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AI & GenAI

AI & GenAI Consulting

AI is reshaping every industry. But between the POC that impresses in a demo and the system that creates value in production, there is a gap that, according to RAND (2024), more than 80% of AI projects fail to cross. We close it (strategic scoping, development, MLOps, training), whether the need is document RAG, AI agents, computer vision or prediction.

Duration

1 day to 3 months

Team

1–4 consultants

Indicative budget

€2–200K

The Nobori Method (understand, scope, prove, scale to production) delivers, on average, double-digit productivity gains for our clients, at both the individual and team level.

< 3 months

average time from idea to production

70%

of our POCs reach production

3x

faster business processes through automation

THE PROBLEM

AI is everywhere. The impact is nowhere

According to RAND (2024), more than 80% of AI projects fail, twice the rate of IT projects without AI. The reasons are rarely technical: poorly prepared data, poorly scoped use cases, business processes that are unmodeled or unfit, a business adoption plan that misses the mark. Recommendation engines, document RAG, computer vision or prediction: the challenge is the same. Scale to production to create lasting value.

Our approach

Step 1

Understand before prescribing

We immerse ourselves in your business context, your data and your teams. We identify what is really blocking you, and what can create value starting tomorrow.

Step 2

Pick the right battles

Use cases are prioritized by real business impact, not demo appeal. Every use case has a business sponsor, a budget and a success criterion.

Step 3

Prove fast, decide better

A working prototype in a few days to a few weeks to validate the value, so investment decisions rest on facts, not convictions.

Step 4

Build to last

Production rollout, integration into your business processes, handover to your teams. We deliver a production-ready system.

How we fit into your organization →

Technologies & tools

OpenAIOpenAI
AnthropicAnthropic
LangChainLangChain
LangGraphLangGraph
DustDust
MistralMistral
OllamaOllama
PythonPython
MLflowMLflow
Hugging FaceHugging Face
TensorFlowTensorFlow
FastAPIFastAPI

Team certifications

AWS Machine Learning SpecialtyGoogle Cloud Professional Machine Learning EngineerMicrosoft Azure AI Engineer AssociateDatabricks Certified Machine Learning Professional

Case studies

Concrete results, not promises

Industry·6 months

International group (confidential)

Multi-agent AI orchestration platform with no vendor lock-in

100+

AI use cases identified

Thousands of person-days

in potential productivity gains

100 AI use cases identified, thousands of person-days of gains: now someone has to build the platform that makes them possible

Read the story

Complementary expertise

Combine our areas of expertise for greater impact

Data & Analytics

Build an ML factory on reliable, governed data.

Discover

IT Architecture & Modernization

Integrate AI into your existing IT without creating technical debt.

Discover

Our commitments on this offering

ROI-first approach

Every use case is assessed on its business impact before any technical investment.

From POC to production

We deploy to production with industrialized MLOps. We do not stop at strategy slides.

EU AI Act & sovereignty

Active monitoring of the EU AI Act and of data sovereignty issues.

What our clients say

Nobori worked with us on a project to capture and leverage the industrial know-how that had never been formalized within the company. They separated marketing from reality, identified the solutions that truly fit our priorities, and built an ROI-driven roadmap, with measurable results every quarter.
G

Guillaume

Chief Information & Digital Officer

Global industrial group

Frequently asked questions

How long does it take to get a first AI use case into production?
A first working POC is typically delivered in 2 to 6 weeks. Full production rollout with MLOps and monitoring takes another 1 to 2 months. We prioritize high-impact, low-complexity use cases to generate value quickly.
Do we need perfectly clean data to start an AI project?
No, perfect data is not a prerequisite. Our initial audit identifies the data that is usable as-is and the data that needs quality work. We often start with a narrow but reliable data scope, then expand step by step.
How do you handle data confidentiality with LLMs?
We offer several architectures depending on your sensitivity level: LLMs hosted on-premises (Mistral, LLaMA via Ollama), dedicated Azure OpenAI instances, or APIs with strengthened contractual clauses. Each option is documented with its implications for cost, performance and GDPR compliance.
How is working with you different from working with a software vendor like Dataiku or Databricks?
Vendors push you toward their platform. We are independent and choose the architecture that best fits your context: sometimes a simple Python script is enough. Our goal is to maximize your autonomy, not to create dependency on a tool.
Will our internal teams be able to maintain the solutions after you leave?
That is our commitment. Every engagement includes structured knowledge transfer: technical documentation, training sessions, and a post-engagement support period. We also size the technical complexity to the maturity of your teams.
How much does AI consulting cost for a mid-market company?
The budget depends on the scope. An AI maturity audit with use-case identification starts at €2K (1 day). A working POC with deployment costs €15–50K over 2 to 6 weeks. A full scale-to-production program (MLOps, training, handover) runs €80–200K over 2 to 3 months. We always size the work to the expected business value, not to the technical complexity.
What is the difference between Nobori and a Big Four–type firm for an AI project?
The Big Four often rely on junior consultants supervised by seniors who do not write code. At Nobori, our AI consultants are hands-on practitioners: they design the architecture, write the code, deploy to production and train your teams. We are independent of software vendors (no Dataiku or Databricks resale), which keeps our recommendations objective. And our knowledge-transfer commitment means your teams are autonomous after we leave.

Let's talk about your AI project

Our team works with you from vision to delivery

Book a discovery call

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