- Web development
Artificial Intelligence and Cybersecurity: Protect Your Digital Products
AI Web Project Management: Upgrade Your Work Methods
Artificial intelligence is no longer just a technological promise: it is now at the very core of every stage of software design.
Toolseeta, project manager in Mauritius, is a key figure in IT project management at Esokia. On a daily basis, she orchestrates the development of custom web and mobile applications for international clients across various sectors such as real estate, travel, and services.
To understand how AI and project management intersect in practice on the ground, explore this exclusive interview with Toolseeta. From initial scoping to final delivery, go behind the scenes of a hybrid methodology that combines productivity gains, business innovation, and absolute data security.
How would you describe the evolution of client demand regarding AI at Esokia?
Today, there are two key aspects to how we use AI in our projects:
Building custom AI tools for our clients' platforms: For instance, developing a chatbot specifically trained on the client's industry and core business.
Leveraging existing AI tools internally: We harness these technologies to speed up development, design, and quality assurance (QA) phases during platform delivery.
Beyond the buzzword, what is the tangible value you bring to clients by integrating tools like Gemini or Claude?
The main benefit comes down to time. With the help of these tools, we spend significantly less time on time-consuming tasks such as writing specifications or generating code. This also greatly optimizes the verification and testing phases for the applications we build.
You work across diverse sectors (Travel, Real Estate, Food Delivery...). How is AI specifically applied in the field?
AI comes into play right from the very early stages of a project. During the pre-sales phase, AI tools help us understand client needs, analyze existing systems, and explore potential solutions.
We then integrate it throughout the entire value chain:
Scoping & Documentation: We rely on AI to accelerate the creation of solution presentations and to draft foundational documents (requirements specifications and user acceptance test plans).
UI/UX Design: The next step involves creating visual wireframes for websites or mobile apps, where AI tools directly assist us.
Development: During the dev phase, assistants like Claude or Copilot help generate code, which developers inspect and refine before implementation.
Testing & QA: AI tools are also used to execute and assist with test cases. Testers run them manually the first time, then automate the process so it runs seamlessly in subsequent cycles.
What were the main technical challenges during this integration?
The primary challenge is thoroughly understanding the client’s business domain to run the analysis behind a coherent proposal. When it comes to the technical setup of the tool, the core principles remain unchanged. The main hurdle continues to be the learning and training phase of the custom AI tool to make it fully effective.
How do you manage the transition between traditional development and integrating AI modules?
For us, AI doesn't replace traditional development—it enriches it. We approach this transition through a hybrid, step-by-step model:
The Modular Approach (API-first): We build solid architectures first. AI is then integrated as independent "building blocks" via APIs. This allows us to add or update an AI model without ever destabilizing the core application.
The POC (Proof of Concept) Method: Before deploying AI at scale, we validate its use on a restricted scope to test response relevance and measure concrete Return on Investment (ROI).
Change Management: Integrating AI alters habits. We support our clients with early user-testing phases to ensure the AI module is intuitive and well-adopted.
In your methodology, how do you ensure the security and reliability of client data?
It is a crucial question because our clients entrust us with their most valuable assets. We apply a security policy built on three key pillars:
Isolated Environments: Through "Enterprise" access or cloud services (Azure, AWS), we guarantee that sent data is never used to retrain public models.
Anonymization & Filtering: Prior to sending data to an LLM, cleaning scripts mask all personal data (GDPR) or confidential information.
Human-in-the-Loop Validation: AI proposes, humans validate. No critical decision or major database update is 100% automated without prior human review.
How have teams (developers, project managers) adapted their working methods?
Integrating assistants like Copilot or Claude has shifted our value added toward more strategic tasks.
For Project Managers: We spend less time on basic descriptive writing and more on prompt engineering and business process analysis.
For Developers: The role is evolving toward that of a reviewer and architect. Since AI generates repetitive functions in seconds, developers can focus on performance optimization and security.
The golden rule remains critical thinking. We train our teams never to trust AI blindly. Every line of code generated and every specification produced is thoroughly reviewed and tested with rigor.
What next major AI integration trends do you foresee for businesses?
The market is moving beyond the basic stage of a "chatbot answering questions." We see three major trends emerging:
Autonomous AI Agents (Agentic AI): Granting autonomy to AI to execute complex workflows (e.g., analyzing an incoming email, querying the CRM, drafting a response, and updating a delivery ticket under supervision).
Ultra-Fast Multimodal AI: Native integration of real-time voice and image analysis (e.g., taking a photo of an issue in the field to automatically generate a technical report).
Advanced Semantic Search (RAG): Enabling companies to query their internal knowledge base (such as in advanced document management projects) smoothly and with source attribution.
What advice would you give to a client who is still hesitating?
My advice is very pragmatic: identify the most repetitive and time-consuming friction point for your teams on a daily basis (data entry, customer support, document search).
Test AI on that specific use case through a 2-to-4-week pilot project (POC). This will allow you to measure immediate time savings and ROI, gently onboard your teams, and understand the true potential before rolling out more ambitious initiatives.
Ready to Integrate AI into Your Business Application?
At Esokia, we help you identify your AI opportunities and design high-performing, secure, custom web and mobile applications.
Contact our experts today at [email protected] to map out your AI project.
