“How iLeaf Prevented a Client From Wasting ?12 Lakhs on Unnecessary AI Development”

AI
- Prepared by

casestudy

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.

iLeaf's Process

1

Communication

We discuss to ensure that we have the exact idea of what is required

2

Collaboration

There's regular interaction with the client to ensure things are on track

3

Development

Begins according to the needs of our client

4

Result

The final output will be a perfect match to our clients requirement

Findings

More than 82% of customer queries were:

Order status
Delivery updates
Payment confirmations

Basic FAQs

All deterministic and rule-based - not requiring GenAI.

Solution Provided

Instead of building a costly AI chatbot, we implemented:

A rule-based workflow
API integrations to their OMS
Templated responses
Voice + chat automation
n8n pipelines for follow-ups

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

Contact Us

Let's create something outstanding

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