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Better Products Faster: How Propel's AI Delivers Your AI

6-8 months

of AI coding agent maturity has reshaped how software gets built industry-wide

Shift left

moves engineering focus from writing code to defining what to build, as AI handles the how

Orchestrator

best describes engineers who direct AI tools while staying fully in control of the code

Quick Overview

Watch Propel's CTO explain how AI reshapes software development inside Propel in this webinar. The session distinguishes AI-assisted coding, where engineers direct AI tools like an orchestra conductor, from vibe coding, where users accept AI output unchecked. It details Propel Sage, an internal AI agent answering product questions for support, QA, and implementation teams, cutting questions escalated to engineering. The session also shows how AI code review on GitHub catches bugs before customers do, a model Propel plans to apply to ECO reviews in PLM and QMS to flag downstream risk early, shaping Propel One's ongoing development.

Key Takeaways

AI-assisted coding keeps engineers in control, unlike casual vibe coding.
Vibe coding lets anyone generate code quickly from prompts, but production-grade software demands AI-assisted coding, where engineers act as orchestrators who review, guide, and validate every output before it ships.
Propel applies AI across the entire software development lifecycle, not just coding.
Propel uses AI for requirements and user story generation, test coverage, QA test case creation, release notes, risk assessment, and customer support, extending automation far beyond simply writing code into every phase of development.
How an internal AI agent reduces and streamlines product questions from engineering.
Propel built an internal AI product expert called Propel Sage, trained on internal product knowledge and rolled out as a Slack bot. It now answers how-to questions for customer support, QA, and implementation teams, reducing escalations to engineering.
AI code review on GitHub catches bugs before they reach customers.
Propel's developers use AI code review agents on GitHub to flag issues during the review cycle, before code ships. Catching bugs at this stage avoids the far higher cost of fixing defects after a customer discovers them in production.
Lessons from AI code review are shaping ECO change review in PLM and QMS.
Propel sees change order review as the PLM equivalent of code review, and plans to apply AI to flag downstream risks, such as supply chain, production, or compliance impact, early in the ECO review cycle before changes are approved.
Propel is moving toward more autonomous AI agents, with humans still in the loop.
Propel's roadmap points toward greater automation and autonomous AI agents that can detect anomalies and take proactive action. Humans remain in the loop, but manual, tedious tasks are increasingly handled by agents across the product.
Learn More
See how Propel One already pre-populates ECO reviews with impact summaries, affected owners, and risk data, turning days of manual review into a focused 15-minute decision for change teams.
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