Product Development

How Product and Engineering teams use AI to synthesize inputs, prioritize work, and accelerate innovation.

All workflows

28 workflows

Tushar Amrit

Nora Research: Customer Voice-to-PR

As part of the broader “customer voice to PR” initiative, Nora Research is designed to transform customer feedback into production-ready fixes. But early pilots revealed key friction points, from overly verbose reports to unnecessary code generation. This latest iteration focuses on making the system faster, clearer, and more actionable.

Hala Abualtayeb

Customer Pulse

Hala Abualtayeb built a unified planning portal to solve a common challenge across product and engineering: understanding what customers are actually struggling with before it turns into escalations. By combining multiple data sources and using AI to cluster themes, the system enables more proactive planning and smarter prioritization.

Ivan Penev

CLI Tools for Agents

Ivan Penev shipped CLI Tools for Agents so Abnormal’s coding agents, Claude and Nora, can reliably retrieve operational and analytics context during real investigations. The goal was simple: fewer stalled sessions, more answers engineers can act on.

Shrivu Shankar

Prompt to Demo Portal

When you’re iterating on Abnormal’s customer-facing portal UI, review friction is real. A PR might contain the “right” code, but most reviewers still can’t easily answer the simplest question: what does it look like? The Prompt to Demo Portal is Shrivu’s quick solution to collapse the process into a single Slack prompt that generates both the change and a demo video.

Andy Chen

Enterprise Context Layer

As product velocity increases, documentation quickly falls out of sync with reality. Andy Chen and Sara Faradji are building a Claude Code–powered documentation layer that automatically updates knowledge bases, reconciles conflicting sources, and ships customer-facing updates within minutes of code changes.

Shrivu Shankar

Constitutional Markdown

Shrivu Shankar built Constitutional Markdown to help Abnormal operate as an AI-initiated org, where agents do the first pass on design and implementation. The feature packages Abnormal’s architecture, security, and legal intent into a single, agent-friendly interface.

Chanel Green

Onboarding Scheduler

As Abnormal’s R&D onboarding became more hands-on and AI-first, scheduling enablement sessions by hand no longer scaled. Chanel Green built a self-serve onboarding scheduler using Claude Code and Google Apps Script to automate session scheduling directly from onboarding spreadsheets.

Ivan Penev

Nora PCCMR

Software engineer Ivan Penev built a command-line agent to reduce the toil of production change reviews. It gathers fragmented context, asks clarifying questions, generates detailed JIRA-ready change review tickets, and raises consistency so risky changes ship with safer execution, monitoring, and rollback plans.

Shahzil Sheikh

Modal Dev Boxes

Modal Dev Boxes gives Abnormal engineers instant, secure agent workspaces in the browser. Shahzil Sheikh shows how AI agents can use real tools, access internal resources securely, and reduce setup time from minutes or hours to seconds, enabling faster shipping today.

Shrivu Shankar

Sigma CLI

Sigma is widely used across Abnormal, but its UI-heavy workflows made even small analytics changes slow and painful. Shrivu Shankar built Sigma CLI to bypass the interface entirely, connecting Claude Code directly to Sigma’s underlying data and unlocking fast, AI-driven analytics.

Brandon Qin

RFE Prioritizer

This feature, by Brandon Qin (AI Native Product Manager), helps product managers triage thousands of Jira enhancement requests by enriching tickets with Salesforce ARR and Gong context, then emailing ranked recommendations with rationale. The V1 reduces manual backlog grooming and surfaces renewal risk before quarterly escalations.

Shrivu Shankar

Nora PR Analyzer

As AI writes more of Abnormal’s code, context quality becomes the limiting factor. Shrivu Shankar built NoraPR Analyzer to close that gap by analyzing how AI-written code differs from human-written code and automatically generating the documentation needed to make AI better next time.