TetraScience

Watching our team work the BioIT World booth yesterday: Every conversation starts with "what problem are you trying to solve?" and ends 20 minutes later with someone pulling out their phone to schedule a follow-up. The pattern repeats: Someone asks about connecting instruments, we show them the data layer, they realize their AI strategy has been built on sand. TetraScience's Tetra OS makes the abstract concrete: open architecture, vendor-agnostic, built for scientific intelligence from the ground up. Lots of energy on the floor. Last day is today. If you're here, come by. Booth 315.

Most labs still run on manual handoffs. Scientists design experiments, prep samples, run instruments, then pull data out one system at a time to make sense of it. That's not a workflow. It's a bottleneck with a lab coat on. Our AI-augmented Antibody Selection app shows what happens when the data flows freely and with all the necessary context. Instrument to analysis to decision. No exports, no spreadsheet cleanup. Here's a video demo showing how it would work in concert with autonomous instruments and agentic workflows from our partners at Thermo Fisher Scientific We're at BioIT World (Booth 315) all week if you want to see it running.

Join us May 19–21 in Boston, MA as leaders across life sciences, technology, and biomedical research come together to explore the future of innovation. Visit the Tetra Science booth #315 to see how Thermo Fisher Scientific is helping bring AI and autonomous instruments into the laboratory. Today, many laboratory workflows still rely heavily on manual processes — from experiment design and instrument setup to sample preparation, instrument runs, and data interpretation. We’re working to modernize these workflows by connecting instruments, laboratories, software, and data with advanced AI capabilities that help scientists design, perform, and analyze experiments more efficiently. At the booth, you’ll learn about advancements in: 🔹 Connected laboratory workflows Connecting instruments, infrastructure, software, and data to reduce manual steps and improve visibility from experiment setup through analysis. 🔹 Instrument control and automation Enabling more autonomous workflows across experiment design, instrument setup, sample preparation, instrument runs, and result interpretation. 🔹 Turning instrument output into actionable insights Helping scientists access, analyze, and act on experimental data through connected systems and downstream workflows. We look forward to connecting with you at Bio-IT World, where data and technology are transformed into measurable value and real-world impact. #BioITWorld #LifeSciences #ArtificialIntelligence #LaboratoryAutomation #DigitalTransformation #ThermoFisherColleague #BiomedicalResearch

Congrats to Lily Lyman of Underscore VC on making Business Insider's Seed 40 list of the best women early-stage investors of 2026. Lily's on our board at TetraScience, and we're glad she's in our corner. BI calls her one of the Boston investors founders most want in their corner, someone who helps portfolio companies do the work of recruiting executives and raising later rounds, not just writing checks. Fans have even started calling Lily "the Kevin Bacon of tech" for being six degrees from everyone in the industry. Well-deserved, Lily! https://lnkd.in/eAHYpS6e

The great bottleneck in biopharma AI is not compute power. Nor is it model sophistication. It's the gap between scientists and software engineers, and it's costing the industry years. Scientists understand the biology, the assays, the context, and the experimental nuance that makes data meaningful. Engineers understand the pipelines, the schemas, the architecture, and the infrastructure that makes data scalable. In most organizations, these two worlds rarely meet. When they do, translation is painful and slow. We built a new kind of professional to bridge that gap: the Sciborg. Sciborgs are scientist-engineer hybrids — people who speak both languages fluently. They translate scientific requirements into production-ready AI applications. They understand why a chromatography peak matters and how to encode that knowledge into a reusable ontology. They sit at the intersection of domain expertise and data engineering, and they accelerate everything they touch. The results are measurable. In biologics discovery, Sciborg-deployed AI models cut binding experiment time from 48 hours to 30 minutes — with 94% prediction accuracy vs. a 50% industry standard. In cell line development, Sciborg-deployed workflows compressed timelines from 6–8 months to 2.5 months, enabling 10x improvements in manufacturing titer. In preclinical data review, they drove an 80% reduction in study review time and a 10–20% acceleration in IND readiness. These results came out of embedded, outcome-accountable partnerships between TetraScience and our customers, measured by cycle-time compression and scientific productivity, not hours billed. Is the handoff between your science and IT teams the hidden drag on your AI programs? #Sciborgs #LifeSciences #Biopharma #DataScience

Most Scientific AI initiatives fail at the data layer. The models are ready. The use cases are clear. But the data foundation isn't there—fragmented across instruments, software, missing context, ungoverned. There has to be a better way. The week after next at Future Labs Live Basel (May 27-28), Merck Group and TetraScience are presenting Day 1 on a new operating model that's driving real scientific data and AI outcomes. See how a global biopharma organization is unifying lab data, automating contextualization, and building the semantic infrastructure AI needs to work in production. If you're going to the show, please do catch the session. If not, feel free to schedule a technical session with us: https://lnkd.in/eaS332TA

In most antibody screening workflows, you run binding assays, wait for results, manually review them, then move to developability testing. Then polyspecificity. Each step isolated. Each requiring manual triage. By the time you've consolidated everything, weeks have passed. At Bio-IT World next week we're showing what happens when those assays run in parallel and feed into one unified view. Watch binding, developability, and specificity data flow automatically into a single ranked candidate list that keeps scientists in full decision control. This is the Antibody Selection Data App we're demonstrating live. Built on Tetra OS with Thermo Fisher Scientific autonomous instruments. Book some time to go through the demo with us: https://lnkd.in/eVhYwhZv

Do you have an unshakeable passion for solving one of the foundational hurdles in life sciences research today? Prakash Iyer is looking for you. Please reach out 👇