Our approach
The different steps
Needs analysis and use case identification
We analyze your business processes and identify AI integration opportunities that bring the most value (automation of repetitive tasks, customer experience improvement, predictive analysis, content generation, etc.). We define priority use cases and success metrics to measure AI's impact on your activity.
Selection of adapted AI technologies and models
We evaluate different available AI options (cloud APIs, open-source models, hybrid solutions) according to your needs, budget constraints and confidentiality requirements. We select the most adapted models and technologies (GPT, Claude, Llama, specialized APIs, custom models) and define the optimal integration architecture.
Integration architecture design
We design the technical architecture of the AI integration by defining entry points in your application, data flows, prompt management, error handling and fallback mechanisms. We ensure the integration respects performance constraints and costs of your existing infrastructure.
AI API development and integration
We create connectors and wrappers that integrate AI APIs into your application. We implement secure authentication, token management, prompt optimization, response processing, error handling and complete logging for reliable operation in production.
Testing and performance optimization
We rigorously test the AI integration in different scenarios: response quality, latency, error handling, usage costs and user experience. We optimize prompts, implement intelligent fallbacks, adjust model parameters and test scalability to guarantee optimal performance.
Monitoring, maintenance and continuous improvement
We set up proactive monitoring of your AI integrations with cost tracking, response quality, performance and usage. We ensure continuous maintenance, updates during AI API evolutions, prompt optimization and continuous improvement based on user feedback and performance metrics.