Business copilots & Vision AI
Copilots wired into your processes, and computer vision where the information is an image: data stops informing and starts triggering.
An analysis that triggers no action stays a report.
Governing, making reliable, analysing and forecasting lead to an informed decision. That decision still has to reach the actual work, at the right moment and inside the tool where the person already works.
That is what copilots are for: they live inside the process rather than beside it, propose the action and leave the decision to the human. And where the information is not a row but an image, computer vision takes over.
What we do
Four capabilities so data triggers rather than informs.
Copilots per function
Assistants designed for one specific job, wired into its tools and its rules, rather than a general assistant that knows the context of none.
embedded in the application the person already uses, not beside it
- A scope of action written up front
- Context gathered before the question
- Anything that commits stays human-approved
Process automation
The repetitive action is delegated, the decision stays human. Every proposal is logged and replayable, which makes it auditable afterwards.
each proposal keeps its inputs, not just its outcome
- Frequent, well-framed gestures first
- Sensitive decisions stay supervised
- A proposal can be re-examined later
Vision AI
Quality control, counting, detection on video or photo streams: where the information is an image, the analysis runs on the image rather than on a manual entry.
a confidence score per detection, not a binary answer
- Detection annotated on the area concerned
- Below the threshold, the model asks instead of concluding
- The annotation set remains your asset
Capitalisation
Every validated use case feeds a reusable catalogue, so the second copilot costs less than the first.
connectors, guardrails and evaluation reused from one case to the next
- Connectors and scopes already written
- Guardrails and logging shared
- The evaluation set grows with each case
When data triggers
Dentaline: the X-ray read by the model, the diagnosis made by the practitioner
−95% charting time · 35% → 1% error rate
Manual periodontal charting took up to two minutes per patient and produced a 35% data-entry error rate, on a growing practice, under HDS and GDPR compliance requirements.
A deep learning model identifying teeth, anomalies and implants on panoramic X-rays, integrated into the practice workflow and designed to prepare the reading without ever replacing the medical decision.
Fraud caught in real time, KYC cut by more than half
+40% fraud detection · −60% KYC time
Detect fraud on massive volumes in real time, shorten a KYC slowed by scattered customer data, and make regulatory data reliable, under heavy compliance and sovereignty constraints.
A governed data foundation with MDM for a single customer reference and continuously measured quality, detection models wired into the flows, and a data copilot whose every answer carries its sources.
What you get
The scope of action, written first
What the copilot does alone, what it prepares for approval, what it never does. The confidence threshold and mandatory supervision on anything irreversible are written there, not in a usage guide handed over afterwards.
The tools and their scopes
A copilot is worth what its connections are worth. Each tool is declared with what it may read and what it may write, and the vision model with its input and its output.
A complete proposal
From the photo taken on site to the prepared action: the defect detected and annotated, the equipment history, the part checked in stock, the slot proposed. Everything is ready, nothing is decided.
The evaluation before go-live
Past interventions are replayed and proposals compared to what actually happened. A defect class below the confidence threshold blocks the opening, it does not ship with a warning.
Copilot or agent?
The question comes up in every framing session, and it is badly put: these are not two maturity levels of the same thing, they are two answers to two different problems. What settles it is not the technology, it is who the gesture belongs to.
Assists one person in their work, where that work happens.
Runs a chain of work end to end, under specification.
It belongs to a person, and recurs in their day.
It is a sequence of tasks that can be described from start to finish.
A scope of action written up front and a confidence threshold.
An executable specification and its acceptance criteria.
A person at work, at the moment they need it.
An event or a batch of work, with no human involved.
The business approves every proposal that commits.
An engineer reviews before integration, and the specification prevails.
The rule we apply
If the gesture belongs to a person and recurs in their tool, it is a copilot. If the work is a chain whose specification and acceptance criteria can be written, it is an agent. A framing session settles it on a real use case rather than on a preference, and it sometimes concludes both: a copilot for the business, agents behind it.
Let the framing settle itHow we deliver
Discover
depending on the number of sources, entities and the level of compliance required
- Quality and reference data audit
- Value / feasibility matrix
- Prioritised data roadmap
MVP
depending on the use case retained and the systems to connect
- Data foundation and data copilot in place
- Impact measurement and KPIs
- Go / no-go before industrialisation
Scale
depending on the number of use cases and domains to connect
- Shared data platform
- MLOps and LLMOps pipelines
- Change management and extension
Run
service commitment defined with you
- Continuous observability and quality
- FinOps optimisation of data and AI
- Continuous compliance audit
Insights & Perspectives
How PerformanceAI transforms marketing analytics
AI agents that do not merely report marketing data but propose the trade-off, inside the tool where the decision is made.
Agentic commerce: steering smarter growth
What changes when an agent acts at every commercial interaction rather than a dashboard consulted once a week.
The interface is dead, welcome to the age of intent
When users express an intent rather than navigate a screen, it is the process itself that has to be rethought.
The rest of the data journey
Turn the decision into action
A first copilot inside the tool your teams already use, with its guardrails and its impact measurement.
Frequently asked questions
An assistant designed for one specific job, wired into its tools and its rules, proposing the action inside the tool the person already uses. A general assistant knows the context of no job in particular.
No. It proposes and prepares, the decision stays human on anything that commits. Every proposal is logged and replayable, which makes it auditable afterwards.
When the information is an image rather than a row: quality control on a production line, counting, detection on a video stream. It removes a manual entry, which is where the errors come from.
With a use case where the action is frequent, the rule is clear and the impact is measurable. A copilot on a rare and fuzzy process costs the same and proves nothing.
No, provided you capitalise. Validated building blocks, connectors, guardrails and evaluation, are reused, and that is the difference between a catalogue and a series of proofs of concept.
It asks a question instead of preparing an action. Below the agreed confidence threshold, a proposal is not displayed as one: what is prepared almost always ends up approved, and that is what makes a low threshold expensive.
The one that recurs every day, whose rule can be written without ambiguity and whose errors can be undone. A rare gesture leaves too little material to evaluate the copilot before opening it, and an irreversible one puts a risk on the first attempt that it should not carry.