Six use cases we install most often. Each one is a "work package": scoped, built on your documents, handed over to your teams. We start with the one that hurts you the most.
For each case: the pain point you know, and what the AI takes on once the work package is installed.
The pain point — Combing through the entire tender documents (DCE) — tender rules (RC), technical specifications (CCTP), contract terms (CCAP), bill of quantities (BPU/DPGF) — to hunt down penalties, firm deadlines, requirements buried in an annex and contradictions between two documents. An oversight is paid for during execution.
With AI — You upload the file, the AI produces a summary with the points to watch, the exact article where each clause appears, and the discrepancies between documents. You walk into the go/no-go meeting with the reading already done.
The pain point — On the evening of the meeting, going back over raw notes, rewording them into language that commits the parties, going through each observation from the previous time one by one and noting what remains open. Several hours for a document no one should have to read twice.
With AI — You give it your notes; it returns project meeting minutes sorted by trade (or by topic: studies, schedule, coordination), in contractual wording, that carries over items from past meetings and flags those still open. You review and circulate instead of drafting.
The pain point — A few days before submission, writing the technical bid (mémoire technique) from a blank page, digging out the right methodology paragraphs and the right project references in old files, readapting them to the work package and the client of the day.
With AI — We don't write your bid for you — you are the one putting your signature on the line. The AI structures it : it extracts your past reusable building blocks, builds the outline, formats the references. You take the pen on the substance. The blank page disappears, your expertise stays entirely yours.
The pain point — In the tender award phase, bids come in at heterogeneous levels of detail : some contractors break things down more finely than others, some slip in unrequested variants. You have to realign item by item, spot the missing items and the prices that are out of line with the market — to build the bid analysis report that the construction economist has to present.
With AI — You upload the bids, it brings them to a common framework and compares them line by line: the gaps, the price discrepancies and the variants stand out, the comparative analysis table is pre-filled for your report.
The pain point — Standard letters, payment reminders, file summaries, preparing assembly minutes — repetitive, time-consuming tasks, but where imprecise wording can have regulatory consequences.
With AI — Your in-house templates become assistants: you give the context of the file, it produces the letter, the reminder or the summary, in your format, ready to review and sign. Your teams keep control and time.
The pain point — The applicable building standard, the lessons from the project next door, the method we used on the previous project — it can't be found, or it depends on the person who was there and is now on holiday. The knowledge exists somewhere, in past CCTP files, technical sheets, people's heads.
With AI — An internal assistant plugged into your own documents, that anyone can query in plain language: "which building standard applies here", "what did we do on this type of work package". It answers by citing the source. Knowledge no longer depends on a single person.
These cases are not exhaustive — the first work package is defined during the diagnostic, based on your real pain points.
Every case is installed the same way: a scoped work package, built on your documents, handed over to your teams, then maintained over time.
See how a work package is installed →30 minutes to identify your first work package and the gain it can generate, on your own documents.