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Schedule Interview NowMy journey at Softaims has been defined by curiosity, growth, and collaboration. I’ve always believed that good software is not just built—it’s carefully shaped through understanding, exploration, and iteration. Every project I’ve worked on has taught me something new about how to balance simplicity with depth, and efficiency with creativity. At its core, my work revolves around helping businesses and people achieve more through thoughtful technology. I’ve learned that the most successful projects come from teams that communicate openly and stay adaptable. At Softaims, I’ve had the opportunity to work alongside professionals who challenge assumptions, share knowledge generously, and inspire continuous improvement. I take pride in focusing on the fundamentals—clarity in logic, consistency in design, and empathy in execution. Software is more than a set of features; it’s a reflection of how we think about problems and how we choose to solve them. By maintaining this perspective, I aim to build solutions that are not only effective today but also flexible enough to support the challenges of tomorrow. The culture at Softaims promotes learning as an ongoing process. Every new project feels like a step forward, both personally and professionally. I see each challenge as a chance to refine my skills and contribute to the shared vision of building technology that genuinely improves lives.
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Developed an AI-powered video semantic search solution using advanced embedding models and FastAPI. The project focused on enabling precise frame-by-frame analysis of video content to extract meaningful scenes and segments based on user-defined prompts. By implementing state-of-the-art semantic search algorithms and efficient metadata indexing, the solution delivers fast and accurate results. Designed for applications like media indexing, content analysis, and personalized recommendations, this tool streamlines video retrieval processes and maximizes the value of video datasets.
A high-quality transcription and speaker diarization service tailored to your meeting recordings. With years of experience in natural language processing and AI solutions, I specialize in delivering accurate, time-efficient transcriptions that differentiate between speakers and capture every detail. Whether for business meetings, interviews, or conference calls, my service ensures clarity, reliability, and actionable insights. My work is powered by advanced AI tools, guaranteeing precision and quality, and I strive to exceed client expectations with a client-focused approach.
In this project, we aim to develop an advanced question-answering bot that leverages the power of OpenAI GPT-models and combines it with the Langchain framework and Pinecone DB to provide accurate answers using private data not indexed by default GPT models. Our solution incorporates the Langchain framework to enrich each API call to GPT-3.5/4 with relevant context, resulting in better outcomes without the need for model fine-tuning. To make the most of private data, we will employ Pinecone DB , and its approximate nearest neighbor (ANN) search capabilities for efficient text indexing and retrieval of similar contexts. By combining these technologies, the question-answering bot will be able to provide more precise answers without fine-tuning, taking advantage of the vector similarity and vector database to enhance the context length of each query. This cutting-edge solution is perfect for businesses looking to harness the power of AI for efficient and accurate information retrieval using their unique, private data.
We can search through a database of products and find visually similar products. This feature can be used to do product deduplication on an eCommerce platform and to find the same product across multiple sites for tasks like price comparison. This can be extended to add text features and metadata features like price and type etc to make it more robust in matching the product.
Video analytics done for retail stores with face detection, face recognition, people counting, action recognition and heat map generation with activity level of the floor. Multiple video feeds are consumed and divided into image frames and send to processing modules which would process these frames and detect persons, faces and actions. This is a modular architecture as we can add more processing modules to get more insights from the video data.
in Deep Learning
in
2011-01-01-2015-01-01