Meet Our Founder: Srinithin Somasundaram's Journey to Building Feedlooply

From YESP Tech to Feedlooply—discover the story behind our founder's mission to make feedback loops accessible to every startup.

Hi, I'm Srinithin Somasundaram, the founder of Feedlooply. Today, I want to share the story behind why I built this platform and how my journey led me to focus on solving one of the most overlooked problems in product development: turning scattered feedback into actionable insights.

The Problem That Started It All

Like many founders, my path to building Feedlooply wasn't linear. It started with a frustration I experienced repeatedly across different projects and teams: we were drowning in feedback but starving for clarity.

Whether it was user comments scattered across Slack threads, support tickets buried in different tools, or insights from customer calls that lived only in someone's notes, the pattern was always the same. We had the signals—we just couldn't see the forest for the trees.

My Background: From YESP Tech to Product Obsession

Before Feedlooply, I founded YESP Tech, where I learned firsthand how critical it is to listen to users—and how hard it can be to do it systematically. At YESP Tech, we built solutions for businesses, but I kept running into the same challenge: teams knew they needed to collect feedback, but they struggled to turn that feedback into confident product decisions.

I watched talented teams make reactive choices based on the loudest voice in the room, rather than the strongest signal in the data. I saw promising products stagnate because feedback was treated as noise rather than navigation. That's when I realized the problem wasn't just about collecting feedback—it was about creating systems that turn feedback into compounding product value.

The 'Aha' Moment

The breakthrough came during a particularly chaotic product review at YESP Tech. We had feedback from five different channels, three competing priorities from different stakeholders, and a team that was genuinely trying to do the right thing—but we had no systematic way to weigh the evidence.

That's when I realized: most teams don't fail because they ignore users. They fail because they can't efficiently process user signals into clear, confident decisions. The tools existed to collect feedback, but the tools to analyze, prioritize, and act on feedback were either too expensive, too complex, or simply didn't exist.

Why Feedlooply?

I started building Feedlooply because I believe every startup deserves access to the same feedback intelligence that larger companies take for granted. Too many great products die not because they lack potential, but because their teams can't see the patterns in their user feedback clearly enough to make confident bets.

Feedlooply is designed to solve three core problems:

• Scattered signals: Feedback lives everywhere—Slack, support tickets, calls, emails. We centralize it.
• Analysis paralysis: Raw feedback is overwhelming. We use AI to find patterns and themes.
• Broken loops: Users share feedback but rarely see outcomes. We help you close the loop visibly.

The Vision: Feedback Loops for Everyone

My vision for Feedlooply is simple: make systematic feedback analysis accessible to every team, regardless of size or budget. I want the solo founder building their first product to have the same clarity about user needs as a Series B startup with dedicated user research teams.

This isn't just about building another feedback tool. It's about democratizing the ability to listen systematically, analyze intelligently, and act confidently on user signals.

What's Next

We're just getting started. Feedlooply is launching with Early Access pricing that makes it accessible to indie hackers and early-stage teams—the people who need it most but often can't afford enterprise solutions.

I'm building this for founders like me who believe that listening to users shouldn't be a luxury. It should be a superpower that every team can afford and every product can benefit from.

If you're tired of scattered feedback and reactive product decisions, I'd love to have you join us. Check out our [Early Access pricing](/#pricing) and see how Feedlooply can help you turn user signals into product clarity.

Extended Insights

In practice, making feedback actionable requires a consistent operating rhythm. Most teams collect fragments in different places and never consolidate them into decisions. A weekly loop—collect, triage, aggregate, decide, and close the loop—turns raw input into compounding product value. If you’re new to this cadence, start with a single 30–45 minute session and refine from there. We expand this approach in [Why Startups Fail at Feedback](/blog/why-startups-fail-at-feedback) and demonstrate how 'evidence buckets' replace ad‑hoc opinions. The goal isn’t a perfect taxonomy—it’s repeatable choices made visible to the team and, when appropriate, your users.

Treat support as a continuous PMF survey rather than a cost center. When you tag by intent (bug, friction, capability, trust), segment, and lifecycle, patterns emerge quickly. Within 6–8 weeks you can plot severity by frequency and spot jobs‑to‑be‑done hidden in plain sight. That’s why we call support a near real‑time PMF barometer in [Finding PMF Signals from Support](/blog/pmf-signals-from-support). Leaders often overweight a single loud anecdote; proper tagging counters that bias with structured evidence.

Closing the loop is where trust compounds. Even a 'not now' communicates respect when it’s backed by a short rationale and a path to re‑evaluation. When you do ship, connect the change to the user’s workflow, show before/after, and invite a tiny next step—reply with edge cases, try the new flow, or share a screenshot. We maintain a template library in [Close‑the‑Loop Messages That Convert](/blog/closing-the-loop-that-converts) to make this easy for PMs, engineers, and support alike.

NPS should be a weekly diagnostic, not a quarterly scoreboard. Ask “What’s the main reason for your score?” and link the commentary to behavior. A detractor with high ARR exposure can outweigh several passives; the point is to anchor anecdotes in facts. We detail the mechanics in [Treat NPS as a Diagnostic, Not a Scoreboard](/blog/nps-is-a-diagnostic). Use this signal to prioritize the smallest change that removes the sharpest recurring pain.

Beta programs are not early access—they’re structured risk removal. Define entry criteria, make limitations explicit, and schedule brief weekly office hours. Participants should feel like partners, not unpaid QA. Our 4‑week template in [Design a Beta Program That De‑Risks GA](/blog/beta-program-that-de-risks) shows how to balance speed with reliability while keeping the surface area small enough to learn quickly.