Integral, a crypto SaaS provider, struggled with slow customer support response times, impacting client satisfaction and team morale. Their support team was overwhelmed, spending hours crafting responses to repetitive queries. At Datify, we implemented an AI-driven solution that leveraged their existing documentation and integrated an LLM-powered support chat tool. The result? Drastically reduced response times, happier customers, and a support team empowered to focus on complex issues—all while maintaining a personal touch.

Integral had built a loyal customer base with their innovative SaaS platform, but their rapid growth exposed critical inefficiencies in their customer support operations: ​ • Delayed Responses: With a growing user base, the support team took an average of 4 hours to respond to customer inquiries, leading to frustration and a 10% drop in customer satisfaction scores. • Repetitive Queries: Over 70% of support tickets were repetitive, involving basic troubleshooting or feature-related questions already covered in their documentation—but the team lacked a quick way to access and utilize this information. • Overworked Team: The small support team of two dedicated agents was stretched thin, often working late to clear ticket backlogs, which led to burnout and high turnover. • Inconsistent Responses: Without a centralized knowledge base integration, agents often provided inconsistent answers, risking brand credibility. • Missed Opportunities: Slow response times meant missed opportunities to upsell or cross-sell during customer interactions, stunting revenue growth. ​ Integral needed a scalable, AI-driven solution to streamline their support process, reduce response times, and empower their team to focus on high-value interactions—all while maintaining the personalized service their customers expected.

We designed a robust, AI-powered system to revolutionize Integral's customer support workflow, integrating seamlessly with their existing tools: ​ • Documentation Scraping & Categorization: We developed a Python script to scrape every page of Integral's extensive documentation, extracting content and metadata. The script output an Excel sheet with each page's URL and an AI-assigned label, matching the category labels used in their support chat tool (e.g., "Billing," "Troubleshooting," "Feature Requests"). • LLM-Powered Contextual Responses: We integrated the categorized documentation into their support chat tool, enabling it to natively query a large language model (LLM) with the right context. When a customer submitted a query, the tool automatically identified the relevant category, pulled the appropriate documentation, and suggested a tailored response in seconds. • Agent-Friendly Interface: We built a custom dashboard within the support tool, allowing agents to review AI-suggested responses, edit them for tone, and send them with a single click. • Continuous Learning: The system was designed to learn from agent edits, refining its suggestions over time to better align with Integral's brand voice and customer expectations. • Integration with Existing Tools: We connected the solution to Integral's CRM ticketing system (Plain), ensuring seamless tracking of customer interactions and enabling agents to identify upsell opportunities during support conversations. ​ The result was a game-changing system: Integral's support team could now respond to customer inquiries in under 2 minutes, with AI-suggested responses that were accurate, on-brand, and contextually relevant. This freed agents to focus on complex issues and relationship-building, transforming support into a strategic asset.