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“How iLeaf Prevented a Client From Wasting ₹12 Lakhs on Unnecessary AI Development”
Prepared by Vivek S N
Background
A logistics company approached iLeaf wanting to build a GenAI-powered support assistant.
Client’s Request
“We want an LLM to answer all customer queries and automate support.”
Architectural Evaluation
Our AI Solution Architect analyzed:
- Call logs
- Support ticket patterns
- FAQ types
- Conversation complexity
The Challenge
The client faced the risk of spending over 12 Lakhs on a GenAI solution that wasn’t needed, as most queries were simple, rule-based FAQs. A 4-month development timeline and potential errors from LLMs added cost and complexity. With varied support data, automating without analysis could lead to inconsistent responses, and a full AI system would be harder to maintain and scale over time.
The Result
- Reduced development cost by 70%
- Faster go-live (3 weeks instead of 4 months)
- Accuracy improved from 62% to 98%
- Zero hallucinations
- Scalable, low-maintenance system
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