AI & data
Put AI where it actually adds something.
Artificial intelligence is useful when it handles what a human does slowly and without enjoyment: classifying, extracting, matching, summarising. It becomes a problem when it is handed a decision with no safety net. We start from your process and your data, not from the model.
When AI makes sense
On repetitive, high-volume tasks with little judgement involved — that is where the gain is real.
Your teams spend every day reading, sorting and re-typing incoming documents.
Your data exists, but scattered across systems that do not talk to each other.
You have to match information that is close but never identical: names, references, addresses.
You receive free-text requests that have to be triaged before they can be handled.
You produce reports by hand from several exports.
You have tried a generic AI tool and the result did not survive contact with your business reality.
What we put in place
Processing that fits into your existing tools, not one more platform to open every morning.
Document processing
- Extracting data from structured and unstructured documents
- Automatic classification and routing to the right handling path
- Matching against your existing reference data
- Consistency checks before anything is written
Data and reporting
- Consolidating scattered sources into a usable model
- Data quality: duplicates, missing values, inconsistencies
- Dashboards fed automatically
- Historisation, so a figure can still be explained months later
Assisting the work
- Search across your internal documentation, with sources cited
- Drafting support framed by your templates and your rules
- Case preparation: gather, summarise, flag what is missing
- Detection of unusual cases to be checked by a human
Where the decision stays human
Automated processing saves time on preparation. Anything that commits your business goes through a review, and goes back when that review fails.
Collection
Gather what arrives, whatever the format
Sources
- Incoming documents
- APIs and feeds
- Databases
- Business tools
Data & context
Clean it, match it against your reference data, date it
AI + business rules
The model proposes, the rules verify what is certain
Human review
A person decides on anything that commits the business
Rejected or corrected, the case goes back into processing — and feeds the reference set.
Action in the process
Record it, send it, trigger the next step
Below the confidence threshold agreed with you, the case goes to review rather than passing silently.
AI does not always need to decide.
The most important part of an AI project is not the model. It is what happens when the model is wrong.
- A human stays in the loop
- On decisions that commit the business, the system proposes and a person approves. The proposal saves time; the approval keeps responsibility where it belongs.
- An explicit confidence threshold
- Below a certain level of certainty, the case goes to manual review rather than passing silently. That threshold is a business parameter, not a technical setting.
- Business rules above the model
- What is certain gets coded as rules. A ceiling, a regulatory condition, a consistency check are not learned statistically: they are verified.
- Full traceability
- Every result keeps its sources, its processing version and its timestamp. Without that, you can neither audit, nor correct, nor explain.
- A fallback to conventional processing
- When the model is unavailable or outside its domain, the process has to continue another way. A system that stops because the AI does not answer is not operable.
We do not announce a success rate before testing on your data. A figure published against a public benchmark says nothing about your case.
How a project runs
We start by measuring on your data. An AI project that is not measured cannot be steered.
- 01
Start from the process
Which task takes time today, who does it, and what happens when it is done badly?
- 02
Look at the real data
Volume, quality, format, usage rights. This step sometimes concludes that the data has to be dealt with before the model is even discussed.
- 03
Evaluate on a sample
Testing on your real cases, against a reference set built with you. The result is measured, not felt.
- 04
Fit it into the flow
The processing slots into your existing tools, with its rules, its thresholds and its review path.
- 05
Monitor over time
Data drifts, cases change. What is not monitored degrades without warning.
What we rely on
The choice depends on how sensitive your data is and on what you must be able to explain.
Language models
Via API or in a controlled hosting setup, depending on the confidentiality your data requires.
Retrieval-augmented search
Answers grounded in your own documents, with cited and verifiable sources.
Conventional processing
Rules, pattern matching and simple statistical models, where they are sufficient and cheaper.
Data platform
Collection, cleaning, historisation and exposure of data to the tools that consume it.
How we work
Often through a short evaluation engagement on your data, before committing to a full project.
Consulting & team extension
Our consultants join your team to bring the skills you need: development, functional analysis, technical analysis, business analysis or architecture.
Learn moreCustom project
We take responsibility for the whole thing: analysis, design, development, integration, delivery and evolution.
Learn moreIT partner
A lasting point of contact to maintain, evolve and connect the software and systems your business runs on.
Learn more
Related use cases
Bring AI into an existing process
You have spotted a repetitive, document-heavy task, without knowing whether AI would actually help.
Automate a business process
The same information is entered several times, approvals happen by email, and nobody can say where a file currently stands.
Connect systems that don’t talk to each other
Your ERP, your CRM and your business tools each hold part of the truth, with no reliable exchange between them.
Frequently asked questions
Does our data go to an AI provider?
That depends on the architecture chosen, and it is a decision to take explicitly at scoping. Depending on the sensitivity of the data, you can use a commercial API with contractual no-reuse commitments, or host the processing in an environment you control. We document what each option implies, in cost and in constraints, before you choose.
How do we know the output is reliable?
By measuring it on your own data, against a reference set validated by your business teams. That is the only measurement that counts. A performance figure published by a vendor against a public benchmark says nothing about your specific case.
Do we need a lot of data to start?
Not always. Approaches built on existing models or on document retrieval work without any specific training. Volume becomes decisive when a bespoke model has to be trained, which is rarely the first step worth considering.
Will this replace jobs?
That is not the objective we pursue, and not an outcome we guarantee. What we design shifts the effort: less data entry and sorting, more handling of cases that require judgement. If headcount reduction is your objective, it has to be stated at scoping, because it changes the design and the safeguards needed.
Is generative AI always the right answer?
No. For classifying documents against stable criteria, extracting fields from a fixed form or matching references, simpler approaches are often more reliable, cheaper and easier to explain. We pick the simplest approach that answers the need.
Let’s talk about your case
Tell us which task your teams repeat the most. That is almost always the right starting point.