---
title: "AI Evals Platform for Cross-Functional Teams at Scale"
category: "talks"
date: "2026-06-29"
time: "1:55pm-2:15pm"
track: "AI-Native Enterprises"
room: "Leadership 1"
speakers: ["Nachiket Paranjape", "Swaroop Chitlur Haridas"]
sourceLabels: ["Official conference schedule", "Public YouTube metadata"]
scheduleTrack: "AI-Native Enterprises"
scheduleRoom: "Leadership 1"
scheduleLabels: ["AI-Native Enterprises", "Leadership 1", "session", "confirmed"]
---
# AI Evals Platform for Cross-Functional Teams at Scale

## Conference Context
- Date/time: 2026-06-29 · 1:55pm-2:15pm
- Track/room: AI-Native Enterprises · Leadership 1
- Speaker(s): Nachiket Paranjape, Swaroop Chitlur Haridas
- Session type/status: session · confirmed

- Track: AI-Native Enterprises
- Room: Leadership 1
- Session type: session
- Status: confirmed

## Session Description
DoorDash's Evals Platform is designed for more than just engineers. It brings human review, automated judges, and online experimentation into a single calibration loop so engineering, product managers, and strategy and operations teams can all contribute to improving AI quality. Engineers can instrument, trace, and evaluate agent behavior, while cross-functional teams can review outputs, curate trusted examples, and provide structured feedback that improves how automated judges behave over time. By combining experimentation, fully customized annotation workflows, calibration, and analytics in one system, the platform turns AI quality from a fragmented technical exercise into a shared operating model for continuously improving agent performance and making rollout decisions with confidence. While vendor platforms offer pieces of this workflow, we needed something broader: a unified system that lets engineers, product managers, and Strategy & Ops all participate directly in improving AI quality. Our goal is not just to run evals, but to enable cross-functional teams to review outputs, calibrate judges, run experiments, and make rollout decisions without being blocked on engineering. That requirement, along with tighter integration into our internal workflows and operating model, is why we are building this platform in-house.

## Media Evidence
No related AI Engineer channel video found yet.

## Evidence Graph
This evidence graph is generated from currently linked source material: official schedule text, related video pages, cached transcripts, visible slide text, dense/reconstructed slide pages, and AI slide-classification audits.

### Media Signals
No linked video, transcript, or slide source has been attached yet.

### Agent Reading Notes
Use these signals to refine the synopsis, topic links, people/company context, and method notes. If a source is a related external video rather than an exact official recording, keep it framed as supporting evidence.

## Transcript Status
No official session recording transcript was found by exact title match on the AI Engineer YouTube channel during this run.

## People
- [[nachiket-paranjape]]
- [[swaroop-chitlur-haridas]]

## Notes
- Pending transcript synthesis when an official recording or confirmed matching video is available.

## Synthesis
### Synthesized Breakdown
# AI Evals Platform for Cross-Functional Teams at Scale ## Conference Context - Date/time: 2026-06-29 · 1:55pm-2:15pm - Track/room: AI-Native Enterprises · Leadership 1 - Speaker(s): Nachiket Paranjape, Swaroop Chitlur Haridas - Session type/status: session · confirmed - Track: AI-Native Enterprises - Room: Leadership 1 - Session type: session - Status: confirmed ## Session Description DoorDash's Evals Platform is designed for more than just engineers. It brings human review, automated judges, and online experimentation into a single calibration loop so engineering, product managers, and strategy and operations teams can all contribute to improving AI quality. Engineers can instrument, trace, and evaluate agent behavior, while cross-functional teams can review outputs, curate trusted examples, and provide structured feedback that improves how automated judges behave over time. By combining experimentation, fully customized annotation workflows, calibration, and analytics in one system, the platform turns AI quality from a fragmented technical exercise into a shared operating model for continuously improving agent performance and making rollout decisions with confidence.

### Speaker And Company Context
- [[nachiket-paranjape|Nachiket Paranjape]] — Software Engineer at [[doordash|DoorDash]].
- [[swaroop-chitlur-haridas|Swaroop Chitlur Haridas]] — role not listed at [[doordash|DoorDash]].

### Topics Covered
- [[agent-security]]

### Derived Links And Source Material

### Novel Concepts / Clever Methods
- No highlighted novel concept has been detected yet.

### Evidence Boundary
This synthesis is based on the official schedule and linked source pages. It should be revisited when exact session recordings or transcript-backed secondary sources are available.