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October 17, 2025
Workshop recap: Onboarding is just the start: Turning feedback into competitive advantage
On October 16, we hosted a live workshop with Nimesh Chakravarthi, co-founder of Edgedive, YC F24, to explore how teams can move beyond onboarding and turn customer feedback into a true competitive advantage. Drawing from his journey from LinkedIn to Y Combinator to leading Edgedive, Nimesh shared how fragmented feedback channels, reactive processes, and organizational silos can slow down product development, and what it takes to fix them.
The cost of ignored feedback
Nimesh noted that most companies collect plenty of feedback but fail to act on it. Even teams that analyze data often stop short of implementing fixes or following up with customers. The real challenge, Nimesh noted, isn’t gathering feedback but turning it into meaningful change.
Edgedive’s evolution has centered on closing that loop by helping companies automatically detect issues, route them to engineering, and track when they are resolved.
When feedback volume becomes a scaling problem
Early-stage startups can often manage feedback manually. But once a company surpasses roughly 50 to 100 customers, communication channels multiply and information begins to fragment.
Edgedive addresses this by creating a single source of truth that every team, from support to engineering, can access and use in their own workflows. This centralization allows organizations to move faster without losing customer context.
What high-performing teams do differently
The best teams, Nimesh said, share an obsessive focus on customers. They not only track issues but also celebrate fixes, closing the loop with users who reported them.
That follow-up process builds trust and motivation. Engineers gain energy from seeing their work directly improve customer experience, while customers feel heard and valued. The emotional impact, Nimesh emphasized, is just as important as the operational one.
Empowering engineers with ownership
A powerful idea from the discussion was end-to-end ownership, having one person manage a feedback item from identification through resolution.
With automation, an engineer can investigate a bug, generate a fix, deploy it, and notify the customer, all within one continuous workflow. This reduces information loss and accelerates product quality improvements.
Balancing vision with feedback
When asked how to balance customer requests with a long-term product vision, Nimesh emphasized the importance of focus. Teams should prioritize feedback from their ideal customer profile rather than trying to please everyone. Staying close to those core users, he said, is the best way to keep product direction grounded in real needs.
Quantifying qualitative feedback
A major shift in the LLM era is the ability to quantify qualitative data. Nimesh described how Edgedive uses AI to assign urgency and impact scores to feedback by analyzing who raised it, contract value, and related product usage data. This allows teams to prioritize the highest-value fixes while maintaining a nuanced understanding of user sentiment.
Managing bias and privacy in data pipelines
To avoid over-representing vocal users, Nimesh recommended pulling insights from multiple channels such as support tickets, sales calls, and customer meetings.
On the privacy front, he stressed the importance of obfuscating sensitive data before processing it with language models, ensuring that personally identifiable information never enters AI pipelines.
The future: your AI support engineer
Looking ahead, Nimesh painted a bold vision: every company having its own AI support engineer. This virtual teammate would continuously parse feedback, identify bugs, propose fixes, and even write code changes for review. By automating the repetitive parts of maintenance and bug resolution, teams can focus more on strategic initiatives and product innovation.
Education and the path to self-serve systems
A recurring theme was education. Many companies, Nimesh noted, underestimate how far AI systems have come. Edgedive’s white-glove onboarding helps teams understand available tools like Claude Code and how to integrate them into their existing workflows. While full self-serve AI platforms remain difficult to achieve, especially the last 10 to 15 percent of edge cases, both Nimesh and the 14.ai team see a near future where systems will “self-heal” with minimal human intervention.
A huge thank you to Nimesh for sharing his insights, and to everyone who joined us live.
