SaaS Application - DataWise

The SaaS Application - DataWise project is a practical example of the Dev-AI experience, demonstrating how innovation in the development process and a robust strategy can generate a high-value product.

1. Project Overview

DataWise is a corporate, multi-tenant, and scalable SaaS platform designed to unify data, documents, and compliance processes in a singular environment governed by artificial intelligence. This project was born from the critical need to empower medium and large organizations to manage their information more efficiently, securely, and autonomously, overcoming fragmentation and technical dependence.

2. The Real Problem

Medium and large organizations frequently face a series of operational and strategic bottlenecks: data fragmented across multiple systems, difficulty accessing reliable information quickly, low governance over the data lifecycle, and excessive reliance on technical teams for basic queries and reports. This reality results in slow decision-making, increased operational costs, compliance risks, and an unsatisfactory user experience for business areas. What was broken was the fluidity and security of information, and this mattered because it compromised agility, compliance, and competitiveness.

3. Insight and Strategy

The diagnosis indicated that the root of the problem was not a lack of data, but the absence of an intelligent layer to unify, govern, and make it accessible. My strategy was to develop a platform that acted as an "intelligence hub," using AI not only to interact with data but also to accelerate the system's development itself. The key decision was to adopt a robust multi-tenant architecture, prioritizing security and governance from design (security by design and privacy by design), with rigorous validations at each phase. The biggest challenge was balancing performance, scalability, and operational simplicity, ensuring that AI was a means to solve real problems, not an end in itself. This meant investing in software engineering for instructional prompts, guiding Dev AI Agents and ensuring code and architecture integrity.

4. The Solution Developed

DataWise functions as an intelligent interface between legacy systems, databases, and end-users. It allows business users to perform complex queries in natural language, automate tasks, generate APIs, and build auditable knowledge bases, all in a secure and governed environment. The platform is composed of specialized modules:

• Core-QueryWise: Intelligent knowledge base for documents, policies, and URLs, with contextual responses in natural language.
• DB-QueryWise: Database queries in natural language, generating automatic SQL and real-time insights.
• API-QueryWise: Automatic generation of secure and documented REST APIs, facilitating integrations.
• Code-QueryWise: Software engineering assistance, generating code, tests, and documentation.
• LGPD-QueryWise: Automation of data privacy compliance and auditing.
• Legal-QueryWise: Legal analysis and contracts with specialized AI.
• DPO-QueryWise: Complete management for DPOs, including requests and executive reports.

Its differential lies in its ability to democratize access to information and automate high-value processes, without the need for constant intervention from technical teams.

5. Technologies Used (with Purpose)

The technological choice was guided by the need for robustness, scalability, and, crucially, by optimizing the AI-assisted development process.

• Frontend: React (for a dynamic and responsive interface), Material-UI (ensuring visual consistency and intuitive user experience).
• Backend: Node.js with Express (for efficient and scalable APIs), Python (main engine for AI processing and complex prompt engineering), PostgreSQL (relational database for transactional and governance data, ensuring integrity and scalability).
• AI / Automation: Large Language Models (LLMs) via APIs (for natural language processing, SQL generation, code, and contextual responses), Embeddings (for semantic search and document contextualization), Software Engineering with Instructional Prompts (methodology to guide Dev AI Agents, ensuring security and adherence to requirements in 70-80% of development).
• Infrastructure: Google Cloud Platform (GCP) (for scalability, security, and managed services), Docker and Kubernetes (for container orchestration, ensuring resilience and portability), API Gateway (for secure API management).
• Other resources: Integration with IBM WatsonX Governance and Guardian (to strengthen AI governance, traceability, risk control, and regulatory compliance throughout the AI lifecycle). Application of Pentests and security tools (SAST/DAST) at all stages to mitigate risks such as Prompt Injection, SQL Injection, and ensure data governance.

6. Delivered Results

The results of DataWise are concrete and measurable. The platform allowed a drastic reduction in development time, from 6-9 months with a team of five developers to just four months with a single professional, intensively using AI in a secure and governed manner. For clients, the gains include:

• Efficiency Gains: Autonomy for business areas to access and analyze data, reducing IT dependence by up to 60%.
• Cost and Time Reduction: Optimization of operational and compliance processes, saving valuable time and resources.
• Scalability Achieved: Multi-tenant architecture that allows rapid onboarding of new clients, with guaranteed data segregation.
• Improved User Experience: Intuitive interface and natural language queries that make interaction with complex data accessible to everyone.
• Risk Mitigation: Robust data and AI governance, auditable logs, and LGPD compliance, strengthened by integration with IBM WatsonX, which generate high confidence in platform usage.

7. Differentiators and Learnings

What makes DataWise unique is its conception and development, where AI was an essential partner, not just a functionality. The construction process, 80% AI-assisted, with software engineering applied to instructional prompts and a battery of pentests at each phase, proved the feasibility of drastically accelerating innovation with security. I learned to deepen prompt engineering to exert precise control over Dev AI Agents, ensuring that the generated code was robust, secure, and aligned with requirements. This project elevated my technical and strategic repertoire, demonstrating how it is possible to transform complex problems into intelligent and future-ready digital solutions, combining design thinking with cutting-edge technologies.