FREN

Let’s keep in touch

Social

Artificial intelligence

Artificial intelligence

Automate your processes and boost your products with AI: integrations that hold up in production, not demos.

Talk about your projectSee the lab
We write faster, but we check no less. 45% fewer development days, with stronger project management and testing. See our production chain with AI

01Approach

At iones, we build artificial intelligence into the core of your products and operations: RAG on your private data, autonomous agents, ROI-driven AI strategy, with the same quality standards as our Build & Run deliveries.
An AI prototype can be put together in two days. A reliable, secure and maintainable AI service is an engineering project: data quality, answer evaluation, guardrails, inference costs, monitoring. That is the work we take on, from the idea to the run.
Every building block we offer is first tested in-house in our iones lab: we measure before we recommend, and we also publish what does not work.

Scope of work

  • Assistants and search across private documents
  • Agents connected to your business tools
  • AI features in your web and mobile products
  • AI audit and roadmap
  • Training for business and tech teams
  • Run, monitoring and cost control

02Our offers

01 / 03

LLM and RAG integration

Have AI answer from your company data, with precise, sourced answers and no information leaks.

Your contracts, procedures, tickets, product sheets and knowledge bases hold the answers your teams and customers look for every day. RAG (retrieval-augmented generation) connects a language model to this content: the question triggers a search in your documents, and the model writes its answer only from the passages retrieved.
The difficulty is not plugging in a model, it is getting accurate, verifiable answers. We work on the whole chain: preparing and splitting documents, hybrid search (semantic and exact words), re-ranking of results, mandatory citation of sources, and refusing to answer when the documentation does not contain the information.
On the security side, your data stays compartmentalised: access rights carried over from your tools, hosting in France or on your own infrastructure, models used without retraining on your content, logging of requests.

What we deliver

  • Indexing pipeline for your sources: PDFs, office documents, wikis, tickets, SQL databases
  • Hybrid search engine with result re-ranking
  • Conversational assistant with cited, clickable sources
  • Connection to your IS: ERP, CRM, document management, intranet
  • Access rights management by user and by project
  • Evaluation set and quality dashboard: accuracy, refusal rate, latency
  • Operating documentation and re-indexing plan

Practical use cases

Internal document assistant

Your teams query procedures, contracts and meeting minutes in natural language and get the answer with the source passage, instead of searching through ten shared folders.

Augmented customer support

Support agents receive a suggested reply based on the knowledge base and the ticket history, which they approve before sending.

Intelligent product search

On an e-commerce site or a B2B catalogue, users describe what they need and get the relevant references, even without knowing the right keyword.

Analysis of large documents

Extraction of clauses, comparison of versions, summaries of tenders or technical reports, always with a reference back to the original page.

How we go about it

  1. 01

    Source audit

    Inventory of the documents, their quality, their freshness and the associated access rights.

  2. 02

    Measured prototype

    A first pipeline on a limited scope, evaluated on a set of real questions written with your teams.

  3. 03

    Industrialisation

    Integration with your tools, rights management, monitoring, automatic re-indexing.

  4. 04

    Continuous improvement

    Tracking of unanswered questions, enrichment of the documentation, tuning of the pipeline.

02 / 03

Autonomous agents

Automate your time-consuming tasks and workflows with agents connected to your business tools, under human control.

An agent does not just answer: it reads, decides and acts. It looks up a ticket, queries a database, fills in a form, writes a report, triggers an action in your CRM. Properly scoped, it absorbs the repetitive tasks that overload your teams and cuts handling times from several hours to a few seconds.
Our conviction, built on our own trials: autonomy is a dial, not a switch. Agents are excellent at reading, qualifying, summarising and preparing. They are less reliable as soon as they have to decide alone on a subtle business rule. So we design each agent with an explicit scope of action, technical guardrails and human validation where an error is costly.
Technically, we connect agents to your tools through standard interfaces (API, MCP protocol), read-only by default, with an allowlist of permitted actions, a budget per task and full traceability of every decision.

What we deliver

  • Map of the tasks that can be automated and the expected gain
  • Custom agents connected to your tools: CRM, ERP, ticketing, messaging
  • Internal copilots for your support, sales, HR or technical teams
  • Guardrails: allowlist of actions, human validation, budgets, audit log
  • MCP connectors and APIs to your information system
  • Monitoring dashboard: volumes handled, acceptance rate, costs
  • Fallback procedure and manual recovery plan

Practical use cases

Ticket triage and qualification

The agent reads each incoming request, categorises it, suggests a priority, writes a technical summary and routes it to the right team before any human intervention.

Processing of incoming requests

Quotes, contact forms, job applications: extraction of the information, completeness check, creation of the record in the CRM and a draft reply.

Business copilot

An assistant that knows your procedures and your data, prepares files, writes meeting minutes and reminds you of the steps not to forget.

Multi-tool workflow automation

Reconciliation of data across several systems, generation of periodic reports, reminders and updates that used to require manual copy and paste.

How we go about it

  1. 01

    Choosing the right case

    A frequent task, clear rules, a reversible error: we start where the gain is certain.

  2. 02

    Agent under observation

    The agent suggests, the human decides. We measure the agreement rate on real cases.

  3. 03

    Graduated autonomy

    Action categories whose observed agreement rate exceeds 95% become automatic; the others stay under control.

  4. 04

    Monitoring

    Tracking of volumes, costs and discrepancies, with alerts and an emergency stop.

03 / 03

Strategy & AI literacy

Identify high-ROI AI opportunities, for you and for your clients, and bring your teams to concrete, measurable adoption.

Most AI projects that fail do not fail on the technology. They fail because the use case was poorly chosen, the data was not ready, or the teams never adopted the tool. Before writing a line of code, we help you answer three questions: where does AI really create value for you, in what order to proceed, and how to measure it.
For digital agencies, the challenge is twofold: gaining productivity internally, and being able to offer credible AI services to their own clients. We step in under white label or alongside you to qualify requests, estimate and deliver.
Finally, building AI literacy is not a one-hour lecture. It means hands-on workshops by job function, on your cases and your tools, clear usage rules (confidentiality, verification, intellectual property) and shared tooling so that good practices do not depend on a few enthusiasts.

What you get

  • AI maturity audit: data, tools, skills, regulatory constraints
  • Map of use cases ranked by value, feasibility and risk
  • Phased roadmap with budget and success indicators
  • Scoping and estimate of POCs, with explicit stop criteria
  • AI literacy workshops by function: management, marketing, support, development
  • AI usage charter and data confidentiality policy
  • Tooling for development teams: code assistants, shared skills, MCP servers

Practical use cases

Agency launching an AI offer

Building the catalogue, the sales pitches and the first estimates, then white-label delivery by our teams on signed projects.

Management that wants to prioritise

Two weeks of workshops and interviews to move from a list of ideas to three ranked, costed projects with indicators.

Development team to equip

Setting up shared AI tooling, common conventions and access rules, with measurement of the time saved and the effect on the quality of code reviews.

Compliance

Review of existing uses, risk classification under the GDPR and the AI Act, and a realistic compliance plan.

How we go about it

  1. 01

    Diagnosis

    Interviews, review of tools and data, inventory of the AI uses already in place, official or not.

  2. 02

    Prioritisation

    Workshop to score use cases: value, feasibility, risk, timeframe.

  3. 03

    Roadmap

    Three horizons: quick wins, foundational projects, bets to be investigated through a POC.

  4. 04

    Support

    Workshops, tooling, quarterly measurement points and course adjustments.

03Why iones

Why entrust your AI projects to iones

A senior engineering team that treats AI as a production component, not as hype.

Tested before it is offered

Every building block goes through our internal lab. We recommend what we have measured, and we say what does not work yet.

Your data stays yours

Separation by project, hosting in France or on your own infrastructure, a confidentiality commitment and no retraining on your content.

Technological independence

An architecture that does not lock you in to any vendor: we route each use to the right model and can switch models without rewriting everything.

A measured ROI

Indicators defined at scoping, measured in production, and explicit stop criteria: a POC that proves nothing is stopped.

04Method

Our method for an AI project

4 steps

  1. Step 01

    Scoping

    • Use case and success indicators
    • Audit of available data
    • Security and compliance constraints
    • Estimate of inference costs
  2. Step 02

    Measured POC

    • Limited scope, short timeframe
    • Evaluation set on real cases
    • Stop criteria defined in advance
    • Decision: continue, adjust or stop
  3. Step 03

    Industrialisation

    • Integration with your IS
    • Guardrails and rights management
    • Automated tests and CI/CD
    • Documentation and knowledge transfer
  4. Step 04

    Run & improvement

    • Monitoring of quality, latency and costs
    • Model watch and updates
    • Handling of user feedback
    • Quarterly ROI review

Start a project

Ready to take action?

Tell us what you need – we will get back to you quickly with a technical assessment and an action plan.

  • Reply within 48–72 hours
  • No-obligation conversation
  • NDA confidentiality
How can we help you?