Machine Learning Engineer

We have recently developed an AI-powered custom sales agent with a distinctive persona that defines its conversational style. This agent utilizes a comprehensive knowledge base of product information and FAQs, alongside a detailed product catalog showcasing all available offerings. Through natural dialogue, the agent comprehends consumer goals, addresses product-related inquiries, and suggests the best solutions to meet their needs. Key features include:

• A well-defined persona that shapes the agent's tone and engagement style.
• An extensive knowledge base covering product details, comparisons, and FAQs.
• A complete product catalog with ordering and configuration options.
• Advanced conversational understanding of consumer goals and questions.
• Intelligent logic to match consumer needs with optimal product recommendations.

We recently developed a system to optimize a client's unstructured data for training a large language model (LLM). The process involves:

• Converting unstructured data such as webpages, videos, documents, and databases into structured formats.
• Using automated methods to filter and organize disparate data sources.
• Training an open-source LLM fine-tuned to the client's specific business needs.
• Enabling the customized LLM to fluently interact with the client's databases and documents, providing enhanced natural language capabilities.

We have recently developed an solution seamlessly converts documents into branded PowerPoint presentations. By leveraging generative AI, it analyzes document content, auto-generates slides with relevant images and text, aligns the design with company templates. This integration of text, image ensures a polished and professional presentation every time.

• Analyzes the content.
• Auto-generates slides with images and text extracted from the document.
• Aligns the presentation design with company templates.
• Incorporates a virtual assistant avatar to narrate slides using text-to-speech

We developed a sophisticated recommendations engine tailored for an e-commerce website to enhance user experience and boost sales. This engine offers both product-to-product and user-to-product recommendations, leveraging advanced AI techniques for optimal results.

Product-to-Product Recommendations

1. Related Product Recommendation: Utilizing product descriptions and context, we identify and recommend related products. This is achieved by employing transformer models to understand and match product characteristics accurately. 2. Context-Based Product Recommendation: Using transformer models, we analyze the product descriptions and context to find related products that complement each other, ensuring relevant and useful suggestions for customers.

User-to-Product Recommendations

3. Personalized User Recommendations: By collecting and analyzing user data, we recommend products tailored to individual preferences. This approach uses deep learning techniques to understand user behavior and preferences, delivering highly personalized and relevant product suggestions.