Data strategy, AI and automation

Notre approche

Unleash the potential of your data and focus your teams on what really creates value with an integrated AI and no-code approach.

Our experience on projects combining CRM, automation and data marketing has allowed us to develop a strong expertise in the structuring and valorization of customer data. We combine no-code tools (Make, n8n, AirTable...) and AI solutions to make your processes intelligent, efficient and measurable. We help you transform your customer knowledge into a driver of efficiency and performance.

Methodologie

We approach data and automation as strategic levers for growth. Our approach is based on design thinking to understand uses, on design sprint to rapidly prototype flows, and on agile principles to iterate and industrialize solutions as they are adopted.

Déroulement projet

Between 3 and 6 months depending on the complexity of your data ecosystem and the maturity level of existing tools.

Phase 1 — Diagnosis and data framework

Analysis of data sources, existing flows and business goals.

Ecosystem mapping (CRM, website, marketing, sales, support) and identification of automation opportunities.

This phase results in a clear vision of the data strategy and a prioritized road map of use cases.

Phase 2 — Design and prototyping

Definition of automation flows and design of AI scenarios.

Rapid prototyping with no-code tools (Make, n8n, AirTable, Notion, Zapier, etc.) to validate data paths and key interactions.

At the end of this phase, an operational MVP makes it possible to test the value and technical feasibility.

Phase 3 — Industrialization and integration

Production of validated flows, connection to internal systems (CRM, ERP, business tools) and access and security management.

Training teams in the maintenance and monitoring of automations.

The system becomes autonomous, documented, and scalable.

Phase 4 — Continuous Optimization and Applied AI

Implementation of performance monitoring dashboards and automatic detection of bottlenecks.

Progressive integration of AI components (predictive analysis, content generation, customer scoring, automatic classification).

A continuous improvement plan guarantees ramp-up and constant adaptation to new uses.

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Livrables

Audit of existing data flows and tools
Mapping sources and customer contact points
Data strategy and automation roadmap
Definition of priority use cases
Target architecture and choice of tools (Make, n8n, AirTable, etc.)
No-code automation scenarios
Functional prototype (MVP)
Technical and functional documentation
Training teams in maintenance and evolution
Monitoring dashboard and automatic alerts
Continuous improvement and AI integration plan