Our Blog
Practical guides and insights on software development, AI and digital technology.
Cloud Infrastructure for AI: A Practitioner's Comparison of AWS, GCP, Azure and OVHcloud
A practitioner's comparison of AWS, GCP, Azure and OVHcloud for AI projects: GPUs, vector storage, costs and European data sovereignty.
AI Agents for After-Sales Support: Managing Service Without Hurting the Customer Experience
AI customer support agents: technical architecture, prompt engineering and human escalation for efficient support without sacrificing quality.
Automated Testing in the AI Era: Write Less, Cover More
Discover how AI transforms automated testing: broader coverage, 70% less maintenance and proven ROI. A strategic guide for CIOs and CTOs.
CI/CD for AI projects: deploy your models without breaking production
CI/CD for AI projects: ML pipelines, model versioning and automated rollback. A complete guide to production deployment without regressions.
API Design for AI Projects: Patterns and Antipatterns
API design for AI projects: REST, GraphQL, WebSocket and MCP patterns for connecting applications to LLMs and agents. An expert guide.
Agile Project Management and AI: Adapting Scrum When Speed Triples
How to adapt Scrum sprints, reviews and retrospectives when AI triples development speed. A practical guide for CIOs and project managers.
B2B AI Sales Agents: Myth or Reality for Your Sales Team?
B2B AI sales agents: discover what they really automate—prospecting, qualification and follow-up—and what remains a human responsibility.
Custom AI Agent vs. Automation SaaS: The Complete Decision Guide
Custom AI agent or automation SaaS such as Zapier, Make or n8n? A decision matrix, real costs and criteria for choosing the right approach.
AI in Corporate Finance: Automating Without Putting Compliance at Risk
AI in corporate finance: explore mature use cases, from account reconciliation to fraud detection, without compromising compliance.
Technical Escalation: Managing AI Project Crises Without Losing Client Trust
Technical escalation on an AI project: communication protocols, incident management and the provider's approach to preserving client trust.