Scope your AI projectsbefore investing.
We help your teams turn AI ideas into clear decisions: prioritized use cases, technical feasibility, risks, costs, required data, and execution plan. By the end of the scoping, you know what to launch, what to postpone, and what to discard.
Before launching an AI project.
A good AI project doesn’t start with choosing a model. It starts with a clear problem, available data, and a decision to be made.
Start with the business
We begin with tasks, pain points, volumes, costs, and business decisions. AI is only considered if it provides a tangible benefit over a simpler solution.
Assess feasibility
Available data, expected quality, IT constraints, security, costs, integration, and operations. Each use case is evaluated before being launched.
Prioritize without leniency
Some topics deserve a pilot. Others must be postponed or abandoned. The framework also helps avoid unnecessary AI projects.
Six framing areas.
No generic AI roadmap. We frame the topics where a clear decision must be made: launch, test, simplify, or stop.
AI idea audit
Inventory of ideas, grouping by use case families, clarifying objectives, and removing duplicates. We turn a vague list into a readable portfolio.
Use case prioritization
Business impact, feasibility, risk, cost, data dependency, integration complexity. Each topic is evaluated using the same criteria.
Technical feasibility study
Data analysis, source systems, access rights, hosting constraints, possible models, and target architecture. We ensure the project can hold up in production.
Cost & workload estimation
Build costs, per-use costs, hosting, licenses, monitoring, maintenance. The goal: avoid the appealing pilot that becomes too expensive to operate.
Product & UX framing
Users, journeys, human validation points, assistant limits, refusal rules, feedback. An AI project must remain usable and controllable.
Pilot plan
Scope, data, success criteria, metrics, timeline, deliverables, risks, and next decisions. The pilot is framed before launch.
What we deploy.
AI framing must intersect business, product, technical, security, and operational aspects. Not just model comparison.
Example — AI use case portfolio.
A business unit comes with a dozen AI ideas: internal assistant, report generation, incoming request automation, document extraction, sales scoring. The scoping helps sort the topics, discard false good ideas, and define priority pilots.
A project in five stages
A dedicated team from scoping to delivery with a dedicated project manager
The real pain points.
Interviews, processes, repetitive tasks, current costs, tools used, breaking points. We start from the field, not from a list of abstract ideas.
The use cases.
Grouping ideas, clarifying objectives, removing duplicates, formulating testable use cases.
Impact, feasibility, risks.
Each topic is scored using the same criteria: business value, available data, technical complexity, cost, security, adoption, operations.
Launch, postpone, abandon.
We distinguish between quick wins, structural topics, high-risk projects, and false good ideas. Not everything deserves a pilot.
Scope, cost, criteria.
For selected topics: target architecture, scope, timeline, budget, risks, success metrics, and next steps.
Why frame before launching.
An AI project can fail for the wrong reasons: missing data, underestimated costs, vague use cases, overlooked security, or lack of metrics. Framing helps identify these issues before development.
Avoid false good subjects
Not all use cases justify AI. Some are better addressed with classic automation, UX improvements, or a better business tool.
Estimate the true cost
Cost isn’t limited to the prototype. It must include model integration, hosting, connectors, monitoring, maintenance, and usage volumes.
Verify the data
No good assistant without accessible, reliable, and properly governed data. Framing checks what truly exists.
Decide quickly
In the end, each topic gets a decision: launch a pilot, deepen the analysis, postpone, or abandon. Framing reduces uncertainty before investment.
Frequently asked questions
The most common questions we get during framing. If yours isn’t here, reach out to us!
An AI project, an application overhaul, or a modernization to scope?
75011 Paris