Thank you for reading IQE ebook – I want to know more about Infosys Quality Engineering capabilities

To make the chatbot
more helpful,

we hit TAB

The platform was built to manage budgets and resources. But the chatbot meant to support it couldn’t keep up.

With thousands of FAQs and documents, it often gave answers that weren’t accurate or consistent. Storing all that data in a vector database and running it through an LLM only made things slower. Teams still had to track budgets and resources manually.

Infosys Quality Engineering stepped in with a new approach: testing the chatbot for impact, not just answers. Using open-source tools, we checked if replies were relevant, consistent, and reliable. And results started to improve. The chatbot got better. Users felt the change.

But the real breakthrough was yet to come.

Switch TAB, Switch Outcome

Support became seamless. Work got lighter.

What got better

Built with open-source DeepEval framework + Python, trained on embedded FAQs from thousands of documents in a vector DB
Trained on platform-specific FAQs and use cases
Tested for real-world relevance and consistency
Automated tasks like budget tracking and resource allocation

What it led to

Accelerated decision-making with 50% faster chatbot responses
Increased team productivity by cutting 70% of manual effort

Faster project delivery through 80% quicker resource allocation
Lower operational costs with 50% fewer ServiceNOW tickets
Improved customer experience thanks to 30–40% fewer support calls

Why it made
a difference

With Gen AI embedded in testing and an open-source validation stack, the chatbot stopped being reactive and started being reliable.