# Raja Mohan: full site text Plain-text version of https://rajamohan.me for AI assistants and crawlers. Short summary: https://rajamohan.me/llms.txt --- Source: https://rajamohan.me/ # Raja Mohan Senior product leader, engineer turned PM. ProdLoop.ai (https://prodloop.ai) LinkedIn (https://www.linkedin.com/in/mohanraja/) Substack (https://rajamohan.substack.com/) GitHub (https://github.com/mraja775) Email Eight years at Microsoft across Bing Ads ML and Microsoft Teams, where as Principal PM Lead I led 12 PMs and partnered with 60+ engineers and designers while Teams grew from 500K to 350M users. Now founder and CEO of ProdLoop.ai (https://prodloop.ai), where I’m building self-improving AI agents for enterprise voice operations: about $24K MRR and 15 enterprise customers so far, and I own product, sales, pricing and hiring. - 500K → 350M Microsoft Teams users while I led search and mobile product: 12 PMs, 60+ engineers and designers - +17% YoY RPM Revenue per mille growth on the Bing Ads international marketplaces I owned (~$400M a year) - −62% fraud loss About $20M saved by moving Bing Ads risk from rules to ML; detection from 4 hours to 5 minutes - ~$24K MRR 15 enterprise customers at ProdLoop.ai, building from zero as founder and CEO ## Selected work - Microsoft Teams · 2018–2023 Search, an ML platform, and a mobile product team through hypergrowth Set the charter for Teams search as the front door to M365 knowledge, shipped relevance ranking (+1.7% session success), bootstrapped the Teams ML platform, and led 12 PMs across Search and Mobile. - ProdLoop.ai · Founder & CEO · 2024–present Zero to $24K MRR: building a voice-AI company 15 enterprise customers including Porter, Pepperfry and Vedantu; 10M+ conversations processed; results like 12% incremental revenue at Vedantu and 100% QA coverage at Pepperfry. - Bing Ads · 2017–2018 +17% RPM: growing Bing Ads’ international marketplaces Owned revenue and relevance for international marketplaces; +17% YoY RPM while balancing advertiser, user and Microsoft value. - Bing Ads · 2015–2017 From rules to ML: cutting ad fraud loss by 62% Owned the risk portfolio (payment fraud, phishing, malware, counterfeit, editorial abuse) and moved detection from hand-written rules to ML. ## My story I started out as a software engineer, and as a founding engineer at IdealInvent I spent about three years building a BPM product from scratch and travelled to Stockholm, Denmark and across the Nordics to implement it with customers. Somewhere along the way the question that pulled at me stopped being “how do we build this?” and became “should we build this, and for whom?” So I went to IIM Bangalore for an MBA and crossed over into product management. Microsoft took me to the US, Singapore and India. On Bing Ads I worked on a mature machine at massive scale, where every decision is an experiment. On Microsoft Teams I joined a young product still finding its shape and stayed through its growth into one of the most used apps in the world. One taught me how to run a machine; the other taught me how to build one. Both taught me that the customer’s truth matters more than any opinion in the room, including mine. In late 2023 I left Microsoft to build ProdLoop.ai (https://prodloop.ai), and I’m still building it. Enterprises run millions of customer conversations and listen to almost none of them. We build voice agents, test them before they go live, and analyze every conversation after, for teams such as Porter, Pepperfry, Vedantu and Namma Yatri. Running a company end to end, from raising money and setting prices to selling, hiring and delivery, has changed how I think about product leadership more than anything before it. Outside work: running, mountains, books and writing, which live on Sidequests. ## Writing I write about product, AI and leadership at Raja’s Newsletter (https://rajamohan.substack.com/). - From Care to Candor: Transforming Teams with Genuine Feedback (https://rajamohan.substack.com/p/from-care-to-candor-transforming) Aug 2023 Why great teams need both care and candor, not one or the other. - Care: The Currency for Managers to Build Thriving Teams (https://rajamohan.substack.com/p/care-currency-for-managers) Oct 2022 Care as the real currency of leading a hybrid team. - Focus & Deep Work: Superpowers for the 21st Century (https://rajamohan.substack.com/p/focus-and-ability-to-do-deep-work) Sep 2022 Doing work that matters in a world built to distract you. --- Source: https://rajamohan.me/prodloop ProdLoop.ai · Founder & CEO · Jan 2024 – present · prodloop.ai (https://prodloop.ai) # Zero to $24K MRR: building a voice-AI company I’m building ProdLoop.ai, self-improving AI agents for enterprise voice operations: we build voice agents, test them before launch, and analyze and improve every conversation in production. We’re at about $24K MRR with 15 enterprise customers in Indian logistics, marketplaces and consumer services. Revenue: ~$24K MRR Customers: 15 enterprise Conversations: 10M+ processed ## The problem Enterprises run millions of sales and support calls and review 3–5% of them by hand. As companies replace human agents with voice AI, the gap gets worse: nobody can say with confidence whether a bot is correct, compliant or losing conversions, or why a funnel is leaking. ## What we’re building - Voice agents for appointment booking, partner onboarding, assisted sales and driver support, with every conversation automatically checked. - Pre-production testing: voice-to-voice and LLM-to-LLM simulation, so agents are evaluated before customers ever hear them. - Post-production intelligence: QA on 100% of calls, funnel intelligence, A/B testing, and real-time monitoring with replay and diagnostics. - Built for India: 13+ Indian languages, AI models hosted in India, and LLM graders that agree with human reviewers about 95% of the time. ## Customer outcomes Results our customers have reported, written up in full on prodloop.ai/customer-stories (https://prodloop.ai/customer-stories): - Pepperfry · furniture marketplace From 3–5% of calls reviewed to 100% AI QA on every support call: manual QA time down 85%, CSAT up from 93% to 96%, first-call resolution up 10% and assisted-buying conversion up 20%. (https://prodloop.ai/pepper-fry) - Vedantu · ed-tech 12% incremental revenue from smarter lead prioritization Scored every sales conversation for intent, role and blockers so agents call the right lead next: call-to-demo rate doubled and demo-to-sale rose 25%. (https://prodloop.ai/vedantu) - Porter · logistics 20% fewer “customer not reachable” calls Analyzed 100% of calls in Hindi, Tamil and Kannada at 95%+ accuracy; root causes turned into product fixes such as platform-initiated calls and an automated waiting-time check. (https://prodloop.ai/porter) - Namma Yatri · mobility Catching driver overcharging at scale Fine-tuned voice models flag drivers asking for more than the app fare across local languages; 250K+ minutes analyzed, with results feeding driver scoring and allocation. (https://prodloop.ai/namma-yatri) ## Three pivots, each from customer evidence We started in voice analytics, moved to evals and observability as customers began deploying voice bots, and then to agentic AI for voice operations, serving heads of sales and support directly. Each move followed where customers actually spend money and attention, not where our roadmap said they should. ## Running the business As founder and CEO I own the whole loop: product, pricing, the sales cycle, delivery and renewals. Our customers include Porter, Pepperfry, Vedantu, Snabbit, Namma Yatri and Jar. I’ve raised about $450K from Google for Startups, Microsoft for Startups, AWS Activate and an NSRCEL–Kotak grant, and lead a team of forward-deployed generalists who sit close to customers. We’re part of the Velocity–Kotak BizLabs accelerator at NSRCEL, IIM Bangalore. ## What it’s teaching me about product leadership Being accountable for revenue, not just roadmap, keeps sharpening my judgment on which problems are worth solving, and gives me first-hand depth in what makes LLM systems work in production: evals, grader calibration, multilingual quality and the cost of being wrong. ← All work Next: Bing Ads marketplace → --- Source: https://rajamohan.me/teams-search Microsoft Teams · Principal PM Lead, Search & Mobile · Mar 2018 – Sep 2023 # Search, an ML platform, and a mobile product team through hypergrowth I joined Teams when it had about 500K users and left when it had about 350M. I set the charter for Teams search, built the shared signals that personalized the product, and led a team of 12 PMs across Search and Mobile, partnering with 60+ engineers and designers. Scale: ~500K → ~350M users Team: 12 PMs; 60+ eng & design Search result: +1.7% session success ## The situation Teams was growing faster than almost any product Microsoft had shipped. Search was still built like a keyword lookup over messages and files. As organizations moved their whole working day into Teams, “find that thing someone sent me” became one of the most frequent, and most frustrating, things people did. ## What I owned - Product for Teams search: the results experience, relevance, autosuggest and personalization. - Bootstrapping a Teams ML platform so ranking and personalization weren’t rebuilt feature by feature. - Leading a team of 12 PMs across Search and Mobile, partnering with 60+ engineers and designers. ## Key decisions Set the charter: Teams search as the front door to M365 knowledge. Rather than build a Teams-only index, I re-platformed Teams search onto the M365 index and substrate and aligned the Teams and M365 Search organizations behind it. That made Teams the place people searched across all their work, not just their chats. Relevance over recency. We moved search from chronological, keyword-matched results to a modern results page ranked by relevance. This was a bet that users would trust a ranked list more than a strict timeline, and we measured it on session success rather than clicks. It raised search session success by 1.7%, which at Teams’ scale is a very large number of people finding what they needed. Build signals once, use them everywhere. Rather than each feature inventing its own notion of “who matters to this user,” I bootstrapped a shared ML platform with affinity scores: user-to-user and user-to-channel. Those signals fixed autosuggest, improved personalization in search, and became a foundation other teams could build on. ## Leading mobile For my last two years I also led the PMs for Teams mobile, making it a first-class productivity app across work, school and home, including experiences such as calendar and feeds. The job shifted from making the best call myself to making sure a team made good calls without me: setting the bar, coaching on trade-offs, and keeping strategy aligned across engineering, design and the wider Teams org. ## What I took from it Early-stage products run on customer observation, not dashboards. And at hypergrowth scale the highest-leverage product work is often infrastructure for other teams: a shared signal or platform beats ten clever features. ← All work Next: ProdLoop.ai → --- Source: https://rajamohan.me/bing-ads-marketplace Bing Ads · Senior PM, Bing Ads Marketplace & Serving · Feb 2017 – Feb 2018 # +17% RPM: growing Bing Ads’ international marketplaces I grew revenue per mille 17% year over year across Bing Ads’ international marketplaces, where I owned revenue and relevance for about $400M a year. Result: +17% YoY RPM Shipped: 10+ packages in 6 months Levers: Click prediction, ranking, pricing ## The situation An ads marketplace is a three-sided balance: users want relevant results, advertisers want returns, and the platform wants revenue. In international markets the data is thinner and behaviour varies by country, so models and policies tuned for the US don’t simply transfer. ## What I owned Revenue and relevance for the international marketplaces, across the core levers that decide which ad shows and what it costs: click prediction, ranking, allocation, pricing and ads understanding. ## How we worked Bing Ads is a mature product at massive scale, where every change is an experiment. Improvements shipped through careful online tests with guardrails on user experience and advertiser outcomes, so revenue gains didn’t come at the expense of relevance. ## Outcome We shipped more than 10 product packages in six months, which together grew revenue per mille 17% year over year across the international marketplaces, while holding user quality (defect and quick-back rates) and advertiser ROI (conversion rate, cost per acquisition). ## What I took from it At scale, small model gains compound into large business outcomes, but only with the discipline to measure every side of the marketplace, not just the one that pays. ← All work Next: Bing Ads ad quality → --- Source: https://rajamohan.me/bing-ads-quality Bing Ads · Senior PM, Bing Ads Ad Quality · Apr 2015 – Jan 2017 # From rules to ML: cutting ad fraud loss by 62% I owned the Bing Ads risk portfolio and moved detection from hand-written rules to machine learning, cutting fraud loss 62% year over year (about $20M) and detection time from 4 hours to 5 minutes. Fraud loss: −62% YoY (~$20M) Time to detect: 4 hours → 5 minutes Phishing & malware escalations: −95% in 6 months ## The situation Bad actors on an ads platform are adversarial and fast. Bing Ads relied heavily on hand-written rules that took time to write, were easy to route around and caught abuse hours after it started. Every hour of delay meant losses and a worse experience for users and honest advertisers. ## What I owned The full risk portfolio: payment fraud, phishing, malware, counterfeit goods and editorial abuse. ## Key decision: rules to ML Instead of adding more rules, we moved detection to machine learning models that learn from labelled abuse and adapt as patterns shift, with rules kept as a safety net where precision mattered most. The product work was as much about the system around the models as the models themselves: labelling, review workflows and the tolerance for false positives that could block legitimate advertisers. ## Outcome - Fraud loss down 62% year over year, about $20M. - Time to detect abuse down from 4 hours to 5 minutes, using unsupervised models that adapted as fraudsters changed tactics. - User escalations from phishing and malware down 95%, to 0.6%, within 6 months. ## What I took from it This was my first experience turning a manual, expert-driven process into an ML system. I’ve applied the same pattern since: replacing sampled, human review with models that cover everything, which is exactly what ProdLoop.ai does for calls. ← All work Next: Microsoft Teams search & ML platform → --- Source: https://rajamohan.me/leadership # How I lead The principles I’ve run product teams and a company on, at Microsoft and at ProdLoop.ai. If you’re thinking of working with me, this is what to expect. ## Operating principles - Customer truth is the only truth. Opinions in the room, including mine, lose to what customers actually do. I’d rather watch ten calls than read one summary deck. - Question the problem before solving it. The most valuable product work is often deciding that the brief is wrong. - Usage, stickiness and value are the north star. Shipping is an input. People coming back because the product helps them is the outcome. - Outcomes over effort. Hard work that doesn’t move the outcome isn’t the goal. I measure teams, and myself, on results. - Speed with acceptable quality. Most decisions are reversible; make them fast. The few that aren’t deserve the time. - Action over endless discussion. When we disagree, we test. A small experiment usually settles what a long meeting can’t. - Build the system, not just the feature. Shared platforms, signals and evals compound. I look for the work that makes every other team faster. ## How I work with people Care and candor, together. People do their best work when they know you’re on their side and that you’ll tell them the truth. I’ve written about this in From Care to Candor (https://rajamohan.substack.com/p/from-care-to-candor-transforming) and Care: The Currency for Managers (https://rajamohan.substack.com/p/care-currency-for-managers). Respect everyone, and do the right thing unseen. Integrity is what you do when nobody is checking. Careers are a jungle gym, not a ladder. I encourage people to move sideways into whatever builds their strengths, and I’d rather help someone grow out of my team than keep them in a role they’ve outgrown. Room for deep work. I protect focus time for my team and myself; good product thinking needs long uninterrupted stretches (https://rajamohan.substack.com/p/focus-and-ability-to-do-deep-work). ## Teams and businesses I’ve led - Led a team of 12 PMs across Microsoft Teams Search and Mobile, partnering with 60+ engineers and designers, through Teams’ growth from 500K to 350M users. - Bootstrapped the Teams ML platform, getting other teams to build on shared affinity signals. - Founder and CEO of ProdLoop.ai: I hire and lead a team of forward-deployed generalists, own pricing, sales and renewals, and have grown the company to 15 enterprise customers and ~$24K MRR. - Run project briefs for engineering students and evaluate their work, as a way to find and grow early talent. --- Source: https://rajamohan.me/career # Career Senior product leader, engineer turned PM. About 20 years across software engineering, product management and entrepreneurship, in India, the US, Singapore and the Nordics. ## Experience PL Jan 2024 – Present · Bengaluru Founder & CEO ProdLoop.ai (https://prodloop.ai) - Building self-improving AI agents for enterprise voice operations: ~$24K MRR and 15 enterprise customers (Porter, Pepperfry, Vedantu, Snabbit, Namma Yatri and others); I own product, pricing, sales and renewals. - Customer results include 12% incremental revenue at Vedantu, 100% QA coverage with 85% less manual review at Pepperfry, and 20% fewer “not reachable” calls at Porter. Customer stories → (https://prodloop.ai/customer-stories) - LLM graders with ~95% agreement with human reviewers across 22 Indian languages. - Raised ~$450K and lead a team of forward-deployed generalists; funders include Google for Startups, Microsoft for Startups, AWS Activate and an NSRCEL–Kotak grant. Case study → MS Mar 2018 – Sep 2023 · Bengaluru Principal PM Lead, Search & Mobile Microsoft Teams - Led 12 PMs and partnered with 60+ engineers and designers. Set the charter for Teams search as the front door to M365 knowledge. - Led search product as Teams grew from ~500K to ~350M users; delivered a relevance-ranked results page (+1.7% search session success). - Bootstrapped the Teams ML platform with user–user and user–channel affinity signals that fixed autosuggest and personalization. - Led the PM team for Teams mobile. Case study → MS Feb 2017 – Feb 2018 Senior PM, Marketplace & Serving Microsoft Bing Ads - Grew revenue per mille +17% YoY across international marketplaces (~$400M/yr in revenue owned), shipping 10+ product packages in 6 months. Case study → MS Apr 2015 – Jan 2017 Senior PM, Ad Quality Microsoft Bing Ads - Owned the risk portfolio: payment fraud, phishing, malware, counterfeit and editorial abuse. - Moved detection from rules to ML: fraud loss −62% YoY (~$20M), time to detect 4 hours → 5 minutes, phishing and malware escalations −95%. Case study → II ~3 years · India, Stockholm, Denmark & the Nordics Founding Engineer IdealInvent - Built a BPM product from scratch as a founding engineer. - Travelled to Stockholm, Denmark and across the Nordics to implement it with customers on site. SWE Early career · India Software Engineer Infosys, Trigent, Prodapt The engineering foundation that still shapes how I work with engineers and judge technical trade-offs. ## Education IIMB MBA Indian Institute of Management Bangalore BE Bachelor’s degree, Engineering ## Quick facts Who is Raja Mohan?: Raja Mohan is a senior product leader based in Bengaluru, India, and an engineer turned product manager. He is the founder and CEO of ProdLoop.ai, a voice-AI company, and previously led product for Microsoft Teams Search and Mobile and worked on machine learning for Microsoft Bing Ads. What is Raja Mohan doing now?: He is building ProdLoop.ai, which makes self-improving AI agents for enterprise voice operations. ProdLoop.ai has about $24K in monthly recurring revenue and 15 enterprise customers, including Porter, Pepperfry and Vedantu. What product leadership experience does he have?: At Microsoft he was Principal PM Lead for Teams Search and Mobile, leading 12 product managers and partnering with 60+ engineers and designers while Teams grew from about 500K to 350M users. On Bing Ads he grew international marketplace revenue per mille 17% year over year and cut ad fraud loss 62%. What does he specialize in?: AI and ML products, voice and conversational AI, LLM evaluation, enterprise search and relevance, ads marketplaces, trust and safety, and taking products from zero to one. Where is he based, and how do I contact him?: Bengaluru, Karnataka, India. Reach him on LinkedIn at linkedin.com/in/mohanraja or by email at rajamohands@gmail.com. Full history on LinkedIn (https://www.linkedin.com/in/mohanraja/), or email rajamohands@gmail.com for my resume. --- Source: https://rajamohan.me/sidequests # Sidequests The stuff I do when I’m not building products — running, mountains, and books. This is where I try to practice what I write about: just keep showing up. ## Running I spent years sure that running wasn’t for me. I was wrong. Two half marathons later, 5AM runs with the JJ group have become my favourite way to think. - I Was Wrong. Running is Great. (https://rajamohan.substack.com/p/i-was-wrong-running-is-great) Oct 2023 A longtime strength-training devotee finally gives running a chance. - Power of Community & Give Grace to Yourself (https://rajamohan.substack.com/p/power-of-community-give-grace-to) Jan 2024 Reflections from my second half marathon. ## Mountains & Travel In 2025 I trekked to Everest Base Camp — the mountains have a way of quieting everything down and reminding you what matters. The same year took us out into nature on a family trip to Kenya. - Everest Base Camp – Reflections from the Trek (https://rajamohan.substack.com/p/everest-base-camp-reflections-from) Apr 2025 What the mountains taught me about silence, mindset, and showing up. ## Health & Showing Up Five years of making health a non-negotiable — and learning that consistency beats intensity every single time. - Health Has to Be P0 (https://rajamohan.substack.com/p/health-has-to-be-p0) Aug 2025 Five years of making health a ritual, and learning to keep showing up. - 2025: A Year of Showing Up (https://rajamohan.substack.com/p/2025-a-year-of-showing-up) Dec 2025 Pausing to look back as the year comes to a close. ## Reading I read to keep learning — and in 2022 I found my way back to fiction. A few favourites: - 5 Books I Loved Reading in 2022 (https://rajamohan.substack.com/p/5-books-i-loved-reading-in-2022) Feb 2023 Returning to fiction — three of my five favourites were novels. ← Back to my story, or read more on Substack (https://rajamohan.substack.com/).