I’ve been working with one of my companies that’s been in the B2C business for 16 years. Despite their experience, they faced a challenge many established B2C companies encounter: maintaining fast and consistent support while operating in a market with fierce competition and tight margins. Add in the complexity of supporting customers in multiple languages (both Estonian and English), and you’ve got a genuine business problem that needed solving.
When AI capabilities started showing real practical value beyond the hype, I saw an opportunity to address this longstanding pain point.
Diagnosing the Problem
We started by analyzing three years of support ticket history. This wasn’t a casual review – we methodically categorized the types of problems clients reported and mapped out how the support team typically resolved these issues.
The findings were eye-opening. About 92% of tickets fell into predictable patterns with consistent resolution approaches. This wasn’t just interesting data – it was validation that an AI solution could potentially handle the vast majority of support inquiries if designed correctly.
Rethinking the Support Model
Rather than just adding AI to the existing workflow, we decided to reimagine the entire support structure. The AI agent would become first-line support, with the human team evolving into a senior support unit handling more complex cases.
This wasn’t about replacing people – it was about focusing human attention where it adds the most value. The existing team members could concentrate on challenging problems requiring judgment and creativity, while the routine issues could be handled consistently by the AI.
Building the Solution
The core of our solution was a framework application to support and operate the AI agent. This framework handles all the technical processes, communication between systems, and monitoring of the AI’s work. It’s what enables the agent to access necessary systems while maintaining security boundaries.
When a ticket arrives, the system:
- Identifies if we recognize the client
- Analyzes and classifies the ticket
- Determines if it needs a response
- Passes this analysis to the AI agent with tools to investigate and take actions
Quality Control by Design
Having experienced my fair share of frustrating automated support interactions, I was determined not to create another system that left customers feeling stranded or misunderstood.
We built multiple validation checkpoints into the process:
- The AI agent investigates and proposes a solution
- A validator checks if the agent actually addressed the problem with accurate information
- If needed, the solution goes back to the agent with notes
- Once validated, a responder crafts the final reply in the client’s language and the company’s tone
The system also includes clear escalation paths for issues the AI can’t handle due to insufficient information or complexity.
For transparency, each AI-generated response includes a footnote identifying it as AI-assisted, along with an easy feedback mechanism. This helps us catch and address any problems quickly before they become patterns.
The Impact
The transformation has been significant. The company now delivers consistent, rapid support at scale. And the human team focuses on complex, interesting problems instead of repetitive tasks. Customer satisfaction has improved because simple issues get resolved quickly, and complex issues receive more dedicated human attention.
What I learned through this process is that effective AI implementation isn’t about technology alone – it’s about thoughtful system design with the right checks and balances. By deeply understanding the specific challenges and building appropriate guardrails, we’ve created a solution that delivers real business value today.
The most satisfying part? Seeing a 16-year-old pain point finally resolved through a thoughtful combination of human insight and AI capabilities.
