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Our expert team can quickly identify opportunities to integrate GenAI into your solutions

Our GenAI work

KILI

Knowledgeable Interactive Language Improver

A tool that helps to learn a new language through podcasts. This tool provides textual translation of podcast text in both original language and the native language of the user in a side by side fashion for the listener to easily understand the meaning of the statements heard. This product uses OpenAI Whisper (audio to text translation) and flan-t5-xl (language translation)

Demo

VIDURA

Versatile Intelligent Document Understanding and Retrival Agent

A chatbot that can understand large amounts of documents and answer based on the context and the conversation history to the users. This tool is powered by RAG technique.

Demo

KRSNA

Knowledgeable Retail Shopping Navigation Assistant

A chatbot that can help your customers to pick the right product to buy based on their preferences. This tool uses LLM to find the customer preferences and searching for a match in its vector database of items

Demo

SITA

Smart Information and Task Assistant

A chatbot that can answer questions very specific to user data by integrating with internal API endpoints of an organization. A good use case is for the customer support chatbot which can give accurate details about their account, transactions, history, etc. This tool is powered by LangChain and API adapters to easily integrate hundreds of APIs

Demo

SEA

Sales Email Agent

A tool for software service providers to generate lead gen emails based on the needs of the target organisation and the capabilities of their organisation. This tool is powered by Vector database and sales domain knowledge

Demo

Optimal Match

OptiMatch

An innovative solution for determining the similitude of two products based on their title. This can be used to identify duplicate products in a database, or to match products from different e-commerce platforms.

OptiMatch is powered by two LLM models. The first is a proprietary, fine-tuned Bi-Encoder and the second is a Cross-Encoder. Both base models were chosen for their accuracy, efficiency, and performance after extensive testing.

Demo

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