AI product engineering & agentic systems
AI Product
& Agentic Systems Consultant
Turn an AI use case into a reliable, measurable product: from scope and architecture to evaluation, guardrails, implementation and handover.
Scope an AI use case Explore the expertise- 2008
- Building digital products since
- 10
- Open-source agent recipes
- 01
- Reference blueprint
Clément Sauvage · Bits’n Coffee
From AI use case to product system.
Clément Sauvage is an AI product and agentic systems consultant and senior product engineer. He helps companies turn an AI use case into a reliable, measurable product that people can actually use. Building digital products since 2008, he connects product intent with retrieval, memory, tools, MCP, agentic workflows, evaluation, observability and guardrails.
He was the technical lead and author of Nebius Agent Blueprint Recipes: ten open-source recipes and a reference blueprint showing in code what production-ready AI agents require. Based in France, he works directly with product and engineering leaders in English and French across Europe, Switzerland, the United Kingdom and the United States.
When to bring me in
Move beyond the impressive demo.
The role sits between the use case and the system that runs it: deciding what is worth building, how it should behave and what the internal team needs to operate it safely.
Where is the AI product now?
You have an AI opportunity, but not yet a product decision
Define the user, task, available data, measurable outcome and cost of a wrong answer before committing to a model, provider or architecture.
The prototype works, but cannot yet be trusted in production
Add evaluation, observability, cost and latency models, explicit failure behaviour and the operational foundations required to put the system in front of real users.
The system must retrieve, remember or act
Design bounded workflows, tools, MCP integrations, permission boundaries and human checkpoints around the autonomy the product actually needs.
AI must become part of an existing web or mobile experience
Integrate useful AI capabilities into the product people already use, choosing cloud or on-device inference around privacy, latency, reliability and interface quality.
AI product engineering
The system around the model.
The engagement can be a focused product and architecture review, a production-shaped prototype or hands-on technical leadership through implementation and handover.
Use-case scoping and system design
Validate the task, users, data, success criteria and deterministic boundaries, then choose models, providers and an architecture around cost, latency, privacy, residency and the ability to change later.
RAG, memory and agent orchestration
Design grounded retrieval with traceable sources, conversational or persistent memory, and typed workflows with bounded work instead of open-ended loops.
Tools, MCP and permission boundaries
Connect agents to real systems through explicit tools and Model Context Protocol integrations, with rules for what they may do, on whose behalf and when a person must confirm.
Evaluation, observability, cost and latency
Create test scenarios, simulated conversations, failure cases and regression checks, while making every execution inspectable and modelling operational cost before scale.
Prototype, product integration and handover
Build focused prototypes or reference implementations that are typed, observable, containerized and tested, then document the system and transfer its evaluation and operating conventions to the internal team.
Production evidence
Agents designed to outgrow the demo.
Nebius Agent Blueprint Recipes demonstrates the architecture, implementation and operating foundations behind production-ready agentic systems.
How I work
Start with the use case. Measure the behaviour. Bound the autonomy.
The model comes after the task, the data and the cost of being wrong. Even a prototype should leave a credible path to operation rather than a second implementation project.
- 01Frame
Start from the use case, not the model
Clarify the task, users, data, constraints and outcome before choosing a provider or designing an agent.
- 02Evaluate
Make it measurable before making it bigger
Define expected behaviour, failure cases and regression tests before adding users, data or autonomy.
- 03Control
Give the system no more autonomy than it needs
Design permissions, checkpoints, fallbacks and failure behaviour as product decisions rather than late safeguards.
- 04Transfer
Leave the team in control
Ship production-shaped code, evaluations and documentation that the internal team can operate and evolve without dependency.
Frequently asked questions
Before the first model call.
The use case, boundaries, evidence and operating responsibility should be explicit before the system gains autonomy.
What does an AI product consultant do?
An AI product consultant turns a use case into a product that can work in production. That includes scoping value, choosing architecture, designing retrieval, memory, tools and workflows, setting up evaluation and observability, defining permissions and failure behaviour, integrating the result and handing it over.
What is an agentic system?
An agentic system can retrieve information, call tools and APIs, remember context and complete multi-step work. Production readiness comes from bounded workflows, permissions, guardrails, evaluation, observability and explicit behaviour when something goes wrong.
Which models and providers do you work with?
Clément works with providers including OpenAI, Anthropic, Mistral, Google and Nebius, open-weight models, and on-device inference on Apple platforms where it fits. The choice follows the use case, cost, latency, data residency and privacy requirements.
What is MCP and why does it matter?
The Model Context Protocol is an open standard for connecting AI systems to tools and data sources through explicit interfaces. It matters because actions are where agents create practical value and where permission and operational risks become concrete.
How do you evaluate an AI system?
With representative test scenarios, simulated conversations, known failure cases and regression checks, combined with observability that makes executions inspectable. The goal is to know whether behaviour is improving or degrading before users find out.
Can you work with regulated companies, including Swiss financial institutions?
Yes. Clément designs architectures that can fit within an organization’s governance, security and control requirements, including traceable answers, controlled data flows, bounded permissions and human checkpoints. He does not provide compliance or regulatory advice and works with the teams who do.
Do you build prototypes or only advise?
Both. Clément builds focused prototypes and reference implementations when working code is the fastest way to validate a use case, and remains hands-on through architecture, implementation and handover.
Can you add AI to an existing mobile app?
Yes. With a background in native iOS, macOS and web products, Clément integrates AI into existing experiences and chooses between on-device and cloud inference around privacy, latency, cost and the product’s interface.
Do you work with companies outside France?
Yes. Clément is based in France, works in English and French, and takes engagements across Europe, Switzerland, the United Kingdom and the United States. Remote collaboration is standard, with on-site workshops where useful.
Tell me what AI should do.
I’ll tell you how I would scope it.
Share the users, task, available data, existing product and constraints. I’ll personally review the context and explain the smallest credible path from use case to an operable product.
Scope an AI use case