TetraScience

The future of science depends on professionals who can bridge traditionally separate worlds. We're actively growing our team and seeking top-tier talent to join us as we scale to meet explosive customer demand. Every person who joins our team gets to work on industry-defining collaborations from day one. If you're someone who wants your work to have real impact—we'd love to connect. Check out our open positions: https://xmrrwallet.com/cmx.plnkd.in/eWeEHP7

🌟 Live showcase tomorrow, July 30th at 9am ET (3pm CET) and 1pm ET! 🌟 Tech transfers between departments or sites are rarely smooth, especially when teams rely on different CDSs like Empower and Chromeleon. Without consistent data and context, method validation can quickly go off track. See how the latest tools streamline transfers and protect quality: ➡️ Golden Chromatogram Profiling – Quantify peak deviations across CDSs and sites ➡️ System Usage Monitoring – Get complete visibility into instrument activity ➡️ Automated OOS Detection – Detect anomalies fast with ML-driven alerts Led by our scientists and engineers, the showcases dive into real-world use cases. This isn’t a webinar—it’s a working session. Reserve your spot here: https://xmrrwallet.com/cmx.plnkd.in/ee3dZH9h

The work we're doing at TetraScience is unlike anything else in the industry. We're industrializing AI-native scientific datasets and collaborating deeply with the world's leading biopharma companies to improve how they use scientific data and AI for drug R&D, QC, and manufacturing. We're hiring! Why come over to TetraScience now? Every person who joins our team gets to work on industry-defining collaborations from day one. No waiting years to touch meaningful projects—you'll immediately be contributing to breakthroughs that could accelerate life-saving treatments. We're actively growing our team here in Boston/Cambridge with some compelling roles. We're seeking top-tier talent to join us as we scale to meet explosive customer demand: 🔬 𝗦𝗰𝗶𝗲𝗻𝘁𝗶𝗳𝗶𝗰 𝗕𝘂𝘀𝗶𝗻𝗲𝘀𝘀 𝗔𝗻𝗮𝗹𝘆𝘀𝘁𝘀 – Bridge cutting-edge science with business impact ⚗️ 𝗦𝗰𝗶𝗲𝗻𝘁𝗶𝗳𝗶𝗰 𝗗𝗮𝘁𝗮 𝗔𝗿𝗰𝗵𝗶𝘁𝗲𝗰𝘁𝘀 – Design the data foundations for tomorrow's discoveries 🤖 𝗔𝗽𝗽𝗹𝗶𝗲𝗱 𝗔𝗜/𝗠𝗟 𝗘𝗻𝗴𝗶𝗻𝗲𝗲𝗿𝘀 – Build AI systems that directly accelerate drug development 🛠️ 𝗔𝗜 𝗣𝗹𝗮𝘁𝗳𝗼𝗿𝗺 𝗘𝗻𝗴𝗶𝗻𝗲𝗲𝗿𝘀 – Create the infrastructure enabling scientific innovation If you're someone who wants your work to have real impact—where your expertise directly influences how pharmaceutical R&D gets done—we'd love to connect. We're pioneering an entirely new category, and frankly, opportunities like this don't come around often. Check out our open positions: https://xmrrwallet.com/cmx.plnkd.in/eWeEHP7

The UK Research Integrity Office (UKRIO) just released a watershed report on AI in research—and it validates everything we've been saying about the Scientific AI revolution. 🧬 𝗧𝗵𝗲 𝗿𝗲𝗮𝗹𝗶𝘁𝘆: While every pharma CEO talks about AI transformation, most organizations are sleepwalking into a research integrity crisis. 𝗙𝗶𝘃𝗲 𝗰𝗿𝗶𝘁𝗶𝗰𝗮𝗹 𝗳𝗮𝗶𝗹𝘂𝗿𝗲 𝗽𝗼𝗶𝗻𝘁𝘀 𝘁𝗵𝗲 𝗨𝗞𝗥𝗜𝗢 𝗶𝗱𝗲𝗻𝘁𝗶𝗳𝗶𝗲𝗱:  → Legal compliance gaps: organizations using AI tools without proper data governance  → Ethical blind spots: AI systems perpetuating bias without human oversight → Research record contamination: fabricated sources and "AI hallucinations" polluting scientific literature  → Transparency failures: researchers publishing AI-generated content without disclosure  → Skills atrophy: over-reliance on AI undermining critical thinking capabilities This isn't just about research integrity. It's about the future of scientific innovation itself. The report's core insight: "𝗔𝘂𝘁𝗵𝗼𝗿𝘀 𝗿𝗲𝗺𝗮𝗶𝗻 𝗳𝘂𝗹𝗹𝘆 𝗿𝗲𝘀𝗽𝗼𝗻𝘀𝗶𝗯𝗹𝗲 𝗳𝗼𝗿 𝗲𝘃𝗲𝗿𝘆𝘁𝗵𝗶𝗻𝗴 𝗽𝘂𝗯𝗹𝗶𝘀𝗵𝗲𝗱 𝘂𝗻𝗱𝗲𝗿 𝘁𝗵𝗲𝗶𝗿 𝗻𝗮𝗺𝗲, 𝗿𝗲𝗴𝗮𝗿𝗱𝗹𝗲𝘀𝘀 𝗼𝗳 𝘄𝗵𝗲𝘁𝗵𝗲𝗿 𝗶𝘁 𝘄𝗮𝘀 𝗴𝗲𝗻𝗲𝗿𝗮𝘁𝗲𝗱 𝗯𝘆 𝗔𝗜." 𝗕𝘂𝘁 𝗵𝗲𝗿𝗲'𝘀 𝘄𝗵𝗮𝘁 𝘁𝗵𝗲 𝗿𝗲𝗽𝗼𝗿𝘁 𝗱𝗼𝗲𝘀𝗻'𝘁 𝗮𝗱𝗱𝗿𝗲𝘀𝘀: How do you maintain integrity when your data infrastructure is fundamentally broken? You can't bolt ethical AI practices onto fragmented data silos. You can't ensure transparency when your data lineage is opaque. You can't maintain human oversight when your data scientists spend 80% of their time on data wrangling instead of insights. 𝗧𝗵𝗶𝘀 𝗶𝘀 𝘄𝗵𝘆 𝘄𝗲 𝗯𝘂𝗶𝗹𝘁 𝗦𝗰𝗶𝗲𝗻𝘁𝗶𝗳𝗶𝗰 𝗗𝗮𝘁𝗮 𝗙𝗮𝗰𝘁𝗼𝗿𝗶𝗲𝘀. 𝗧𝗵𝗶𝘀 𝗶𝘀 𝘄𝗵𝘆 𝘄𝗲'𝗿𝗲 𝘁𝗿𝗮𝗶𝗻𝗶𝗻𝗴 𝗦𝗰𝗶𝗯𝗼𝗿𝗴𝘀. 𝗧𝗵𝗶𝘀 𝗶𝘀 𝘄𝗵𝘆 𝗦𝗼𝘃𝗲𝗿𝗲𝗶𝗴𝗻 𝗦𝗰𝗶𝗲𝗻𝘁𝗶𝗳𝗶𝗰 𝗔𝗜 𝗺𝗮𝘁𝘁𝗲𝗿𝘀. The organizations that get this right will lead the next decade of scientific discovery. The ones that don't will be relegated to footnotes in the history of failed drug programs and compromised research. The choice isn't between AI and human expertise—it's between AI with integrity and AI without it. What's your organization doing to ensure your AI transformation doesn't become your integrity crisis? See the full report: https://xmrrwallet.com/cmx.plnkd.in/dPh983r4

🌟 Intern Spotlight 🌟 Harrison Adams joins TetraScience's development team as our youngest contributor this summer. Despite being a high school senior, Harrison's experience with LLM implementation and multi-language programming (Python, Java, C, C++) positions him to contribute meaningfully to our Scientific AI platform development. Harrison will work directly on AI applications that help scientists extract insights from complex experimental data. Each of our interns join engineering and scientific teams working on production systems that serve global pharmaceutical and biotech organizations. Their contributions directly support our mission to radically improve and extend human life through Scientific AI. Welcome to the team, Harrison!

Your legacy SDMS isn’t just outdated—it’s a barrier to innovation. TetraScience helps biopharmas migrate decades of siloed data into a modern, AI-ready cloud platform with: ✅ Vendor-agnostic, harmonized formats ✅ Full experimental and instrument context ✅ GxP-compliant, cloud-native architecture Turn archived data into active insight. Read the blog: https://xmrrwallet.com/cmx.plnkd.in/eM4Hewza

There is no better gig than being a TetraScience summer intern! Each of our interns join engineering and scientific teams working on production systems that serve global pharmaceutical and biotech organizations. Their contributions directly support our mission to radically improve and extend human life through Scientific AI. 🌟 Intern Spotlight 🌟 TetraScience welcomes Sameer Chawla to our engineering team. A computer science and statistics student at University of Maryland, Sameer brings data engineering and machine learning expertise to our Scientific AI mission. His background in full-stack development and data analysis directly supports our work replatforming petabytes of scientific data for AI applications. Sameer will contribute to our data engineering infrastructure that processes scientific data from 1,000+ sources across global biopharma organizations. Welcome to the team, Sameer!

📊 5,000+ hours/year = $1M lost. That’s the cost of manual column documentation at large biopharmas. Chromatography Insights removes the burden: ✅ Automate column usage tracking ✅ Generate audit-ready reports instantly ✅ Consolidate data across CDSs This is chromatography—modernized. Read the blog: https://xmrrwallet.com/cmx.plnkd.in/e4D8ucKB

🧪 𝗔𝗜 𝗶𝘀 𝗿𝗲𝘃𝗼𝗹𝘂𝘁𝗶𝗼𝗻𝗶𝘇𝗶𝗻𝗴 𝗰𝗵𝗿𝗼𝗺𝗮𝘁𝗼𝗴𝗿𝗮𝗽𝗵𝘆, 𝗯𝘂𝘁 𝘁𝗵𝗲 𝗳𝗼𝘂𝗻𝗱𝗮𝘁𝗶𝗼𝗻 𝗿𝗲𝗺𝗮𝗶𝗻𝘀 𝘀𝗰𝗶𝗲𝗻𝘁𝗶𝗳𝗶𝗰 𝗿𝗶𝗴𝗼𝗿 This excellent piece in LCGC International by our own Anthony Edge captures a critical truth: AI's power in separation science lies not in replacing human expertise, but in augmenting it with unprecedented pattern recognition capabilities. Read the article here: https://xmrrwallet.com/cmx.plnkd.in/gBunPgaG 𝗞𝗲𝘆 𝘀𝗰𝗶𝗲𝗻𝘁𝗶𝗳𝗶𝗰 𝗶𝗻𝘀𝗶𝗴𝗵𝘁𝘀: 🔹 AI transforms chromatography from deductive to abductive analysis—discovering relationships human intuition might never conceive 🔹 Data harmonization is essential: "garbage-in-garbage-out" applies strongly to ML models analyzing chromatographic data 🔹 The transition from empirical methods to data-driven precision requires maintaining theoretical understanding 𝗪𝗵𝗮𝘁 𝗿𝗲𝘀𝗼𝗻𝗮𝘁𝗲𝘀 𝗺𝗼𝘀𝘁: "𝘛𝘩𝘦 𝘧𝘶𝘵𝘶𝘳𝘦 𝘴𝘦𝘱𝘢𝘳𝘢𝘵𝘪𝘰𝘯 𝘴𝘤𝘪𝘦𝘯𝘵𝘪𝘴𝘵 𝘸𝘪𝘭𝘭 𝘣𝘦𝘤𝘰𝘮𝘦 𝘮𝘰𝘳𝘦 𝘰𝘧 𝘢𝘯 𝘪𝘯𝘵𝘦𝘳𝘱𝘳𝘦𝘵𝘦𝘳 𝘢𝘯𝘥 𝘴𝘵𝘳𝘢𝘵𝘦𝘨𝘪𝘴𝘵, 𝘶𝘴𝘪𝘯𝘨 𝘈𝘐 𝘵𝘰𝘰𝘭𝘴 𝘸𝘩𝘪𝘭𝘦 𝘢𝘱𝘱𝘭𝘺𝘪𝘯𝘨 𝘧𝘶𝘯𝘥𝘢𝘮𝘦𝘯𝘵𝘢𝘭 𝘱𝘳𝘪𝘯𝘤𝘪𝘱𝘭𝘦𝘴 𝘵𝘩𝘢𝘵 𝘩𝘢𝘷𝘦 𝘨𝘶𝘪𝘥𝘦𝘥 𝘵𝘩𝘦 𝘧𝘪𝘦𝘭𝘥 𝘧𝘰𝘳 𝘨𝘦𝘯𝘦𝘳𝘢𝘵𝘪𝘰𝘯𝘴." At TetraScience, we see this daily—our Sciborgs (scientists with deep data engineering expertise) are proving that the most powerful advances come from combining domain knowledge with computational capabilities. They're designing AI-native scientific datasets that preserve the mechanistic insights chromatographers have developed over decades. 𝗧𝗵𝗲 𝗰𝗵𝗮𝗹𝗹𝗲𝗻𝗴𝗲 𝗮𝗵𝗲𝗮𝗱: ensuring we don't lose the "hard-won, fundamental science" as we embrace AI's pattern-finding power. The best scientific AI will always be built by those who understand both the underlying physics and the computational methods. What's your experience with AI in analytical chemistry? Are you seeing similar patterns in your field? Read the article here: https://xmrrwallet.com/cmx.plnkd.in/gBunPgaG

Traditional SDMS solutions weren’t built for today’s complex, high-volume scientific data. They create bottlenecks and hinder data maturity. Enterprise biopharmas are turning to next-generation scientific data management. See how you can: ✅ Centralize all scientific data—from instruments, software, and legacy SDMS ✅ Automate and integrate workflows for seamless, enterprise-wide data flow ✅ Balance IT governance with scientific flexibility through self-service tools Learn more: https://xmrrwallet.com/cmx.plnkd.in/gawfKpc5