Artificial intelligence for small businesses in Nice and PACA

Time recovered on four specific tasks, not an abstract transformation. Document processing first: supplier invoices, delivery notes, contracts and quotes read automatically, with amounts, dates and references extracted and pushed into your accounts or your ERP. Client responses next: draft replies to recurring requests, prepared from your own templates and signed off by a human before they go out. Automated data entry: meeting notes dictated and then structured, client records completed, data carried from one system to another without anyone touching it. Reporting last: a sales or finance dashboard rebuilt every Monday morning from your own files, with a written summary of the variances. None of the four requires changing software: they graft onto what is already there.

An automation pays off when the task is frequent, repetitive, text-based and low-risk. All four criteria have to hold. In the practices, agencies and services firms we work with on the French Riviera, four projects come back with a quick return: drafting quotes from a catalogue and a conversation with the client, which halves the writing time; sorting and routing incoming email to the right person, with an automatic holding reply; searching internal documents, which replaces digging through shared folders with a question asked in plain English; and chasing unpaid invoices, prepared automatically and sent once approved. Conversely, anything that commits the business legally, touches payroll or decides in a person's place stays out of scope.

Far less than people imagine, provided you start small. We always work through a short proof of concept — one single use case, delivered in two to four weeks, against a target agreed in advance. You measure the real gain before committing to the rest; if the gain is not there, the project stops. Here is how a costing breaks down: use-case scoping — the first conversation remaining free; proof of concept on one use case; rollout and integration with your tools; monthly subscription for hosting, monitoring and support; team training, per session. On top of that comes model usage, billed at cost. The final figure depends far more on the number of data sources to connect and on the level of checking required before a document reaches a client than on the choice of model.

Yes, on three conditions that we settle during scoping. Know where the data goes: we favour models hosted in the European Union, or run on your own infrastructure where the data is sensitive; the contract with the supplier must explicitly exclude the reuse of your data for training. Limit what is sent: anonymisation or pseudonymisation of personal data before processing, a document scope restricted to what is genuinely needed, and defined retention periods. Document it: the processing recorded in the register, the people concerned informed, and a data protection impact assessment where the processing warrants one. We supply all of this in writing at the end of the project.

With a use case, never with a tool. Our method has four stages. One — use case: half a day with you and two or three people who do the work, to list the time-consuming tasks, put a rough figure on them in hours per week, and pick the one with the best return-to-risk ratio. Two — proof of concept: two to four weeks to build a first version real users can work with, on real data, measured before and after. Three — rollout: integration into the tools your teams already use — email, sales management, shared folders — so that no habit has to change needlessly. Four — training: one short session per department, a one-page guide, and a review at three months to adjust. The first conversation is free and carries no obligation.