AI automation goes beyond classic RPA
RPA (Robotic Process Automation) tools automate fixed and predetermined sequences. They fail as soon as an email is formulated differently, a document changes format, or a contextual decision is required. AI automation, on the other hand, understands content and adapts to context.
A Make or n8n flow enriched with Claude 3.5 Sonnet (Anthropic) can read an incoming email, understand the customer's intention, extract relevant information, consult your CRM, draft a personalized response, and send it, without any human intervention and without the email format being normalized. This is what traditional RPA cannot do.
We design automation architectures that combine workflow orchestration (Make, n8n, Zapier), AI reasoning layer (Claude from Anthropic for complex tasks, DeepSeek R1 for structured analysis at reduced cost, Meta Llama 3 for large volumes in private deployment) and connection to your business systems via API. The result: your teams focus on what really creates value.
Our automation approaches
AI Workflows (Make / n8n / Zapier)
Visual automation of your business flows with an AI layer to understand content, make decisions and manage non-standard cases. Native connectors for 2,000+ tools.
- Automatic qualification of incoming leads
- Customer email processing and sorting
- Generation of weekly reports
- Real-time multi-tool synchronization
Autonomous AI agents
For complex multi-step tasks that require reasoning and decision: an AI agent receives an objective and executes it from start to finish, using your tools like a human collaborator.
- Preparation of commercial files
- Automated competitive monitoring
- Contract analysis and data extraction
- Custom content writing on the fly
Augmented RPA
For companies that already have RPA in place: adding an AI layer on top to manage exceptions, understand unstructured documents and make contextual decisions.
- Management of non-standard cases without intervention
- Reading PDF documents and images (GPT-4o Vision)
- Contextual decision-making on complex business rules
- Log audit of each decision made by the AI
Custom API integrations
When no-code connectors are not enough : development of custom integrations between your proprietary systems, your ERP or internal APIs and the selected AI models.
- Connection to your proprietary APIs
- Real-time webhooks with AI processing
- Queues and asynchronous processing at scale
- Technical documentation and maintenance
Tools and AI models for automation
Concrete examples of AI automation
- Incoming lead qualification : AI reads each new lead (form, email, LinkedIn) and automatically completes the CRM file with score, estimated purchase intention and recommended nurturing sequence.
- Weekly commercial report : every Monday morning, an agent collects CRM data, analyzes it and generates a personalized narrative report for each sales manager, without anyone touching Excel.
- Email management and priority sorting : automatic analysis of each incoming email, categorization (urgency, type of request), draft response writing, automatic escalation if needed.
- Invoice and contract data extraction : automatic reading of all incoming PDFs, extraction of key data (amounts, dates, parties, clauses), insertion into database or CRM.
- Batch generation of marketing content : from a product brief, automatic generation of sheets, e-commerce descriptions, social media posts and emailings, adapted by target segment.
- Intelligent alerts on anomalies : continuous monitoring of your business KPIs (sales, production, quality) with anomaly detection by AI and narrative alerts to the right people with explanatory context.
How an automation project is conducted
Mapping of automatable flows
Inventory of all your manual recurring tasks. Prioritization based on volume, time spent, released value and technical feasibility. You leave the workshop with a prioritized list and an estimated ROI by automation.
Selection of AI model by use case
Claude for complex and nuanced tasks. DeepSeek R1 or Mistral Small for low-cost volumes. GPT-4o Vision for mixed documents. The model is chosen to maximize ROI, not out of habit.
Development of the workflow
Construction on Make, n8n or custom code depending on complexity. Connection to your systems, prompt configuration, error and edge case management. Everything is versioned and documented.
Tests & validation
Tests on real data representative of your volumes. Functional validation with your business teams. Measurement of accuracy and error cases before any production deployment.
Deployment & monitoring
Progressive production deployment. Monitoring dashboard: processed volume, success rate, cost per action, average time. Alerts in case of drift. Follow-up at 30 and 90 days.
Frequently Asked Questions about AI automation
Do you need to give access to our IS for automation?
We work via the official APIs of your tools and never ask for direct access to your infrastructure. For on-premise integrations or with your internal ERPs, we work in an isolated environment with your IT team. Access security is discussed upfront and documented in a security plan before any start.
How to ensure the reliability of automations?
Each automation is tested on real data before deployment. We systematically integrate fallback mechanisms (if the AI is uncertain, the task returns to a human), complete logs for each action, and alert thresholds that notify your teams as soon as the error rate exceeds a predefined threshold.