At Being Guided I am working with West Coast US startup Supercog to integrate complex dataflows between Salesforce and other applications. Here an AI Copilot serves to accelerate and thereby, measurably reduce your integration software development time, costs and risks.
This is integration, faster. But how?
AI-powered Supercog
In using best-in-class Large Language Models (LLMs), such as OpenAI ChatGPT-4.o and Anthropic Claude 3.5 Sonnet we are working with Supercog on a growing number of Salesforce integrations. In future, this will include other AI challengers, such as Meta Llama 3.1 and Google Gemini 1.5.
Using a LLM as a Copilot in integrating Salesforce with Supercog is enhancing our co-development process in several ways, as set-out below. The key to success here is achieving our mission of 'integration, faster'.
Unique Features
Specific Insights: Supercog is able to generate real documentation, provide knowledge about custom apps and true working code. This compares to, say, Chat GPT-4.o, which can only provide generalised answers based on public training data. Beyond the general features of a LLM Copilot, Supercog operates as a 'smart assistant'.
Enabling Modernisation: Supercog is especially suited to understanding and rewriting legacy software applications. As I can show in specific use cases with our early adopters, this is referred to as 'agent groking'1. In practice, this means the Supercog AI can understand the legacy code and related requirements more thoroughly, more intuitively.
1. Grok. Derived from the science fiction term 'grok' meaning deep, empathetic understanding.
Code Generation and Suggestions
LWC Development: AI is assisting our developers by generating boilerplate code for Lightning Web Components (LWC), which are crucial for creating Salesforce-native solutions. This includes templates for common UI elements, Apex classes, and LWC interactions with Salesforce data.
Integration Flows: For integrating with other applications (like health EHR, ERP systems, marketing automation tools, etc.), AI is suggesting and generating code snippets for API interactions, handling JSON/XML formats, and setting up authentication mechanisms.
Documentation and Best Practices
Integration Guidelines: AI is providing real-time guidance on best practices for data integration, security considerations (like OAuth configurations), and performance optimisation.
Component Documentation: AI is automatically generating documentation for the developed components, detailing their functionalities, inputs, and outputs, which is essential for maintenance and scalability.
Debugging and Troubleshooting
Error Handling: AI is suggesting error handling mechanisms and provide debugging support by interpreting error logs and suggesting potential fixes.
Performance Issues: AI is offering advice on common performance bottlenecks in integration scenarios and recommend improvements.
Training and User Support
Developer Training: As new developers join our projects, AI is providing explanatory notes and tutorials on how the existing integration framework operates and how to extend it.
End-User Support: AI is also being set-up to serve as a first line of support for end-users, offering troubleshooting assistance and guidance on using the integrated platform effectively.
Automated Testing
Test Case Generation: AI is helping to generating test cases for the integration points, ensuring that all data flows are tested thoroughly, which is crucial for integrations.
Mock Data: AI is generating mock data for testing the components, ensuring they handle various data scenarios correctly.
Continuous Improvement
Feedback Loop: By analysing the usage patterns and user feedback, AI is suggesting areas of improvement for the integration platform.
Feature Suggestions: Based on emerging trends and common integration needs, AI is proposing new features or components that can be added to the platform.
Project Management Assistance
Task Management: AI is assisting in tracking development tasks, integration milestones, and deadlines.
Communication: AI is drafting communications between team members and stakeholders, ensuring everyone is aligned on the project objectives and progress.
Conclusion
To implement this effectively, it's important to integrate AI seamlessly into the development environment. This is a key part of what Supercog is doing on its journey as startup focused on building a next generation AI-powered Integration for early adopters within the Salesforce ecosystem.