Pranav Pandey is a Senior Applied AI Engineer who has spent the last five years building production AI systems across deep learning, computer vision, and large language models. Most recently at SpotDraft, he owned the full stack of a multi-agent AI platform for in-house legal teams, from architectural design through evaluation infrastructure and data ingestion pipelines, serving more than 400 enterprise clients.

Pranav's career spans a range of applied AI contexts. At Wipro, he worked on computer vision research including floor-plan recognition for a government authority in Dubai and a COVID detection system from cough audio. At XTRA, he built 3D human pose estimation and activity recognition systems deployed both on cloud and in-browser. At Motive, he worked on object detection, tracking, and domain adaptation. At Apollo.io, he fine-tuned a LLaMA model for the GTM AI Assistant, building the dataset in-house and validating output quality against a golden dataset. As co-founder of Stybe, he applied AI-driven recommendations to a consumer fashion product.

At SpotDraft, Pranav led three parallel workstreams: designing the routing, planning, and agent architecture for a multi-agent legal AI system; building an evaluation harness from scratch that other product teams could use to surface and track real customer failures; and constructing a large-scale data ingestion pipeline processing millions of documents with LLM-based metadata extraction. He also built an automated prompt optimization system that iterated on failing eval cases to improve agent performance without manual intervention.

Pranav is drawn to problems that sit at the intersection of model quality and system design, where getting the architecture right determines whether the product actually works in production. He is particularly interested in voice AI and the applied research challenges that come with building reliable, high-accuracy AI products at scale.