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Suku Krishnaraj is a visionary GTM leader, seasoned senior executive with extensive…
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Suku Krishnaraj Chettiar shared thisA customer at a large US bank put the bar simply: Accuracy needs to be auditable. Permissions need to be provable. The shared knowledge needs to compound over time. In financial services, you need all three. Most AI only gets you one. I spend a lot of time talking with FSI leaders: banks, capital markets, asset managers, research houses. Everyone is evaluating AI. But very few have moved past security review. The models are not the hard part. For AI to do real work in this industry, the answer has to be accurate enough to trust when a wrong answer creates real exposure. It has to respect who is allowed to see what, down to the row and the column. That is not a preference. In this industry, it is a regulatory line. And it has to understand the shared context the team has built over time. Not just what the data says, but how the business interprets it. That is when teams start handing real work to AI. Here’s what that looked like at a global investment bank. A live outage. Millions, sometimes billions, in motion. People across the team asking questions, correcting assumptions, and teaching the system as they worked. That is what the clip shows. The next incident starts smarter than the last one. In financial services, accuracy, permissions, and shared context are not separate features. They are the minimum bar.
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Suku Krishnaraj Chettiar reposted thisSuku Krishnaraj Chettiar reposted thisINSANE moment at PromptQL! Just closed our first 8 figure deal at a Fortune 500. Excluding token costs tyvm. 🙃 I jotted down some learnings that might be useful if you're a founder in the AI space – esp. at the application layer. My observations are downstream of 3 big changes: ⋅Unstable reference architecture ⋅Existential chaos on both sides ⋅Security The six lessons: 1️⃣ Navigators, beyond just champions 2️⃣ FDXs along with FDEs (forward deployed execs) 3️⃣ Tokens is the budget line-item 4️⃣ Customers want to deepen their moat, not just understand yours 5️⃣ Multi-tenancy is dead 6️⃣ Connect to the data, don't move it Ofc all the old rules still apply. You need a real product, provide value, build a relationship etc. No shortcuts there. Full article here: https://lnkd.in/gVuzhXpb
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Suku Krishnaraj Chettiar reposted thisSuku Krishnaraj Chettiar reposted thisAs a distance runner, I've spent years living inside my own training data, mileage, splits, recovery, trying to figure out what those numbers actually meant. This summer at Alyf Health, I got to chase that same question for patients: could activity data do more than track movement and actually help predict health outcomes? Alyf is an AI-native care platform that connects patients and their care teams in real time, so providers aren't limited to a 15 minute appointment every few years. Continuous wearable data flows straight to the care team, flagging signs of decline early enough to act on, sometimes before the patient notices anything. I built one piece of that system during my internship with Alyf this summer: the Balanced Activity Profile (BAP), a 0-100 score that turns raw wearable data into something a patient can actually act on. Generic trackers give everyone the same targets, 10,000 steps, close your rings. That's the wrong advice for a patient recovering from a health event. What counts as "enough" looks completely different for a 68-year-old six weeks post-surgery than a 22-year-old collegiate athlete. BAP scores activity across five axes and personalizes the baseline to each patient. I built the personalization layer, dashboard, and BAP chatbot using PromptQL. The chatbot can query a patient’s activity data, explain what’s driving their score, and answer questions based on their own history instead of giving generic fitness advice. We also built in safety constraints so suggestions stay appropriate for the patient’s condition. That's the disconnect I want to help close: giving practitioners a way to remotely monitor patients and catch problems before they become emergencies, instead of waiting for the next visit to find out something changed in the meantime. Really grateful to Sachin Desai for the mentorship and the opportunity to build this. If you are curious about how it works, or want to test your own wearable data, you can try BAP yourself here: https://lnkd.in/gAvXqG5e
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Suku Krishnaraj Chettiar shared thisCollaboration is table stakes. Context is the bottleneck. That's the shift we launched and it's what I watch happen inside our team every day.Suku Krishnaraj Chettiar shared thisWe raised $136M and built the first AI version of Slack. Slack is broken in the age of AI: - Work gets constantly interrupted by pings when Agents should be able to prioritize and answer for you -Slackbots like Claude tag still can’t search for information fast and effectively - Context is not shared across teams and agents. There should be one shared and secure context layer. PromptQL solves this. With it you can: - Build entire documents, dashboards, and apps, collaboratively with your team, from your shared context. - Give a new hire the EXACT same context on day one as someone who’s been working at the company for 3 years. - Have your agent tap someone else's agent on the shoulder and coordinate without involving either human. We're hurtling towards a future where AI does more and more work for us. And so AI native workspace needs to be designed around realtime context management and not just realtime collaboration. Slack has a really big problem: it’s SO good at company communication that it can’t refocus the product to company context. In 2018, Rajoshi and I open sourced Hasura GraphQL engine, grew it to over 600M downloads. It's used by millions of developers and running at scale inside Fortune 100 enterprises to deliver fast, secure access to their most critical data. When we spun up our research lab to make AI reliable on data, we realized quickly that the biggest challenge was securely capturing the shared context in multiple people's heads and setting up the right security guardrails around context and data access. Our belief becomes stronger every year: humans and agents should be working together quicker, smarter, and making better decisions while continuously capturing shared context. So far PromptQL is working and working well: McDonalds, Instacart, Cisco and Swiggy use it already. To celebrate our launch, we’re giving away a massive zip file of skills and agents that help founders and entrepreneurs make better decisions. Comment what your business does and we’ll send it across!
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Suku Krishnaraj Chettiar shared thisThe company brain isn't an AI problem. It's a shared context problem. And it's solved.Suku Krishnaraj Chettiar shared thisEven Anthropic couldn't solve the company brain problem with Claude Tag's memory, despite being in the perfect spot for it. Shared context & data for AI is insanely hard. We might have cracked it. I'm speaking @aiDotEngineer at 2.50pm tomorrow on How To Build A Company Brain That Doesnt Leak Company Secrets. This is the first time ever I'll be sharing the inner details of building a system that works for AI native startups and Fortune 100 banks alike, without compromising on AI adoption and accuracy. Excited to share and meet everyone! Link in the comments!
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Suku Krishnaraj Chettiar shared thisPersonal context stays with one person. Shared context compounds across the team. Everyone is racing to give AI more context. Most are solving the easy half. The easy half is personal context. Your notes, your prompts, your workflows, the things that make AI work better for you. The hard half is the context that lives across a whole team and never makes it into a doc. How your team actually counts a deal. What qualifies as a proper disco. Why one motion is treated differently from another. The rules a business runs on that live across dozens of people's heads and rarely make it into documentation. A few weeks ago I started a thread with my AI to build out our second half forecast and streamline our GTM motions. Normally that is a week of meetings wasted. So I pulled in folks from different teams right into the same PromptQL AI thread. Sales fixed how we actually compute conversion rate, a BDR nailed down what counts as a disco, Marketing laid out the real attribution. By the end it was a clean four bucket structure with a live scorecard. I got work done async over a day, which would have taken me a week and multiple meetings. And every correction and edit the team made was simultaneously "taught" to our shared AI. None of that disappeared when they closed the tab. It stayed in the thread, got updated in our company wiki and became part of the shared context the next person could build on. They were not just answering my questions. They were teaching the "company brain". Personal context helps one person work faster. Shared context compounds. Every interaction makes the next interaction better for everyone. If everyone on your team is teaching AI the same things separately, you're still solving the easy half. The leverage is in the half you build together.
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Suku Krishnaraj Chettiar reposted thisSuku Krishnaraj Chettiar reposted thisThe most common thing I hear from folks is why cant we add a context bot or a company brain bot to slack. My friends. That was the very first thing we tried! It failed spectacularly. All “scrape slack” for context things have also failed. Cute demo, but unusable irl. Slack won't become the company brain because it's an absolutely excellent product for being something else: the company mouth. Wrote a thing to explain why...Slack Won’t Become the Company Brain Because It’s Already the Company MouthSlack Won’t Become the Company Brain Because It’s Already the Company Mouth
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Suku Krishnaraj Chettiar reposted thisSuku Krishnaraj Chettiar reposted thisWe just shut down our internal Slack entirely. I spoke with Carl Franzen at VentureBeat this week about why. We replaced it with PromptQL, and now: every conversation has just become work that actually gets done. Not messages about work. Not threads that need follow-up. The conversation itself moves things forward. That's because the context your AI needs to be useful is already in your team's heads. The problem is that all the tools to capture it are too complicated, take too long, and when you finally get around to it; they just don't work. Chat tools were designed to be fast. Not to be memory. And those two things are fundamentally in conflict when you're trying to build AI that actually works. Every day, your team generates knowledge that disappears within 24-48 hours. A marketer defines what a "recycled lead" really means. An engineer figures out why EU payments broke after the Adyen migration. A PM clarifies what "active user" actually means in your context. That knowledge lives in scroll history. Which means it's gone. Or worse, your AI surfaces some random memory from three months ago, a doctor's appointment, a one-off reminder, as if it still matters. So when an AI agent shows up to do work, it starts from zero. Every. Single. Time. Your team ends up re-explaining context to tools that were supposed to save them time. This is not a model problem. The models are remarkable. This is an architecture problem. Most companies are bolting AI onto communication & data infrastructure, neither of which was ever designed to support the other; then wondering why their agents hallucinate and their teams are drowning in "coordination theater." We built PromptQL to work differently. Every conversation gets work done and also teaches the system something, that makes the AI better for everyone else. Context compounds instead of evaporating. When our AI fixes a production bug, it already knows your codebase conventions, your deployment patterns, your business logic; because your team taught it just by working. The power of collaborating with your team, with AI in the loop, is something you have to experience to believe. It's been truly magical for us internally! We're now in preview - so go ahead to promptql.io and try this yourself! Let me know if you need any help setting up :) Thanks to Carl and VentureBeat for the deep dive. Link in the comments 👇
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Congrats Manny E. Lopez on a great run!
I highly recommend Manny for any product strategy or competitive intelligence roles! He is a PRO!
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Suku Krishnaraj Chettiar liked thisSuku Krishnaraj Chettiar liked thisI'm excited to be hosting two events at SF Tech Week this October. Tech Week is the world’s largest decentralized tech gathering, bringing together 100,000+ unique attendees across 3,000+ community-led events in San Francisco, New York, Los Angeles, and Boston. Sign up for my event and other Tech Week events here: https://tech-week.com #SFTechWeek
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Suku Krishnaraj Chettiar liked thisSuku Krishnaraj Chettiar liked thisI'm pleased to share that Qnovo is piloting a new Battery Genome application. It is designed to assess EV battery health and residual value. Thank you to Charged Fleet for covering the news. Battery condition can vary widely between vehicles of similar age and mileage. The differences come from charging behavior, operating patterns, environmental exposure, and cell-level variation. Because the Battery Genome runs in the vehicle, it can analyze data that aftermarket inspections cannot reach. It detects aging patterns and potential defects, and it projects future degradation. This matters as EU battery passport requirements take effect in early 2027. We welcome passports as a move toward transparency, but without a deeper understanding of the battery, they can open OEMs to liability. The Battery Genome complements the passport by adding more certainty around what the battery is actually doing. Read the full article here 👉 https://lnkd.in/gHXM_TCG #BatteryIntelligence #EV #BatteryPassportQnovo Pilots Battery Health Software for EV Residual Value AssessmentsQnovo Pilots Battery Health Software for EV Residual Value Assessments
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Suku Krishnaraj Chettiar liked thisSuku Krishnaraj Chettiar liked thisToday I am joining New Relic as Chief Executive Officer and member of the Board of Directors. I have spent my career working on the infrastructure problems that sit closest to how engineers build and operate software. As AI transforms how applications are built and operated, engineers need observability tools they can trust, and New Relic is built to deliver that. New Relic sits at an extraordinary inflection point in enterprise technology. As AI transforms application architectures and complexity escalates, engineers need observability tools they can trust implicitly, and New Relic is built to deliver exactly that. I have dedicated my career to solving this problem, and I am thrilled to lead this talented team as we expand our AI-powered observability platform and help our customers build with clarity and confidence. Thank you to the New Relic team for the warm welcome. I look forward to working with each of you and am thrilled to lead such a talented team.
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Suku Krishnaraj Chettiar liked thisSuku Krishnaraj Chettiar liked thisYesterday I went to the PromptQL Hackathon to learn about Jev… and somehow walked away tied for 🥇 first place! Huge thank you to our WOMEN x AI (WxAI) community member 🫶 Laura Liu Cruickshanks for inviting me. It was also so fun seeing fellow community members Natali Wong Patty L. and Luis Arevalo there. 💜 I learned a lot about Jev, TypeSafe AI’s System One model. Unlike an LLM that generates text, Jev is built for fast, structured, probability-based decisions things like yes/no judgments, choosing between options, scoring, and routing based on confidence. For the hackathon, I built a WOMEN x AI (WxAI) Task Manager using PromptQL + Jev to solve a very real problem: keeping track of tasks spread across emails, docs, slides, events, and partnerships. It connected to my Google Workspace and created a dashboard that helped me understand what I should prioritize and why. I also ran the same prompt in ChatGPT, and for this particular workflow, PromptQL felt faster and more intuitive. I was honored to share the podium with 🏆 Natali Wong (The Anti-Boring Club) and 🏆 Deborah Jacob (SF Tech Week Planner) Such a fun reminder: learn something new, build something useful, and keep experimenting. 🚀 A huge thank you to the PromptQL team! Rajoshi Ghosh, Laura Liu Cruickshanks, Tanmai Gopal and Anushrut Gupta.
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Suku Krishnaraj Chettiar liked thisSuku Krishnaraj Chettiar liked thisWe are thrilled to welcome Kriti Dutta to the Dataworkz team as our new Data Scientist. Kriti joins as Dataworkz grows into AI agent platform that automate the repetitive, high-stakes workflows enterprises run every day. She has deep expertise in machine learning, NLP and big data, and has built RAG pipelines for enterprise knowledge systems, fine-tuned LLMs for domain-specific use cases, and designed multi-agent systems that automate complex decision-making. That's exactly what we need to make our agents more accurate, more reliable and more valuable for our customers. Just as important, Kriti knows how to turn AI into measurable outcomes like lower costs, better efficiency and smarter automation. That fits right in at an ROI-focused AI company. Welcome to the team, Kriti! We're excited for what's ahead.
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Suku Krishnaraj Chettiar liked thisSuku Krishnaraj Chettiar liked thisAfter a great run at Sumo Logic, my time there has come to an end. My role was eliminated last week as part of a broader company restructuring. I'm incredibly grateful for the people I had the opportunity to work with and for the chance to build and lead Competitive Intelligence at Sumo. Over the years, I got to work across product, product marketing, sales and leadership, helping teams better understand an increasingly competitive cybersecurity and observability market and, more importantly, turn those insights into action. I'm proud of what we built and especially grateful to all the colleagues, customers and partners I learned from along the way. Now I'm looking forward to what's next. I'm exploring leadership opportunities in Competitive Intelligence, Product Intelligence and Product Strategy, particularly in cybersecurity, observability and AI. If you know of a team looking for someone with that background, I'd love to connect. And to everyone else impacted by the recent changes at Sumo, I'm happy to help however I can. #OpenToWork #CompetitiveIntelligence #ProductIntelligence #Cybersecurity #Observability
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Suku Krishnaraj Chettiar liked thisAs chip designers, building safe and functional logic and circuits is a challenge we've successfully tackled for decades, using methodologies and systems like unit test-benches, functional coverage, random test vector generation etc. NVIDIA Open Agent Safety Platform applies similar concepts to testing agents. OpenShell sandboxes the agent and verifies policy before it runs, while Sentry on BlueField-4 sits on the node's only path to the model, out of band, enforcing at line speed. The agent can't reach the watchdog and doesn't need to know it's there. This new infrastructure will accelerate the deployment of safe agents and eliminate the need for any trade-off between safety and functionality - something circuit and logic design engineers do routinely. "Trust and innovation are not in conflict. Safety is how trust is earned. We must build not only the most capable AI, but the most trusted AI, so that this extraordinary technology can realize its enormous promise for the world."Suku Krishnaraj Chettiar liked thisToday, with over 100 industry partners, we introduced the NVIDIA Open Agent Safety Platform, bringing together OpenShell and Sentry. Artificial intelligence is extraordinary technology that will advance discovery, productivity, security, health, and prosperity for generations to come. But its full promise can only be realized when people have confidence that AI is being built to be safe and deployed with wisdom and responsibility. NVIDIA Open Agent Safety Platform Reference Design combines NVIDIA OpenShell and NVIDIA Sentry. OpenShell is an open-source secure runtime that gives AI agents clear, enforceable boundaries. It traces their actions and enforces policy as they work. NVIDIA Sentry delivers added layer of security with hardware-based enforcement on NVIDIA BlueField, continuously monitoring agent activity through a trusted telemetry and detection pipeline and enabling millisecond-scale containment and quarantine. This is bigger than a single product. It's the beginning of an open ecosystem to build the trust layer for safe agent systems. Together, we are building the foundation of the AI economy. Trust and innovation are not in conflict. Safety is how trust is earned. We must build not only the most capable AI, but the most trusted AI, so that this extraordinary technology can realize its enormous promise for the world. https://nvda.ws/4hOkDx7
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Suku Krishnaraj Chettiar liked thisSuku Krishnaraj Chettiar liked thisJohns Hopkins University has been recognized for its academic and teaching excellence and ranks No. 9 among national universities according to the latest undergraduate rankings from U.S. News & World Report. Hopkins also ranked No. 4 in undergraduate research/creative projects, reflecting the university’s longstanding commitment to providing students with opportunities to engage in research and creative projects beyond the classroom. More than 90% of Hopkins undergraduates participate in at least one research experience during their time at the university, spending an average of 6 to 10 hours per week on their research. JHU’s rankings for undergraduate programs and academic disciplines also include: • No. 1 in biomedical engineering • No. 5 (tied) in biocomputing/bioinformatics/biotechnology • No. 12 in engineering https://lnkd.in/eGX74SAn
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