We are not here to add a chatbot to your homepage. We find the repetitive, expensive work inside your business - the re-keying, the chasing, the guessing - and we automate the part a model genuinely does better than a person.
Most AI projects fail for the same three reasons. These are our answers.
Start from the decision, not the model
We begin with a decision you make badly or slowly today, and work backwards to the data. If a spreadsheet and a rule would fix it, we will tell you that instead of selling you a model.
A human stays in the loop
A model that is right 92% of the time still needs a plan for the other 8%. Every system we build shows its confidence, routes the uncertain cases to a person, and records who approved what.
Your data stays yours
We work on your infrastructure or your cloud tenancy. Your documents and customer records are not used to train anyone else's model, and we will sign whatever confirms that in writing.
6 capabilities
What we actually build
Every one of these is in production somewhere. None of them required replacing your existing systems.
Document & Invoice Extraction
Pull structured data out of invoices, purchase orders, contracts and delivery notes. The model reads the document, extracts the fields you care about, and flags anything it is unsure about for a human to confirm.
Typically 80-90% of documents processed without manual keying
An assistant trained on your own product documentation, past tickets and policies. It answers routine questions instantly and hands the rest to a human with the context already gathered.
First-response time measured in seconds instead of hours
Retrieval-augmented generationvector searchWhatsApp and web widgets
Demand & Inventory Forecasting
Forecast what you will sell and what you should hold, using your own order history plus seasonality, promotions and lead times. Buyers get a recommendation with the reasoning attached, not a black box.
Commonly 15-25% less working capital tied up in slow stock
Score inbound leads on how closely they resemble the deals you actually won. Your team works the top of the list instead of the top of the inbox, and managers see which channels genuinely convert.
Sales teams reach qualified leads first, not merely the newest ones
Computer vision on the line or at goods-in: detect defects, verify labels, count units and read serials. Runs on ordinary cameras and flags borderline cases for a human check.
Consistent inspection on every unit, not just the sampled ones
Automate the routine path through a workflow and route only genuine exceptions to a person. The model handles classification and drafting; your team keeps the judgement calls and the final approval.
Staff time moves from data entry to the decisions that need a human
Four weeks to find out whether it works, before you commit to building it.
01
Discovery
One week. We map the process, look at the data you already hold, and agree what "better" would measure.
02
Feasibility
Two weeks. A rough model on your real data. You get an honest read on accuracy before spending build money.
03
Pilot
Four to six weeks. A working system for one team or one document type, with the human review step in place.
04
Scale
Roll out to the rest of the business, with monitoring, retraining and a support agreement.
We will tell you when AI is the wrong answer
A good share of the problems brought to us as "AI projects" turn out to be a reporting problem, an integration problem, or a process that nobody has written down. Those are cheaper and faster to fix, and we would rather do that than sell you a model that solves the wrong thing.
Dites-nous ce dont vous avez besoin - une application mobile, un nouveau site web, un CRM ou une plateforme sur mesure. Un architecte solution vous répondra sous un jour ouvré. Sans engagement et sans jargon.