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Ε˅ÂĿNLP

Ε˅ÂĿNLP. an Evaluation Platform for Natural Language Processing. Founder Profiles. Sandya Mannarswamy (Sandy) Ph.D. from IISc (CS) Strong Patent & Publication profile 20 years of Enterprise software R&D experience with Hewlett Packard, Microsoft and Xerox Research.

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Ε˅ÂĿNLP

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  1. Ε˅ÂĿNLP an Evaluation Platform for Natural Language Processing

  2. Founder Profiles • SandyaMannarswamy (Sandy) • Ph.D. from IISc (CS) • Strong Patent & Publication profile • 20 years of Enterprise software R&D experience with Hewlett Packard, Microsoft and Xerox Research • Saravanan Chidambaram (Saro) • M.Tech., IIT Kharagpur (CE) • Head of Advanced Development (HPE) • 20 years of R&D and Technology Management with Hewlett Packard Enterprise, Microsoft and Oracle sandyasm@gmail.com sarochida@gmail.com A balanced mix of research and product development experience

  3. Pain Point Model selection for deployment is subjective, leading to costly errors, failures and project delays Subjective Evaluation Lack of streamlined mechanism for customers to evaluate different offering against effective criteria Inability to Compare and Contrast Models Models often perform poorly in real world, tend to be fragile and not being very robust Lack of Quantitative Operational Boundaries NLP/ML Models are crux of the AI driven solutions’ success like Chatbots, Sentiment analysis, Question-Answering. They are being deployed in Customer support, HR, Health-care, Social sciences and E-Commerce.

  4. Our Value Proposition What “Master Health Check-up” to “Human” is similar to what “Ε˅ÂĿNLP” is to “NLP Models” WE assess AND assist YOU for answers to these questions on your Models Is it Robust? Is it Unbiased? Is it Interpretable?

  5. Our solution Data Augmentation On-Premise & On Cloud Actionable Insights • A 360 degree objective assessment of the model with just a few clicks in terms of Robustness, Bias, and Interpretability Objective Evaluation over Diverse set of Criteria Compare offerings for Price to Effectiveness • Provision to compare models, apply criteria you care about, and select the best performing model for your operating scenario Quantify and Establish Operational Boundaries • Quantify operational performance characteristics on a wide range of domain, task and scenario specific data generated by us

  6. Our ability to build this venture • We have built mission critical, complex enterprise software for 2 decades (production compilers, platforms & operating system) • We understand how important software quality is and how difficult it is to quantify it • We have a balanced mix of NLP research and production software development experience • We have built enterprise software quality evaluation and performance analysis tools

  7. Help we are seeking from NSRCEL • We would like to validate our product-market fit • Pivot and refine our value proposition • Identify potential customers through networks in NSRCEL • Need NRSCEL help in getting us to connect with corporate/start-ups who are non-NLP/non-AI folks, who are building/buying NLP models without any objective evaluation • Help us to connect to VCs who can fund us

  8. Our Progress so far! • We are currently building a prototype expected to complete the first version by 25th Sep • We have had informal discussions with NLP/data scientists in our network about the utility of our product

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