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Turn an AI demonstration into a reliable business tool.

VISION

Computer vision · YOLO

Detect, count, classify or track objects and events in images or video streams.

  • Feasibility review and representative data
  • Real-time or deferred inference
  • Results integrated into your application
RAG

Retrieval-augmented search

Find the right information in your documents and make the supporting sources traceable in the answer.

  • Source preparation, chunking and indexing
  • Semantic search and citations
  • Access rights and evaluation
COPILOT

AI copilots

Assist your team with a defined task: analyse, write, qualify, search or prepare an action.

  • Prompts and business safeguards
  • Connection to existing tools
  • Measurement of output quality
N8N

n8n automation & orchestration

Connect AI models to tools your team already uses, with workflows that can be reviewed and maintained without bespoke code for every step.

  • APIs, CRM, emails and documents
  • Triggers, checks and error recovery
  • Human approval before sensitive actions

Useful RAG goes beyond a chat interface

Answers need to be relevant, sourced and authorised.

A dependable RAG system requires well-prepared documents, an indexing strategy, suitable retrieval and access rules. It also needs a defined response when the available sources cannot answer the question. European or international deployment adds practical decisions about languages, data location and the providers involved.

  1. Qualify the sourcesIdentify reliable documents, their owners and the data that must remain within a defined boundary.
  2. Build the retrieval pipelinePrepare, segment, index and search content with the appropriate metadata.
  3. Evaluate with real usersTest relevance, traceability and operational usefulness on representative questions with the people who will use the tool.

A working example on this website

A RAG AI assistant that answers in your browser

I built the SDX Development assistant to find website information and follow a conversation about a project, price, delivery time or the current page. Greetings and name recall are handled locally without document retrieval.

RAG runs when the request needs sources. The server retrieves passages; embeddings, writing and checking use the browser’s GPU through WebGPU. A name can be kept in an optional local profile. After navigation or two minutes of inactivity with the chat closed, a personalised invitation can appear beside the widget without opening the conversation window.

Progress documented on 3 October 2026. Generation has no API charge per question, but requires an initial download and a compatible browser. Retrieval stays connected to the server, which may retain unanswered questions. Measurements on S25 Ultra and desktop report total timings and tokens/s. Mobile tests still show missing VAT qualification and citations and a confused timeline; the case study documents these reviewer limitations.

Explore the case study: progress, architecture and tests

The live website assistant, with an answer and its sources.
The live website assistant, with an answer and its sources.

Start with a use case and measurable acceptance criteria

Search your documents

RAG can retrieve useful passages from an authorised collection and ground an answer in those sources. Relevance and errors are evaluated on questions representative of your actual workflows, including the languages the service must support.

Controlled automation

n8n and APIs can connect a request, an approval and a business update. Permissions, errors, alerts and recovery are planned; sensitive decisions retain human approval.

Computer vision

The project starts with representative images and a precise detection objective. Capture conditions, acceptable errors and integration with the field application guide the prototype.

Review data, security and budget before production

A prototype measures feasibility: output quality, response time, failure cases and cost per use. Data sensitivity, access rights, model providers and hosting requirements are examined during discovery, including any European data-location requirements and external services involved.

Model pricing is only one part of the cost. Data preparation, integration, evaluation, monitoring and maintenance are assessed separately. Published prices provide a starting point; production delivery depends on the prototype results and an agreed scope.

RATP Toilettes Connectées — This project illustrates the integration of sensor and operational data into business software. It includes ingestion, synchronisation and correlation; it is not presented as a deployed AI reference.

Questions about an AI project

Does all our data need to be sent to a model?

No. The useful scope, permissions, excluded data and provider are defined for the project. Data flows are designed around those constraints, and any external transfer is considered when choosing the integration.

How is a model selected?

A representative test set makes it possible to compare quality, latency and cost. The choice depends on the task and can evolve without rebuilding the whole integration.

Is a RAG prototype already a production service?

No. Scaling, monitoring, advanced access controls and availability commitments require a separately defined scope and validation before operational use.

An AI idea you want to test against real work?

Let us define the workflow, available data and decision to improve before selecting a model or technology. We can review the use case remotely with your European or international team.