Decide before industrialising
Turn an AI intention into a manageable project
We support product and technical teams in selecting a use case, checking data, comparing architectures and building a measurable first service. The objective is not a spectacular demo, but an evidence-based decision and a realistic path.
- Decision
- Continue, redirect or stop based on measured evidence
- Architecture
- A scenario compatible with data and operations
- Road map
- Explicit stages, risks and costs
Progress through evidence
Six steps to reduce uncertainty
- 01
Qualify the use case
Users, assisted decision, frequency, expected benefit and acceptable cost of error.
- 02
Audit the data
Sources, quality, permissions, personal data, formats, volumes and update frequency.
- 03
Define evaluation
Representative cases, metrics, thresholds and a human validation protocol.
- 04
Compare architectures
Models, RAG, tools, hosting, security, latency, cost and portability.
- 05
Prototype the main risk
A focused experiment on the most uncertain assumption, without hiding limitations.
- 06
Prepare production
Integration, observability, responsibilities, security, operations and continuous improvement.
Usable deliverables
What the engagement should leave with the teams
The format depends on the project. We favour elements that support decisions, implementation and operations.
- Use-case map and reasoned prioritisation
- Audit of data, access and dependencies
- Target architecture and compared scenarios
- Evaluation set and prototype results
- Risk register and control measures
- Road map to production
Points of attention
What we refuse to hide
A POC is not production
A convincing demo proves neither robustness, security nor cost at scale.
The model does not fix the data
Inconsistent sources or poorly defined permissions carry into the final service.
Autonomy must be proportionate
The more consequential an action, the stronger its controls, validation and recourse must be.
Need to explore before framing?
The Lab documents our technical experiments
See our areas of work and articles on RAG architectures, models and integration.