Attained a 20% faster Development Time and amplified the speed of experimentation by 2x using Generative AI + Low Code

Solution Architecture - 

We developed the NL Platform as an application, with its framework based on Python. This framework supports interactions with various Low-Code platforms, allowing users to create, modify, or delete specific functionalities. Initially, users are required to connect to the Low-Code platform. Post-connection, the NL Input Processor aids in interpreting user intents, extracting critical information, and generating meaningful commands or responses. This functionality is powered by Large Language Models (LLMs). Additionally, we harnessed OpenAI's fine-tuned models through APIs to enable this feature. Moving forward, these commands are processed, and relevant actions are executed at the core of the NL platform.

Understanding the Data flow - 

The user NL queries will be sent to the NL platform core, which further sends the inputs to the processor and generates the command of action with the help of OpenAI fine-tuned Large Language models. The core will then process these commands of action, and appropriate actions, such as code completion, code generation, code optimization,  etc., will be taken. Once the action is completed successfully, the vulnerability detector checks the performed action for security-related vulnerabilities. This way, the user will be notified of the changes on the platform.

The customer aimed to provide startups and enterprises with a comprehensive solution enabling them to develop applications and quickly bring their ideas to market. Velotio took up the challenge of creating a unique platform within the given timeframe with a team of front-end engineers, back-end engineers, UX designers, and QA specialists.

We structured the complete project in four parts. Initially, we comprehensively analyzed the customer's platform. After thorough research and brainstorming, we devised a plan to develop an NL Platform for code generation, completion, review, optimization, bug detection, test cases, and documentation generation. We planned to harness the power of OpenAI's codex to generate code based on natural language prompts. In part 3, we integrated the Low code platform with the NL platform. Lastly, we constructed a smooth system for Maintaining, monitoring and supporting the platform in multiple aspects.

Part 1 – Understanding Customer’s Low-Code Platform

During the initial phase, we conducted multiple sessions with the customer's teams to gain a deep understanding of their low-code platform, focusing on its backend, frontend, and underlying technological aspects.

• Application Architecture and Technology Stack – We examined the platform's underlying technology stack, programming languages, and frameworks to assess its compatibility and potential challenges during integration.
• APIs and Integration Capabilities – We evaluated the platform's APIs and integration capabilities to ensure it supports the communication protocols and data formats required for integration.
• Security Mechanisms – We assessed the multiple Security features of the platform, like data encryption, user authentication, and authorization mechanisms. 
• Scalability and Performance – We evaluated the platform's scalability and performance capabilities to understand whether it will handle the increased load and data processing requirements resulting from integration with the NL Platform.
• Data Handling and Transformation – We analyzed the data handling capacity of the platform to ensure that it effectively handles data transformation between applications with different data structures and formats. We also had to maintain data consistency and integrity throughout the integration process.
• Error Handling and Logging – We also examined the platform's error handling mechanisms and logging capabilities, so it prompts meaningful errors and logs to aid in troubleshooting and debugging during integration.
• Customization and Extensibility – We also determined the platform's flexibility in terms of customization and extensibility, so it allows building custom modules and integrating external services.
• Versioning and Upgrades – We also understood how the low-code platform handles versioning and upgrades so it does not break existing integrations with other applications.
• Documentation and Support – We thoroughly reviewed the platform's documentation and support resources, so we have invaluable insights during the integration process and help resolve potential technical issues.
• Regulatory Compliance – We ensured the platform's sensitive data that is subject to specific regulatory requirements like GDPR HIPAA. So, while building the NL platform, we ensured it complies with these regulations to maintain data privacy and security.

Part 2 – Building the NL Platform

• Code Generation and Completion-Natural Language Models –We utilized OpenAI's GPT-4 LLM for code Generation and auto-completion with natural language prompts.
• Code Review and Optimization - Static Code Analysis – We employed AI-driven static code analysis techniques that automatically identify potential issues, recommend improvements, and optimize code performance, ensuring high-quality code.
• Bug Detection Implemented anomaly detection algorithms that automatically identify and flag potential bugs, anomalies, or vulnerabilities in the code. This helped developers address them promptly.
• Security Analysis - Vulnerability Scanning – We leveraged AI-powered vulnerability scanning techniques to thoroughly analyze the codebase and detect security vulnerabilities and potential exploits, enhancing the overall security posture of the application.‍

Test Case Generation and Documentation

• Automated Test Case Generation - We implemented AI-based techniques, such as symbolic execution and search algorithms, that auto-generated comprehensive test cases and covered various code paths to ensure robust and efficient testing.
• Documentation - Using NL generation algorithms, we enabled the automatic generation of clear, concise, and comprehensive documentation from code comments, annotations, and metadata.
• Training the Model (Supervised Learning) - We employed supervised learning techniques and trained AI models on vast datasets of code repositories, enabling them to understand and generate code snippets and perform other related tasks accurately.
• Security, Compliance, and Data Privacy Considerations  - Encryption and Access Controls – We Implemented robust encryption mechanisms and strict access controls to ensure the confidentiality and integrity of sensitive data throughout the NLP platform.
• Data Anonymization - We applied data anonymization techniques to protect user privacy by removing personally identifiable information (PII) from code snippets and other data sources used for training and analysis.

Part 3 - Integration of Low-Code Platform & NL Platform  

• Defining Integration Objectives – We identified specific use cases where natural language processing can significantly enhance the low-code platform. These include creating the forms for any particular use case, integrating the monitoring system into the platform, integrating new user login flow, etc. 
• Implementing API for NL Platform – We built Internal APIs to receive and process commands like “Update database schema,” “add a new workflow,” etc. from the NL platform. These APIs were designed to handle various actions, security validations, and error handling.
• Training and Fine-Tuning NLU Model – We trained the model using labeled data representing the Natural Language (NL) commands and their corresponding actions within the low-code platform. Furthermore, we fine-tuned the model through iterative testing and seamlessly integrated it into the platform.
• Defining NL Commands and Actions – We identified specific commands and actions that the Natural Language platform can understand and execute within the low-code platform. These included defining NL commands such as “add a new feature,” “remove an existing feature,” “update database schema,” and many more. By establishing this clear understanding, users could seamlessly interact with the low-code platform using natural language, simplifying the development process and enhancing user experience.
• Integrated the custom fine-tuned models – Through the NL platform interface, when users issue commands, the custom fine-tuned models interpret and convert them into meaningful commands or actions, representing the intended actions on the low-code platform. This seamless process allowed users to interact naturally with the platform, and their commands were intelligently translated, enabling smooth execution of desired actions.‍
• Implemented Actions – We developed the essential backend code to execute the actions generated by the NL platform. This process involved modifying existing features, adding new components, updating the database schema, and performing various other tasks. By implementing these backend functionalities, the low-code platform could translate user commands from natural language into tangible actions.‍
• Testing – We conducted comprehensive testing of the integration between the low-code and NL platforms. We identified and addressed some issues or inaccuracies in command interpretation and execution. Additionally, we continuously refined the integration based on user feedback and usage analytics.‍
• Deployment and monitoring – After thoroughly testing the integration, we deployed it to the production environment of the low-code platform. We monitored its performance usage and gathered user feedback to ensure it functions as expected and delivers significant value to users. We could make necessary adjustments or improvements by closely monitoring its performance and user reception, guaranteeing a smooth integration.

Part 4 – Maintenance, monitoring, support, and future areas of enhancements  

Maintenance

• We implemented a bug-tracking system to log and track reported issues. This allowed users to report problems encountered while interacting with the NL platform or controlling the low-code applications through natural language.
• Furthermore, we performed regular regression testing to ensure that any updates or fixes introduced do not negatively impact existing functionalities. We maintained the platform's stability and reliability by conducting these tests, guaranteeing swift resolution of reported issues.

Monitoring

• We set up real-time alerts to notify the development and support teams promptly about critical issues or unusual patterns detected during monitoring.
• Additionally, we implemented a comprehensive error logging system to capture and analyze any errors or exceptions occurring in the integrated system. 

Support

• We created comprehensive user documentation and guides that detail the usage of the NL platform.
• We also offered training sessions and onboarding resources to help users understand and master the platform.

Future areas of enhancements

• Intuitive Suggestions – We plan to integrate intelligent suggestion mechanisms driven by user behavior and common actions. We also plan to have some intuitive options or recommendations to streamline their workflows and optimize task completion.

• Real-time Collaboration – We plan to introduce real-time collaboration features to enable concurrent interactions with the NL platform for multiple users. This will foster seamless teamwork and enhance overall efficiency within teams.

• Version Control and Rollback Plan – We plan to meticulously implement version control functionality for the integrated system to record changes made over time. This will ensure easy tracking and, if needed, effortless rollback to previous states in case of unexpected issues arising from updates.