Internal knowledge is hard to find
Teams search across scattered documents, tools and conversation histories.
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AI & machine learning · Applied intelligence
AI and machine learning for production. Connected to your data, evaluated in your context.
We turn a use case into a testable system: data, model, integration, evaluation criteria and human control are designed together.
Since 2021 · 45+ projects delivered · Europe & Gulf
- Service
- AI & machine learning
- Scope
- ProductProduction
- Footprint
- Europe / GCC
When this service is useful
When AI has to move beyond the demo.
The question is not only whether a model responds, but whether it is useful, controllable and possible to integrate.
Documents require too much manual work
Reading, classification, extraction or preparation follow repetitive rules.
A prototype will not reach production
Quality, cost, privacy, latency or integration have not been measured.
A decision could use a predictive signal
Data exists, but its quality and the value of the use case still need testing.
How we frame the problem
Start with the decision to improve.
We define what the system should help someone do before selecting a model.
01
The problem
An AI demo can look persuasive while remaining inaccurate, expensive or impossible to integrate.
02
Our intervention
We frame the data, risk and evaluation criteria, then test a complete workflow with its controls.
03
The intended result
A use case whose value, limits and operating conditions can be judged from observable evidence.
Capabilities
From use case to evaluated system.
Technology follows the problem, the data and the level of risk.
01
Feasibility & data
Source quality, constraints, risks and a comparison baseline.
02
Agents, RAG & language
Augmented retrieval, business tools and text or document workflows.
03
ML & computer vision
Prediction, classification or image analysis where the data supports it.
04
Evaluation & integration
Test sets, human controls, APIs, cost tracking and production behavior.
Possible deliverables
What enables a decision and responsible operation.
Deliverables expose observed performance and the conditions of use.
- 01
Feasibility note and data map
- 02
Prototype or pilot connected to the workflow
- 03
Evaluation set and acceptance criteria
- 04
Agreed RAG pipeline, agent, model or inference service
- 05
Documented human controls, logs and limitations
- 06
Integration, monitoring and evolution plan
Example scopes
Scopes that test a hypothesis.
Each example begins with a measurable question, not a promise of universal AI.
Internal knowledge assistant
Search a defined corpus, cite sources and organize user feedback.
RAGSearchEvaluation
Assisted document processing
Extract or classify information with human validation and traceability.
DocumentsWorkflowHuman control
Prediction or vision pilot
Assess data quality and compare a model with an explicit baseline.
MLVisionBenchmark
Guardrails
What we refuse to hide.
A probabilistic system needs explicit criteria and responsibilities.
- 01
No accuracy level, complete automation or return on investment is guaranteed before evaluation.
- 02
Sensitive uses define suitable human oversight and routes for review.
- 03
Privacy, data rights, model providers and retention are stated in the scope.
- 04
Quality can change with data and models; required monitoring is decided before production.
How the engagement runs
Reduce risk before industrializing.
Production is a decision based on observed results.
01
Formulate
Define the task, current baseline and affected users.
02
Assess the data
Check access, quality, volume, rights and representativeness.
03
Test
Build a pilot and measure behavior, errors, cost and latency.
04
Integrate
Connect the system, controls and monitoring to the real workflow if results justify it.
Frequently asked questions
Applied AI questions.
Good answers start with the data and level of risk.
01Do we need a large amount of data?
It depends on the use case. RAG can use a defined document corpus; predictive modeling needs representative data and a reliable baseline.
02Do you build your own models?
We can integrate existing models and managed services or train specific components when the data and need justify it.
03How do you measure quality?
With an evaluation set, comparison baseline and criteria tied to the real task, plus human validation where needed.
04Can you guarantee accuracy?
No. We measure observed performance, document limitations and help decide whether it is sufficient for the context.
05How is our data protected?
Provider choice, access, retention, transfers and responsibilities are defined around the project’s data and obligations.
Our work
Delivered projects close to this need.
Each client’s context, and what we built for them.
Case studies
Designease — AI Interior Design Platform
AI-powered interior design platform for the Bahrain and Gulf market, built for designers and architects in the Middle East.
Kappeler — CERFA Automation Tool
An AI-powered platform that automates the preparation of CERFA permit applications for Kappeler Publicité’s outdoor signage projects. It pairs a retrieval-augmented knowledge base of French signage regulations with Claude AI to pre-fill, check and generate the administrative paperwork each installation requires.
PricingOS — AI Pricing Strategy Platform
Build your pricing strategy with AI through a structured 9-step framework. Personalized AI Brain for product, market, and customer analysis.
Further reading
How to choose an AI agency in France in 2026: a buyer’s guide
A buyer’s guide from Azinove: generate versus integrate, the questions worth asking, and which kind of firm fits which need — plus a ten-agency landscape built only on public, checkable facts.
SOTEU 2026: Europe wants AI in the factory, not just on the screen
On 16 September 2026, Ursula von der Leyen named artificial intelligence as one of the “tipping points” of our time. The message for the leaders of SMEs and mid-sized companies: the value lies in industrial application — health, transport, agri-food, advanced manufacturing, defence and space — with one clear red line: AI must keep humans in control.
Osez l’IA turns one: what SMEs and mid-sized companies can do in practice now
On 3 September 2026, the French Ministry of the Economy reviews the first year of the national Osez l’IA plan. 2030 targets, ambassadors, Diagnostics Data IA, a catalogue of 88 solutions: a practical guide for a management team that wants to move forward without getting lost.
For US companies
For companies in the United States.
Pages written for US buyers: how an engagement runs across time zones, what we put in writing, and the questions US companies ask before they start.
Custom software development for US businesses
A senior remote team for US companies, working from Europe and the Gulf
Odoo implementation partner (USA)
Odoo for US small and midsize businesses
A US project that is only about AI? CleverAI, the AI product studio of Azinove SAS, builds AI products for US companies: cleverai.ai
Data & analytics
Start with the decisions to improve
Cloud & DevOps
Infrastructure your team can understand
Let’s discuss the need
Which use case genuinely deserves AI?
Start with the task, available data and the decision you want to improve. For a US company, framing, evaluation and integration run remotely from Strasbourg and Bahrain; we have no US office.
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