AI agency · Generative AI · AI agents
AI agency: bring artificial intelligence into your tools
Do your teams lose time searching for information, answering the same questions or re-entering data? AXELITES, an AI agency, identifies the uses that bring a measurable gain, tests them on your data and integrates them into your software: assistants, AI agents, document search, automation and AI for e-commerce, with data and costs under control.
AI built into your processes
- 2012AXELITES founded
- 300+Projects delivered since 2012
- 6Aurelia AI solutions built by our teams
- 3Offices: Paris, Montréal and Tunis
What we build with AI:
- Internal assistants
- AI agents
- Document search (RAG)
- Customer service
- Product content
- Document processing
- Task automation
- Product recommendations
- Forecasts and anomalies
- ERP, CRM, e-commerce integration
- Response evaluation
- Data security
Useful AI is not just another chatbot.
Generative AI brings real gains, provided you start from a specific task and check the results. Our role combines consulting, development and integration: AI needs to fit into your tools and processes, with the right controls, instead of remaining a separate tool.
AI as a gadget
- An impressive prototype that never reaches production
- Consumer tools used with sensitive data
- Made-up answers that nobody checks
- Usage costs discovered on the invoice
- An isolated AI, with no link to your software
AI integrated by AXELITES
- A use case chosen for its measurable value
- Controlled data and access, suitable hosting
- Sourced answers, evaluated before rollout
- Costs tracked and usage limits defined
- AI connected to your tools, with human review when needed
What our AI agency builds for you
From consulting to production, six areas of work, carried by the team that also builds our Aurelia solutions.
AI consulting and scoping
Workshops with your teams, task mapping and prioritization of use cases by value, feasibility and available data.
- Use-case discovery workshop
- Assessment of available data
- Estimate of value and costs
- A realistic AI roadmap
Assistants and document search (RAG)
An assistant that answers from your documents, procedures and knowledge bases, citing its sources and saying when it does not know.
- Indexing of your document sources
- Sourced, verifiable answers
- Each user’s access rights respected
- Integration into your website or collaboration tool
AI agents and automation
Agents that do more than answer: they create a ticket, check stock or update a record in your tools, within the limits you set.
- Actions in your software through APIs
- Defined rules and level of autonomy
- Human handover on sensitive cases
- Visibility over the actions taken
AI for e-commerce
Product descriptions written from your attributes, more relevant search and filters, recommendations based on your sales: our Aurelia solutions integrate with Magento.
- AureliaCompose: product content
- AureliaExplore: search and filters
- AureliaSuggest: recommendations
- AureliaMoon: customer service agent
Data and predictive AI
Demand forecasting, anomaly detection, document classification: we assess these needs against measurable criteria and the actual quality of your data.
- Forecasts and recommendations
- Anomaly detection
- Document classification and extraction
- Data preparation and quality
Security, compliance and monitoring
Model and hosting chosen according to your data, access control, continuous evaluation of answers and cost tracking in production.
- Models as SaaS or on your infrastructure
- Minimized data and controlled access
- Test sets and continuous evaluation
- Cost tracking and usage limits
From use case to production
An AI project is built in short stages: each one brings evidence before the next is committed.
Workshop
With your teams, we identify the repetitive tasks, pain points and decisions that could benefit from AI.
Stage: ConsultingScoping
For the chosen case: data sources, users, access rules, success criteria and prototype budget.
Stage: DataPrototype
A first version runs on your real data, within a limited scope, to measure the quality of the results.
Stage: DevelopmentEvaluation
Test sets, measurement of errors, time saved and usage costs: the decision to roll out rests on facts.
Stage: QualityIntegration
Connection to your software, access management, human review when needed and user training.
Stage: ProductionMonitoring
Tracking of answer quality, costs and user feedback; updates to sources and models.
Stage: Improvement
Which approach for your AI project?
Off-the-shelf tool, ready-made product or bespoke development: these approaches answer different needs and can be combined.
Swipe to see more
| Criteria | Off-the-shelf AI toolSelf-service consumer assistant | Aurelia solutionAXELITES product to integrateFastest route | Custom AIBespoke development |
|---|---|---|---|
| Set-up | Immediate | Fast, starting from an existing product | In stages: scoping, prototype, integration |
| Fit with your data | Limited to documents provided by hand | Connected to your catalogue and sources | Designed around your data and rules |
| Integration with your software | Low | Magento and connected tools | Full, through your APIs |
| Data control | Depends on the vendor’s terms | Model and hosting chosen with you | Model and hosting in your hands |
| Best for | Individual productivity gains | Customer service, content, search and e-commerce recommendations | Specific business processes |
Combining is common: one project can pair an Aurelia solution with custom developments. Scope my AI project
Why work with an AI agency?
Consumer AI tools are enough for individual gains. As soon as AI has to connect to your data, your software and your processes, you need scoping, development, integration and operations skills: choosing the right use case, preparing data, securing access, measuring quality and controlling costs.
An AI agency like AXELITES brings these skills together in one team. We build our own AI solutions, the Aurelia suite, and have been integrating language models into client projects since 2023, including multilingual after-sales support for an e-commerce website.
Generative AI use cases for businesses
The most profitable projects start from a task that is frequent, time-consuming and verifiable. A few examples by function:
- Customer service: answers to frequent questions, order tracking, request triage
- E-commerce: product content, search, filters and recommendations
- Sales and marketing: proposal drafting, meeting summaries, market watch
- HR and legal: searching procedures, analysing documents
- Operations: extracting data from documents, classification, consistency checks
- IT support: internal assistant, log and incident analysis
Assistant, RAG, AI agent or dedicated model: which approach?
An assistant based on retrieval-augmented generation (RAG) answers from your documents: it finds the relevant passages, then the language model writes an answer that cites its sources. It is the most common approach for a knowledge base or internal support.
An AI agent goes further: it chains steps together and acts in your tools, for example to create a ticket or check stock, within the limits you set. Training or fine-tuning a dedicated model is justified less often, when the task is very specific and examples are plentiful. We recommend the simplest approach that meets your success criteria.
How much does an artificial intelligence project cost?
The budget depends on the use case, the state of the data, the integrations required and the volume of use. It covers scoping, the prototype and integration with your software, then operations: model calls, document retrieval, hosting and monitoring.
Starting with a limited prototype lets you measure the value before investing further. Usage cost is estimated from the prototype stage using real volumes, and usage limits can be set to avoid surprises.
Data, security, GDPR and the AI Act
Before sending data to a model, you need to know where it is processed, who can access it and what the provider’s terms say. Depending on how sensitive the information is, we use models available as SaaS with suitable contractual guarantees, or models hosted on your infrastructure or in Europe.
The GDPR applies as soon as personal data is processed: minimization, informing people, retention periods. The European AI regulation (AI Act) adds obligations depending on the level of risk, such as telling users they are interacting with an AI. These requirements are taken into account from the scoping stage.
Why choose AXELITES as your AI agency
A publisher of AI solutions
We build the Aurelia suite: customer service agent, product content, search, recommendations, testing and monitoring. We know what AI in production requires.
Developers to integrate it
AI is only worth something when connected to your tools. Our teams also build business applications and online stores: integration with your ERP, CRM or Magento is part of the project.
Results measured before rollout
Success criteria set during scoping, test sets, measurement of errors, time saved and costs: you decide on facts.
Your data under control
Model and hosting chosen according to data sensitivity, limited access and human review on sensitive operations.
How we move forward together
Choose the use case
A workshop with your teams compares opportunities by value, feasibility and available data.
Test a prototype
A first version runs on representative examples; we measure quality, errors and cost.
Integrate and roll out
If the results are convincing, we connect the solution to your software, manage access and train users.
Monitor and improve
Answer quality, costs and user feedback are tracked; sources and rules evolve with your needs.
A prototype evaluated on your data before deciding on a wider rollout.
AI that is monitored, not just delivered.
Models evolve, your documents change and so do uses. After go-live, we track answer quality, costs and user feedback, and adjust sources, rules and models.
- Answer quality measured on test sets
- Usage costs tracked, with limits when needed
- User feedback analysed
- Sources and access rights kept up to date
- Model changes tested before switching
Field report: an AI agent for customer service
These figures come from the customer service deployment of AureliaMoon presented by AXELITES. They describe that specific context and may vary with the requests, the available data and the scope given to the agent.
Discover AureliaMoonThe Aurelia suite
Six AI solutions developed by AXELITES, which we integrate and adapt to your tools.
- AureliaMoon · customer service agent
- AureliaCompose · product content
- AureliaExplore · search and filters
- AureliaSuggest · recommendations
- AureliaQA · automated testing
- AureliaWatch · monitoring
Your questions, our answers
Practical answers to help you make the right decisions before starting your project.
Discuss my projectAn AI agency helps a company use artificial intelligence in practical ways: identifying useful use cases, choosing models, preparing data, building and integrating the solution into existing software, then monitoring it in production. AXELITES covers these steps and also builds its own solutions, the Aurelia suite.
Choose a specific task, available data and an outcome users can assess, such as document search, classification or drafting answers. AXELITES defines a prototype and quality criteria before integration. Production deployment depends on observed results, costs and operational constraints.
A chatbot answers questions, often from scripts or a knowledge base. An AI agent understands the request, consults several sources and can act in your tools: create a ticket, check stock, update a record. Its autonomy is framed by rules, with a human handover on sensitive cases.
Yes, provided the necessary interfaces and permissions are available. We define sources, access rules and allowed actions. Tests check answer relevance, references and cases where information is missing. Sensitive operations can retain human approval.
It depends on the task, data sensitivity, usage cost and your hosting constraints. Commercial models available through APIs perform well and are quick to integrate; open-source models can be hosted in Europe or on your infrastructure. We compare the options on your own examples before recommending one.
By making them rare and visible: answers grounded in cited sources, refusal to answer when information is missing, test sets to measure errors before rollout and human review on sensitive decisions. User feedback then feeds the corrections.
Yes. The assistant only accesses authorized sources and respects each user’s permissions. Depending on how sensitive the documents are, the model is used with contractual guarantees that data will not be reused, or hosted on infrastructure you control.
Discovery identifies transmitted data, authorized users, supplier terms and hosting arrangements. Cost includes integration, model calls, document retrieval and monitoring. Usage limits, evaluations and supervision are planned as needed before opening the solution to more users.
Yes, that is actually the most common case. AI is added to your existing tools through their interfaces (APIs): an assistant in your intranet, an agent connected to your ticketing tool, content generated in your Magento back office. Replacing software is not a prerequisite.
The European AI regulation classifies uses by level of risk. Most business assistants mainly face transparency obligations, such as telling users they are interacting with an AI. Some uses, for example in recruitment, are subject to stricter requirements. We identify them during scoping.
Let’s choose your first AI use case
Describe a task, its information sources and the controls you want to keep: we will propose scoping and an evaluated prototype.