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.
Related results
Results
- 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…
- 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…
- viditparashar.meviditparashar.me › experience › hsi-labs
Software Engineer at HSI Labs (Aug 2024 – Aug 2025)
Full-timeAug 2024 – Aug 2025
31 Aug 2025 — Designed and built a suite of AI healthcare agents — medical scribe, patient intake, clinical decision support and medical coding — and the NLP pipelines behind them.…