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Generative AI for 80% Customer Support Automation
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Generative AI for 80% Customer Support Automation

Discover how SOAX achieved 80% customer support automation, while improving CSAT by 95%, through a layered generative AI system built for reliability and scale.

Kirill Markin
Former Head of R&D, SOAX
Rating
5
(2)
Format
PDF
Page Count
35
Published
April 25, 2025
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Generative AI for 80% Customer Support Automation
What’s inside?
Table of contents
Meet the Experts

Discover how SOAX achieved 80% customer support automation – while improving CSAT by 95% – through a layered generative AI system built for reliability and scale.

The expert: Kirill Markin, Former Head of R&D at SOAX.

The organization: SOAX, a mid-sized global data extraction platform helping businesses gather web data efficiently. The company focuses on scaling automation within data-intensive, user-facing environments.

The problem: SOAX needed to deliver 24/7 customer support while tackling scalability, repetitive queries, and resource strain -- without sacrificing the quality and speed of responses.

The solution: A layered AI customer support system that uses ChatGPT and complementary tools to automate 80% of queries while smartly escalating complex issues to human agents.

Key decisions
  • Build layered reasoning and cross-referencing to reduce hallucinations and improve context
  • Embed AI workflows into daily operations
  • Select LLMs based on task requirements, not hype
  • Encourage tight, cross-functional collaboration between tech and support
Key results
  • 80% automation of support queries
  • 30–35% reduction in support hours
  • 95% increase in customer satisfaction (CSAT)
  • Maintained human oversight for complex, high-context interactions

1. Interface versus Intelligence: Rethinking Chatbots in Real-World Applications

Why chatbots fail to deliver in real-world scenarios. Underscores the need to move beyond the chat-interface hype to smart and reliable solutions.

2. Who is SOAX?

The case study subject, SOAX.

3. The Pain-Point Drive to AI

The key pain-points that drove SOAX to adopt AI solutions.

4. The Solution: An AI-Powered, Smart Chatbot

The key functions and outcomes of SOAX's AI-powered, smart chatbot.

5. Development and Deployment

The pre-launch decisions, the tech stack, the iterative development approach, the build process, and the overall planning and strategy.

6. Ethical and Security Compliance

The measures implemented for ethical and security compliance.

7. Implementation and Integration (1.5-3 months)

How SOAX integrated its AI solution with existing workflows.

8. Operational Challenges and Solutions

The key challenges encountered by the team and how they addressed them, in problem-solution pairs.

9. Key Insights for Tech Leaders

Key findings of the case. Actionable insights for tech leaders looking to integrate a scalable and reliable customer chat-support system into their businesses.

10. Conclusion

Key insights with closing remarks.

“One of the problems with introducing AI tools into a testing workflow is that you can show them to your colleagues, see them play with it, but they will never open it again. The initial novelty doesn’t transition into a sustained use. But if you implement it inside the interface, within their workflow, it will work.”

Kirill Markin, Former Head of R&D at SOAX

Meet the Experts

Kirill Markin

Former Head of R&D, SOAX
Kirill Markin brings over 12 years of experience in AI, data science, and business automation, with a proven ability to transform technical innovation into practical, impactful solutions. As a driving force behind SOAX's AI-powered customer support system, Kirill played a pivotal role in designing and implementing scalable architectures that balanced efficiency, adaptability, and cost-effectiveness. Kirill’s technical expertise includes prompt optimization, pipeline automation, and developing modular AI workflows to streamline operations. His innovative use of task-specific models and real-time feedback mechanisms ensured SOAX's AI systems remained secure, agile, and aligned with business goals. Describing himself as a “data monkey”, Kirill thrives on experimenting with emerging technologies and continually pushing the boundaries of AI-driven solutions. His hands-on leadership has solidified SOAX’s position as a leader in intelligent customer support automation.
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Reviews

Rajnish Tandon

AI-ML Consultant
"For organizations exploring AI-driven customer support, this is a model worth studying and adapting. SOAX presents a textbook example of intelligent AI adoption – balancing rapid deployment with long-term scalability. Their multi-layered architecture is particularly commendable, especially the use of RAG, sentiment-driven escalation, and modular LLM integration."

Nikhil Bhardwaj

AI Product Manager
"The SOAX team’s maturity in handling complexity, while consistently improving for scale, is a great point of view for young companies trying to build out quick solutions. The ability to turn best practices into evolving systems is something worth noticing through the report. The case covers a real-world AI transformation with precision and depth."

Rajnish Tandon

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