is this fashion?
Sometimes it is fashion, and the Diagnostic will say so with your own numbers. The test is simple: find a task that costs measurable hours every week and where a mistake is recoverable. If nothing in the company fits that description, the honest recommendation is to spend the money elsewhere, and we would rather tell you that before the invoice than after it.
AUTOMATE THE WORK.
NOT THE JUDGEMENT.
It starts with the Diagnostic: what standing still is costing you, what fixing it costs, and what gets fixed first — in your own data. Paid work, credited 100% against the fix if you move within 90 days.
Book your Diagnostic ↗