From Zero to 74%
How AI Transformed Remedico’s Customer Support in Two Weeks
The Client’s Profile
The Client’s Profile
Remedico is an innovative dental software provider that helps modern clinics streamline processes, increase treatment acceptance rates, and boost patient retention. Originally launched in the Gulf region, the company aims to expand into other emerging markets, positioning itself as a leader in digitalizing clinic management.
Country:
UAE
Industry:
Software Development
Integration time:
2 weeks
Support Team:
12 agents
Support Setup:
Zoho Desk + Zoho SalesIQ
Case Overview
Founded in 2022 during the AI boom, Remedico knew from the start that leveraging AI for customer support was the way forward. However, finding the right provider took time, leading the company to rely on a human support team initially.
After extensive demos and trials, they eventually found the perfect fit with CoSupport AI. This strategic move paid off, as CoSupport AI’s solutions enabled the company to achieve an impressive 74% deflection rate within the first month of service, significantly streamlining their support processes.
CoSupport AI’s
Tasks
AI-Driven Ticket Resolution
Automate at least 40% of support tickets, allowing Remedico to scale down the support team by 4 agents.
Faster Resolution Times
Achieved over 50% reduction in resolution time, enabling quicker support and improved customer satisfaction.
Seamless Integration
Connect AI with Zoho Desk and Zoho SalesIQ to take over the resolution process for all incoming tickets, while triaging complex tickets that it can’t handle.
Seamless Integration
with Zoho Desk
and
Zoho SalesIQ
CoSupport AI was fully integrated into Zoho Desk and Zoho SalesIQ,
enabling end-to-end automation of customer support:
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AI Model Training
Remedico lacked a public Help Center, so the only data available for AI training was past support tickets (Zoho Desk) and chats (Zoho SalesIQ). The initial model, trained on all historical interactions (~40,000 tickets and chats), delivered inconsistent results due to major Remedico’s product changes over time and some wrong answers provided by support agents. After analysis, the model was retrained using ~12,000 tickets resolved by top performers in the last 6 months, leading to a significant boost in accuracy.
Summary
Summary
Remedico, a UAE-based dental software provider, partnered with CoSupport AI to improve customer support and cut operational costs. After testing several options, they integrated CoSupport AI with their Zoho Desk and Zoho SalesIQ in two weeks. Initial training on 40,000 historical tickets resulted in inconsistent AI response accuracy, so the AI was retrained on 12,000 recent high-quality ones, boosting accuracy. Now resolving 74% of tickets autonomously, CoSupport AI helped Remedico cut six support roles, save thousands monthly, and reduce the resolution time by over 50%.
Client’s feedback
Client’s feedback
The chatbot resolves around 75% of all support tickets now. This allowed us to downscale the support team by 6 team members, which would save us thousands of dollars per month in the long run. I am also very happy with the accuracy of AI responses. I have checked hundreds of conversations and haven’t noticed a single mistake. Collaboration with the CoSupport AI team has been an absolute game-changer for our customer support processes.
Nataliia Romanova
CEO, Remedico
Recommendations
If you’re looking to optimize your customer support operations, CoSupport AI offers a proven, efficient, and scalable solution. Based on our success with Remedico, we recommend the following steps to achieve seamless AI-driven support for your business:
Identify Automation Goals
Define inquiries you want AI to handle versus those requiring human intervention.
Choose the Right Integration
Ensure your AI support solution integrates smoothly with your existing CRM or ticketing system.
Train AI Model
Train the model using carefully filtered tickets, focusing exclusively on those handled by top performers.
Monitor & Optimize
Continuously track AI performance, analyze customer interactions, and refine the model based on new inquiries and data.
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