EcoAxis: AI-Powered User Manual Navigation

Ignite Solutions with UX design expertise, met a tight deadline for an impactful design to launch the product at a prestigious international exhibition

Industrial IoT solutions

INDUSTRY

India

COUNTRY

OVERVIEW

EcoAxis recognized the potential of AI to improve efficiency, productivity, and enhance capabilities in all aspects of their business

The company partnered with Ignite Solutions to develop a prototype of an AI-driven intelligent user guidance and troubleshooting assistant aimed at improving end-user self-service, reducing support costs, and increasing customer satisfaction. This initiative was one of many AI solutions as part of their broader strategy to explore AI applications in industrial settings

Challenges

Time-consuming search process

Manual search through manuals was tedious and time-consuming

Lack of context

Users had specific situations, but the manuals weren’t designed to assist with specific answers

Lack of AI-powered assistance

Users required a natural language query system to improve search accuracy and speed

No multilingual capabilities

The existing documentation lacked language flexibility, making accessibility a challenge for non-English speakers

Our Approach

Ignite Solutions leveraged AI-powered retrieval-augmented generation (RAG) and image-to-text conversion to create an intelligent search solution. Our goal was to provide users with a natural language interface to process complex queries and instantly retrieve relevant images and text

Strategy, Solutions, and Execution

RAG (Retrieval-Augmented Generation)

Implemented RAG (Retrieval-Augmented Generation) for intelligent document searching

Image-to-text conversion

Used image-to-text conversion to generate accurate text descriptions for manual images

Sequential description structure

Maintained a sequential description structure to preserve step-by-step guidance

Natural language interface

Created a natural language interface, allowing users to ask technical questions and receive contextually relevant responses with images

Multilingual support

Enabled multilingual support, expanding usability for diverse workforces

Contextual follow-up questions

Allowed a natural language conversation with contextual follow-up questions

Tech Stack

Impact and Results