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viditparashar.me

Opinions I will defend

Held loosely enough to be argued out of, but they come up often enough to be worth stating before a call.

  • An AI feature that knows when to abstain beats one with better average accuracy.
  • Aggregate metrics hide the failures that matter. Look at the rare categories first.
  • If a decision cannot be explained after the fact, it should not have been automated.
  • Latency is a correctness property in voice. An answer that arrives too late is wrong.
  • Evaluation is part of the feature, not a phase after it.
  • The interesting problem is almost never the model.
  • Show the user the irreversible thing before you do it. That is cheaper than a better classifier.

Results

  1. viditparashar.meviditparashar.me › experience › ainoviq

    Full-stack and AI Engineer at Ainoviq (Blackngreen) (Mar 2026 – present)

    Full-timeMar 2026 – present

    1 Mar 2026… human-in-the-loop LinkedIn and email platform) and LinkedIn activity — these have produced real conversions. Run internal AI enablement: teaching engineers and…

  2. viditparashar.meviditparashar.me › about › intro

    Vidit Parashar — full-stack engineer, applied LLM and voice systems

    I build AI features for domains where a confident wrong answer costs something. In practice that has meant clinical software — an ambient scribe, and agents for…

  3. viditparashar.meviditparashar.me › experience › hsi-labs

    Software Engineer at HSI Labs (Aug 2024 – Aug 2025)

    Full-timeAug 2024 – Aug 2025

    31 Aug 2025Designed and built a suite of AI healthcare agents — medical scribe, patient intake, clinical decision support and medical coding — and the NLP pipelines behind them.…

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