Ketryx

At Ketryx, we "Chase Hard Problems. Together. Celebrate Wins! Together." Sometimes that means tackling complex challenges in regulated AI. Other times, it means tackling each other on the kickball field. Proud to share that the Ketryx kickball team (Ket-kicks?!) just secured our first win of the season! Turns out the teamwork we bring to the office translates pretty well to the field too. Here's to taking collaboration beyond the conference room and celebrating wins wherever we find them. Let's go, team! ⚽🏆 Ps. ALWAYS remember to stretch! #Ketryx #CompanyCulture #Teamwork #StartupLife #KickballChamps

Last night, Viju Menon, Group President of Global Quality and Operations at Stryker, and I co-hosted an executive dinner in NYC. His opening thoughts sparked a candid conversation on what AI actually looks like inside life sciences organizations (i.e. the “hard and messy stuff”). We explored AI through a cross-industry lens, drawing lessons from past technology inflection points and asking what’s truly different this time around. The discussion really dug into how leaders are introducing AI in regulated environments. We talked openly about what real transformation looks like in practice, moving beyond pilots that stall, toward changes that genuinely reshape how teams work day to day. Quality and regulatory readiness emerged as a critical bottleneck, along with the question of whether the industry is prepared to let AI raise the ceiling on innovation rather than constrain it. Ultimately, the conversation returned to leadership. How do you lead teams through rapid change when AI tools are evolving faster than organizational norms? How do you stay grounded, honest, and human while navigating the noise? Grateful to Viju for co-hosting with me and to everyone who attended for the engaging discussion. These conversations are exactly how cross-industry learning happens faster. Excited for what’s next! Sairam Menon, Eva Bica-Winterling, Anne Shim, Brenda Remy, Brenton Fargnoli, M.D., Irene Biniaris, Lee-Anne Zinetti, Michael Schoen, Viju Menon, Teginder Singh, Evan Feinberg

Join us on 28 May 2026 for the exclusive Lunch & Learn “The unfair advantage: Why smart biotechs run trials in Australia.” As biotech companies face increasing pressure to accelerate development timelines while preserving capital, alternative clinical trial pathways are gaining attention. This session will explore how Australia offers a faster regulatory environment, access to a 43.5% R&D tax incentive and high-quality trial infrastructure - all while generating data accepted by the U.S. FDA. Hosted by Acclime, the session will bring together biotech founders, senior operators and industry advisors for a focused discussion on structuring early-stage clinical trials in Australia. The conversation will cover key considerations such as when Australia is the right fit, how to navigate the TGA framework and how to approach entity structuring, budgeting and operational execution. Contributors include Nucleus Network, a global leader in early-phase clinical trials, BioCina, an end-to-end CDMO supporting biopharma manufacturing, and Ketryx, a Boston-based provider of compliant software solutions for life sciences. Event details: 📅 Date: 28 May 2026 🕒 Time: 12:30 PM – 1:30 PM (ET) 📍 Location: Shared upon confirmation Seats are limited. Apply to attend: https://lnkd.in/g3qiqYwb Blair Lucas David Brown

I spoke last night on a preview panel for the Digital Health & AI Innovation Summit alongside Michael Ringel, Mari Heghinian, PhD, and Dr. Amir Lahav. One theme kept surfacing: healthcare does not have an AI ambition problem. It has an implementation problem. 🔬𝗧𝗵𝗲 𝗰𝗼𝗻𝘀𝘁𝗿𝗮𝗶𝗻𝘁 𝗶𝘀 𝗻𝗼𝘁 𝘁𝗵𝗲 𝘀𝗰𝗶𝗲𝗻𝗰𝗲. 𝗜𝘁 𝗶𝘀 𝘁𝗵𝗲 𝗼𝗽𝗲𝗿𝗮𝘁𝗶𝗻𝗴 𝘀𝘆𝘀𝘁𝗲𝗺 𝗮𝗿𝗼𝘂𝗻𝗱 𝘁𝗵𝗲 𝘀𝗰𝗶𝗲𝗻𝗰𝗲. Using AI for compliance isn't inherently a shortcut that negates safety requirements. AI is a tool to get safe products to patients faster. Every time an engineer copies and pastes to update a document, a patient is waiting longer for access. 🤖 𝗔𝗜 𝗶𝘀 𝘁𝗵𝗲 𝗶𝗻𝗳𝗿𝗮𝘀𝘁𝗿𝘂𝗰𝘁𝘂𝗿𝗲, 𝗻𝗼𝘁 𝘁𝗵𝗲 𝗱𝗲𝗰𝗶𝘀𝗶𝗼𝗻 𝗺𝗮𝗸𝗲𝗿. One question from the panel: is there a conflict in using AI to monitor AI? At this scale of complexity, there is no other path. AI compiles evidence, generates traceability, and surfaces change impact in real time. But a human is always in the loop at approval gates to answer: is this safe enough? 🔄 𝗧𝗵𝗲 𝗳𝘂𝘁𝘂𝗿𝗲 𝗶𝘀 𝗰𝗼𝗻𝘁𝗶𝗻𝘂𝗼𝘂𝘀 𝗰𝗼𝗺𝗽𝗹𝗶𝗮𝗻𝗰𝗲. Cytokines in an ICU patient change second by second, yet we sample them every 12 hours. AI models evolve daily, yet evidence gets generated manually. If the system changes continuously, compliance cannot remain episodic. If your team is grappling with any of this, come join us at the DHAI Summit on June 8-9 in Boston. Where is your team in its AI experimentation journey, and how are you building dynamic compliance into your operating model?

Looking forward to joining Michael Ringel, Mari Heghinian, PhD, and Dr. Amir Lahav tomorrow for a conversation on where AI is actually creating impact across healthcare, pharma, and regulated product development. Excited to dig into what it takes to move AI from experimentation to real-world deployment in safety-critical environments ahead of the DHAI Summit coming to Boston this June! If you’re in Greater Boston, would love to see you there: 👉 luma.com/lm1fj31t

The FDA just announced Elsa 4.0 and HALO. Every reviewer now has a tool that can cross-reference your entire submission history in seconds. 𝗠𝘆 𝘁𝗮𝗸𝗲𝗮𝘄𝗮𝘆: 𝗜𝗳 𝘁𝗵𝗲 𝗙𝗗𝗔 𝗶𝘀 𝘂𝘀𝗶𝗻𝗴 𝗔𝗜 𝘁𝗼 𝗿𝗲𝘃𝗶𝗲𝘄 𝘆𝗼𝘂𝗿 𝘀𝘂𝗯𝗺𝗶𝘀𝘀𝗶𝗼𝗻𝘀, 𝘆𝗼𝘂 𝘀𝗵𝗼𝘂𝗹𝗱 𝗯𝗲 𝘂𝘀𝗶𝗻𝗴 𝗔𝗜 𝘁𝗼 𝗮𝘂𝗱𝗶𝘁 𝘁𝗵𝗲𝗺 𝗳𝗶𝗿𝘀𝘁. Downstream of this announcement, my hypothesis is that 𝗮𝗽𝗽𝗿𝗼𝘃𝗮𝗹 𝘁𝗶𝗺𝗲𝗹𝗶𝗻𝗲𝘀 𝗮𝗿𝗲 𝗮𝗯𝗼𝘂𝘁 𝘁𝗼 𝘀𝗽𝗹𝗶𝘁 𝗶𝗻 𝘁𝘄𝗼. Companies with structured, traceable documentation will move faster through review. Companies managing compliance in spreadsheets will get more deficiency letters, experience more back-and-forth with the agency, and watch their launch dates slip further and further out. If you want to be the “move faster group,” here’s what you need to do: 1️⃣ 𝗙𝗶𝗻𝗱 𝘁𝗵𝗲 𝗴𝗮𝗽𝘀 𝗼𝗻 𝘆𝗼𝘂𝗿 𝘁𝗶𝗺𝗲𝗹𝗶𝗻𝗲. Run an AI-assisted review to surface broken traceability links and gaps in your documentation before you submit.  2️⃣ 𝗦𝘁𝗿𝘂𝗰𝘁𝘂𝗿𝗲 𝗳𝗼𝗿 𝗺𝗮𝗰𝗵𝗶𝗻𝗲 𝗿𝗲𝗮𝗱𝗮𝗯𝗶𝗹𝗶𝘁𝘆. Prose a human reviewer can follow may still have inconsistencies an AI will flag. Ensure your documentation has consistent IDs, clean traceability links, and structured data.  3️⃣ 𝗧𝗿𝗲𝗮𝘁 𝘁𝗵𝗶𝘀 𝗮𝘀 𝗮 𝗰𝗼𝗺𝗽𝗲𝘁𝗶𝘁𝗶𝘃𝗲 𝗱𝗲𝗰𝗶𝘀𝗶𝗼𝗻. Most teams will hesitate while they figure out what this means. The ones that get AI into their compliance process now will move faster while everyone else is still catching up. What's your take on how Elsa 4.0 and HALO are going to change review timelines or internal processes at submitting companies? 👉 Check out the announcement: https://lnkd.in/exXG9MWs