Your partner for successful AI integration

1. Analysis of business needs

Understand the company's strategic objectives and identify opportunities for integrating AI to solve specific problems.

2. Technological audit

Evaluate existing systems and their ability to integrate AI solutions.

3. Definition of the AI strategy

Develop a personalized roadmap aligned with business, functional or operational objectives.

4. Feasibility study

Determine whether the planned AI projects are technically and economically viable.

5. Design of AI solutions

Propose architectures and models adapted to each use case.

6. Data collection

Identify, extract and clean the data needed to train AI models.

7. Data processing and preparation

Perform data standardization, transformation and enrichment operations.

8. Development of AI models

Design, train and optimize machine learning or deep learning models.

9. Validation of models

Test models on test datasets to evaluate their performance.

10. Optimization of algorithms

Adjust models to improve their accuracy, speed and efficiency.

11. Implementation

Integrate AI models into the company's information systems, but also integrate work process changes to avoid potential efficiency losses.

12. Application Development

Create interfaces or software solutions to exploit the results of AI models. (Produced by NormexpertConcept.com)

13. Team training

Train employees to use deployed AI tools and solutions.

14. Development of dashboards

Provide tools to visualize model data and results in real time. (Powered by NormexpertConcept.com)

15. AI Project Management

Oversee the execution of projects while respecting deadlines and budgets.

16. Process Automation

Identify repetitive processes and automate them using AI.

17. Creation of chatbots and virtual assistants

Develop solutions to improve customer service.

18. Predictive analysis

Propose solutions to anticipate future trends and behaviors.

19. Image and text recognition

Implement computer vision or natural language processing systems.

20. Technological monitoring

Follow innovations and trends in the field of AI.

21. Compliance with ethical standards

Ensure responsible and ethical use of AI technologies.

22. Data security

Implement protection protocols to ensure the confidentiality of the data used.

23. Risk management

Identify and mitigate risks associated with the implementation of AI technologies.

24. R&D project management

Collaborate with universities or institutes to innovate.

25. Regulatory advice

Support clients in compliance with laws (e.g.: Law 25, Law 27, GDPR.)

26. Maintenance of AI systems

Ensure monitoring, updating and continuous performance of models, and optimization of technical costs.

27. Return on Investment (ROI) Analysis

Evaluate the impact of AI projects on business results.

28. Communication and documentation

Produce reports and materials to present results to stakeholders.

29. Technical support

Assist teams with issues related to deployed solutions.

30. Strategic expansion

Identify other areas where AI can be applied for greater impact.