Customize SciSure to work your way

Lab operations is a job of countless moving parts. You’re the one who gets the 6 a.m. message about a broken freezer. The one answering late emails about missing reagents. The one tracking down overdue chemical inventory reports. It’s you that’s stuck in the middle—trying to keep scientists on-track, keep EHS happy, and still hit the goals set from administration.

And here’s the truth: your job isn’t one job. It’s twenty. You’re part scientist, part problem solver, part safety officer, equipment coordinator, procurement specialist, compliance tracker—and yes, firefighter. Every day. All the time.

I’ve talked with enough LabOps leaders to know that “multitasking” doesn’t even begin to cover it. One of the first questions I ask when I meet someone in LabOps is: How many hats do you wear? And without fail, they just laugh—or roll their eyes. Because they already know the answer: Too many.

One person recently told me, “If I could just take off one hat—just one—I’d be so much more valuable to my scientists.” That stuck with me. Because it speaks to a deeper truth about your role: you’re the operational engine behind the lab, the one keeping the science moving, the one making sure research doesn’t stall. And yet? You’re often unable to get to the work that moves the needle. 

You’re expected to be the glue—to bind together scientists, EHS, vendors, facilities, IT, and more—but no one’s provided you the tools to actually hold it all together. That’s not your fault. That’s the system’s fault.

We’ve normalized the dysfunction

Most labs I walk into are juggling an eye-watering number of systems: There’s an app for inspections. A training matrix spreadsheet. A cloud drive for ordering. A digital binder for SDS sheets that hasn’t been updated in months. Someone’s still logging chemicals in an Excel sheet. And yet—everyone’s expected to operate like a high-performance team.

Scientists are in their own zone, focused on getting experiments done. EHS is off in theirs, trying to ensure safety and regulatory coverage without full visibility into the data they need to be audit ready. LabOps are often the one caught in the middle, expected to make all of it work. We’ve been sold the idea that this fragmented ecosystem is normal. That cobbling together ten tools and calling it “digital transformation” is just the cost of doing science.

But it’s not normal. It’s just the status quo—and it’s broken.

We don’t need more complexity. We need operational simplicity.

In biopharma, there’s a buzzword that’s been floating around for years: “operational excellence”. You see it in slide decks. Hear it in strategy meetings. Entire departments are built around chasing it.

But I’ll be honest—when I see the typical roadmap for getting there, my eyes glaze over. Not because it isn’t well-intentioned. But because it’s built for a world where you’ve got unlimited time, a dozen project managers, and years to transform your systems. 

That’s not the world LabOps lives in. Most labs don’t need “excellence”, they need something that works today. That’s why, at SciSure, we’re championing something new: operational simplicity. And we’ve created the first platform that actually delivers it—end to end. We’re not here to add another tool to the pile. We’re here to replace the pile with something built from the ground up for the people who actually use it.

The Scientific Management Platform (SMP) is a first-of-its-kind system that unites scientists, LabOps, and EHS under one roof. Not as users of the same tool, but as collaborators inside a shared ecosystem. Forged from a strategic merger between two trusted leaders—eLabNext, known for its ELN and LIMS software, and SciShield, respected for EHS, biosafety, and chemical safety solutions—we’re working hard to deliver something no one else had the conviction to do:

A true end-to-end solution. A home base for the entire scientific organization.

• One login. One interface. One system that links research, inventory, safety, compliance, and regulatory reporting.
• Intuitive dashboards for scientists, EHS, and LabOps—tailored to what they need.
• Built-in safety and compliance workflows—no chasing, no bolt-ons, no “oops, we forgot to do the training”.
• A modular design that lets you adopt what you need today—and expand when you're ready.

With the SMP, you're not logging in and out of 10 tools. You're not re-entering data or transcribing between systems that can’t talk to each other. You’re not cobbling together workarounds just to make your lab function. You're simply working. Confidently. Transparently. Collaboratively.

And that’s the power of operational simplicity: it gives you control. Control over your workflows. Control over your data. Control over your time. In a world that’s constantly changing—funding shifts, regulatory updates, staffing turnover—you can’t control the chaos. But you can control the controllables. That’s what SciSure is here to help you do.

Our goal at SciSure isn’t to force everyone into the same workflow. It’s to connect their silos in a way that makes sense—so that LabOps doesn’t have to be the translator, or the buffer, or the one who copies data from one system to another.

Scientists want to work in their environment. EHS needs reliable data. LabOps needs visibility across both. We’re building SciSure to make that possible—to let everyone do their job well, while ensuring they’re connected in the background where it counts.

From chasing problems to preventing them

I’ve heard it time and time again: LabOps lives in reactive mode. You’re running from one task to the next, never quite catching up, always reacting. There’s no space to think ahead—let alone optimize, strategize, or innovate. But when you give LabOps the right tools, everything changes.

Suddenly, you’ve got real-time dashboards that tell you what’s overdue, what’s at risk, and what’s just around the corner. You get automatic reminders for training, inspections, and compliance updates—not frantic emails the day before something’s due. Instead of cross-checking three systems to figure out who’s behind on chemical inventory, you can see the status in a single view. You stop wondering if your data is current. You know it is.

Reporting becomes fast and confident. MAQs, CFATS, flammables—the kind of documentation that used to take hours now takes minutes, because it’s all built in. No more guesswork. No more copy-paste. And with threshold-based alerts tied to real hazard data, you're not just catching problems. You're preventing them.

But here’s the part I love the most—it changes how you think. Instead of dreading the next audit, you’re ready for it. Instead of catching issues downstream, you’re preventing them upstream. Instead of getting pulled in every direction, you’re setting the direction. That’s the shift we’re creating with SciSure. And it’s not hypothetical—it’s happening right now in labs just like yours.

Time to rethink chemical inventory

Ask anyone in LabOps what drains the most time, and chemical inventory will be near the top. Not because it’s difficult—but because it’s disconnected. Updates get lost. SDSs live in silos. Reports are always a scramble. We’ve built SciSure to turn that burden into a strategic lever.

With SciSure, chemical inventory tasks that used to take hours now take minutes. As soon as a chemical is added to inventory, you know which hazards apply and what regulations are relevant, even going as far as automatically adding it to your regulatory reports. One team we worked with used to spend nearly three full days reconciling chemical inventory across their site. After switching to SciSure, they were done in thirty minutes. Another cut data correction time from seventeen hours per month down to under two minutes. When you scale these time savings across dozens of people, across hundreds of tasks, those minutes and costs add up fast.

We recently looked at data from 32 customer sites to understand the real impact of transforming chemical inventory workflows. Here’s a snapshot of what LabOps and EHS teams are actually gaining back:

Table 1: Time savings reported across 32 SciSure customer sites

But the real value isn’t just speed. It’s confidence. When inventory connects to hazard profiles, to training requirements, to reporting and procurement—you’re not just checking boxes. You’re running a safer, more accountable lab. And you’re giving scientists and EHS teams shared data they can trust.

Flexibility and extendibility built around you. 

SciSure doesn’t pretend to be everything for everyone. We know labs are complex. We know preferences vary. That’s why we built our SciSure Marketplace of integrations to be open and extensible.

Let’s say you already have a scheduling tool your scientists love. Great. Don’t toss it—integrate it. Or maybe your team uses a specific vendor for procurement or equipment monitoring. Perfect—bring them in.

Our Marketplace is designed to let you work the way you want. It’s an open ecosystem where trusted third parties—including some of our own competitors—can plug directly into the SciSure platform. Why? Because we believe that as a scientific community, we are “better together”. Because if it makes your workflow smoother, if it saves your team time, and if it helps scientists get back to the work that matters, then we’ll make room. That’s how serious we are about interoperability.

As one of our Marketplace Partners put it:

“Our integration with SciSure allows scientists, LabOps, and EHS to view critical data in one seamless place. It means labs can proactively monitor environmental conditions and respond instantly to deviations that could compromise valuable samples.”
- Sridhar Iyengar, Founder and CSTO, Elemental Machines

Some of our customers have even started building their own integrations. One group created a fully custom equipment scheduler inside SciSure using our developer tools. No vendor lock-in. No workarounds. Just functionality that fits the way they operate.

That’s what flexibility should look like. Because you’re not here to serve the system. The system should serve you.

Let’s stop fixing what’s broken. Let’s build what works.

The truth is, LabOps shouldn’t have to hustle this hard just to hold the lab together. Scientists shouldn’t be buried in admin. EHS shouldn’t have to chase compliance.

And you? You shouldn’t be the translator between teams and tools that were never built to align in the first place.

That’s why we created SciSure. Not as a patch, but as a true home base. A unified foundation for modern labs—one that connects your people, your processes, and your purpose. Because the science matters. And so do the people and patients behind it.

Let’s make it easier to do the work that matters most. Let’s give LabOps their time, their tools, and their voice back. You’re not the problem. The system is.

Let’s fix it--together.

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Cambridge, MA — July 10, 2025 — SciSure, formerly SciShield and eLabNext, is proud to announce the formal sponsorship of The Engine, a nonprofit founded by MIT to  incubate and accelerate early-stage Tough Tech companies from idea to impact. This sponsorship marks the next step in a partnership rooted in a shared mission: helping startups accelerate the development of life-changing technologies, treatments, and healthcare solutions.

SciSure was recently formed from the merger of SciShield and eLabNext. Both companies were longtime partners of The Engine: For years, SciShield supported The Engine with environmental health and safety (EHS) and compliance infrastructure, while eLabNext powered the digital backbone of many resident companies. Now, by joining forces, SciShield and eLabNext are expanding their support—giving Tough Tech entrepreneurs connected access to the critical digital tools, compliance frameworks, and operational resources they need to scale and succeed.

"We’ve seen firsthand how important the right infrastructure is for Tough Tech startups to reach the market," said Jon Zibell, VP of Global Alliances and Marketing for SciSure "By deepening our partnership with The Engine, we’re making it easier for innovators to focus on what matters most—delivering breakthroughs that have a lasting impact on society."

“Given the fact that science is moving more and more towards Tough Tech and Tech Bio (as in technology-first, using advanced computational tools like AI, ML, cloud, and data engineering), it is so important to be able to create an API/SDK-Powered ecosystem for scientists that know that we can support their platforms, automation, and scalability.” said Zareh Zurabyan, VP of Commercial, Americas for SciSure. 

Through this enhanced partnership, The Engine’s resident companies will gain:

• Integrated Digital Operations: Access to eLabNext’s Digital Lab Platform (DLP), combining ELN, LIMS, lab automation, and compliance tracking into one connected system.
• Built-in EHS and Compliance Support: SciShield’s platform ensures startups meet regulatory standards from day one, reducing risk and saving time.
• Early Access Incentives: Special access to tailored onboarding, incentives, and ongoing support for adopting SciShield and eLabNext solutions. 
• Best-in-Class Software: A full suite of software solutions for Scientists, LabOps, and EHS to ensure the highest probability of achieving commercial success and making a lasting impact.
• Education and Collaboration: New opportunities for startups to participate in co-hosted workshops, hands-on training, and knowledge-sharing sessions on lab digitalization, safety, and compliance.

“For years, SciSchield and eLabNext have provided critical infrastructure and resources to fuel the growth of our residents,” said Alexa Monti, Vice President of Business Development and Strategic Partnerships at The Engine. “We are now thrilled to welcome their sponsorship as SciSure. Partnerships like this are essential to building a resilient innovation ecosystem that enables founders to transform breakthrough ideas into world-changing impact.”

About The Engine:

The Engine is a nonprofit incubator and accelerator dedicated to supporting early-stage Tough Tech companies tackling the world's greatest challenges. Founded by MIT in 2016, The Engine provides critical support for Tough Tech companies in the form of specialized lab and fabrication infrastructure, programming and mentorship, and an ecosystem of experts and investors. By bridging the gap between groundbreaking ideas and real-world impact, The Engine plays a crucial role in advancing solutions to complex global issues. For more information, visit engine.xyz

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Media Contact: 

press@engine.xyz

Media Contact:

Jon Zibell
Vice President of Global Alliances & Marketing
‍J.Zibell@scisure.com

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Over the past decade, the life sciences industry has undergone a foundational transformation, one that redefines how biology is explored, understood, and scaled. 

Traditional, wet-lab-driven biotechnology has shifted toward TechBio, a new model grounded in software engineering, data architecture, and AI-native platforms. This transition replaces linear, hypothesis-driven experimentation with computationally designed, automated, and feedback-driven discovery systems.

Whereas classical biotech workflows revolved around physical assays and post-hoc data interpretation, TechBio organizations prioritize upstream data infrastructure, machine learning (ML)-ready outputs, and modular technology stacks from the outset. 

Bioinformaticians have transitioned from support roles to strategic leadership, while wet lab execution has become programmable. Digital platforms, such as ELN/LIMS ecosystems, have become mission-critical infrastructure. Interdisciplinary teams spanning biology, data science, and software engineering collaborate in product-oriented models, similar to those found in SaaS companies. AI tools also augment decision-making at every stage, from target identification to manufacturing.

This guide outlines what TechBio is and ten defining pillars, providing detailed examples. It explores how TechBio organizations engineer not only therapies, but the digital systems that discover, refine, and validate them. 

As the boundary between biology and computation dissolves, TechBio positions itself as the operating system of 21st-century life sciences, offering a blueprint for faster, more scalable, and reproducible scientific innovation.

Welcome to TechBio, where biology meets software engineering, and the future is being coded before it's cultured.

What is TechBio?

TechBio is the convergence of biology, software engineering, and AI, replacing traditional wet-lab workflows with computationally driven, programmable discovery systems. It reimagines life sciences as a scalable, data-centric platform where interdisciplinary teams and digital infrastructure power faster, more reproducible innovation.

10 Pillars Defining the TechBio Transition

1. Data Architecture Before Wet Work

Traditional biotech began with the bench: Run experiments, generate data, and interpret the results later. 

In TechBio, the inverse is true. Teams now design the data schema, ontology, and analysis pipeline first, enabling smart experiment design, ML-ready outputs, and scalable platforms that can adapt over time.

If your data isn't structured for insight on Day 1, you're already behind. Here are some examples:

• Companies like Recursion Pharmaceuticals use imaging and structured data pipelines before any therapeutic hypothesis is even proposed.
• GLP-1 analog research is now modeled using pre-trained metabolic models that simulate downstream effects before synthesis.
• New CRISPR libraries are generated and screened in silico with predictive scoring models, optimizing guide RNAs based on large omics datasets.

This inversion – starting with data design before experimentation – has reoriented R&D pipelines around long-term scalability. As structured data becomes a strategic asset, TechBio companies are increasingly valued not just for their scientific breakthroughs but for the reusability of their data layers. This has profound implications for platform business models, partnerships, and cross-study insights.

2. AI-First vs. Hypothesis-First

Biotech works in a sequential logic: form a hypothesis, test it in an in vitro model, and iterate. 

TechBio builds AI-native systems that surface insights and correlations before human hypotheses even form, accelerating discovery.

The AI isn't replacing the scientist; it's augmenting their intuition at scale. Examples include:

• Insitro and Inceptive are generating drug candidates with ML from genetic/phenotypic data, especially in diseases like ALS and obesity.
• CRISPR screening now uses AI to predict essential gene targets before experiments, significantly reducing the time-to-lead.

The shift from hypothesis-driven to AI-augmented discovery marks a turning point in biological research. Rather than replacing scientists, AI now operates as a collaborative engine that surfaces new dimensions of correlation and causation. The competitive edge is shifting toward organizations that can orchestrate this human–machine loop efficiently, striking a balance between statistical signals and biological plausibility.

3. Platform Engineering as a Core Competency

In TechBio, companies aren’t just developing drugs; they're developing software platforms that standardize workflows, integrate third-party tools, and turn fragmented research into reproducible systems. 

Internal data platforms, LIMS/ELN integrations, and ML pipelines are essential baseline technologies for competitiveness. Some real-world examples include:

• Ginkgo Bioworks builds reusable cell engineering platforms akin to APIs for cells.
• Arda Therapeutics has created a modular inflammation discovery stack from single-cell data, standardizing the computational exploration of immune pathways and cells.

The rise of internal engineering teams and reusable software platforms in the life sciences mirrors the evolution of the tech industry. Platformization allows TechBio companies to rapidly launch programs across therapeutic areas, onboard partners, and generate real-time feedback loops. The result is a higher innovation velocity and better capital efficiency, traits that investors and pharmaceutical partners increasingly favor.

4. Bioinformaticians Are the New Bench Scientists

In a TechBio org, the bioinformatician is no longer “behind the scenes;” they're core to strategy, productization, and decision-making. Teams prioritize hires who can extract signal from noise, build predictive models, and interface with both biologists and back-end engineers. Bioinformaticians are now tasked with defining the experiment design, not just analyzing results after the fact.
As the bottleneck in modern biology moves from experimentation to interpretation, bioinformaticians have emerged as essential architects of discovery. Organizations that empower computational biology as a front-line discipline – not a downstream service – are demonstrating faster time-to-insight, better target validation, and smarter trial design. Talent acquisition in this field is now a core strategic priority.

5. Composable Lab Tech Stacks

Gone are the days of rigid, siloed lab systems. TechBio demands modular, API-connected ecosystems that allow seamless integration between ELN, LIMS, data lakes, assay instruments, and cloud analysis tools. 

Composability – the ability to select, assemble, and reconfigure components, such as services, modules, or APIs – is the new competitive advantage.

Top TechBio orgs are building integrated ecosystems where ELN, LIMS, and assay data sync in real-time, reducing batch errors and improving reproducibility. Composable architecture transforms labs from siloed environments into interoperable, cloud-connected ecosystems. 

This flexibility enables rapid tool swapping, real-time data syncing, and scalable digital operations. As composability becomes a prerequisite, the market is shifting toward vendors and platforms that emphasize integration, standardization, and cross-domain orchestration.

6. Experimental Automation as Software

Wet lab automation has evolved beyond the use of robotic arms. Now it’s programmable. TechBio teams treat lab execution as code: Experiments are version-controlled and modularized, making them reproducible across different geographical locations. Strateos and Emerald Cloud Lab are commercial examples of how this can work, letting scientists run remote assays, QC, and sample processing with code.
By treating lab execution as programmable infrastructure, TechBio closes the loop between in silico design and in vitro execution. Automation not only accelerates throughput but also unlocks a new paradigm of version-controlled science, where reproducibility and traceability become codified. The winners in this space will be those who can abstract biology into code without sacrificing fidelity.

7. Interdisciplinary Product Teams

TechBio orgs are structured like SaaS companies. Product managers, software engineers, data scientists, and bench biologists all contribute to the strategic path for products. Product-market fit isn’t just about efficacy; it’s about workflow usability, data interoperability, and analytical scalability. 

Dyno Therapeutics, a company using AI to discover and optimize better delivery of gene therapies, employs product managers and ML leads alongside virologists to design AAV capsid platforms with specific tropisms.
The productization of science, where multi-disciplinary teams own features, roadmaps, and outcomes, blurs the lines between R&D and product development. TechBio teams now operate like agile startups, iterating on therapeutic designs with the same velocity and feedback mechanisms as SaaS companies. This accelerates both discovery and market alignment, reducing the translational lag between R&D and impact.

8. Open Science Meets IP-Protected Infrastructure

Rather than hoarding findings in PDFs or publications, TechBio companies publish datasets, APIs, and tools while protecting their insights via proprietary ML models and data platforms. It's not just about the molecule or target; it's about the ecosystem that discovers it.

TechBio is redefining the balance between openness and defensibility. By releasing tools and datasets while protecting the infrastructure that operationalizes them, companies can build communities, accelerate adoption, and establish defensible moats around proprietary layers. This hybrid approach to IP strategy mirrors the open-core model in software and is fast becoming the norm in science-forward organizations.

9. AI-Augmented Decision-Making in R&D

From target identification to trial design, AI is infused across the R&D lifecycle. NLP models extract insights from literature, generative models design protein structures, and predictive models flag risks before they manifest. For example:

• GLP-1 and incretin research is being accelerated by multimodal AI models that predict cardiometabolic response based on genetic and dietary data.
• CRISPR off-target prediction tools, such as DeepCRISPR and CRISPR-Net, minimize risk before editing begins.

From discovery to development to manufacturing, TechBio companies are using predictive models to make faster, more informed decisions. This transition lowers risk, reduces cost, and improves outcomes, positioning AI-augmented pipelines as the gold standard for next-generation therapeutics.

10. Speed, Scale, and Signal

TechBio companies operate on startup timelines, not scientific timelines. They use cloud infrastructure, continuous data streaming, and rapid feedback loops to compress cycle times from months to days. Signal extraction and throughput are the key metrics. What used to take 18 months in a wet lab now happens in 6 weeks via computational modeling and robotic execution.

By adopting cloud infrastructure, continuous experimentation, and agile pipelines, companies can reduce the cycle time from question to answer and from idea to impact. As signal extraction becomes the metric that defines productivity, organizations are now judged by how efficiently they can learn, not just how much they can test.

The Venture Capital (VC) & Private Equity (PE) Outlook: Why TechBio Is the New Investor Mandate

The TechBio transition has fundamentally reshaped investor psychology in life sciences. Where traditional biotech relied on long timelines, binary risk, and molecule-centric valuations, today’s VC and PE firms are seeking software-first, platform-oriented, and AI-native biology companies that exhibit repeatable innovation, scalability, and enterprise value beyond a single therapeutic asset.

Key Investment Trends Driving Capital Deployment in TechBio

The biotech investment landscape is shifting, with VC deployment accelerating in late 2025 and favoring AI-native, TechBio firms modeled after high-growth SaaS companies. Private equity is moving away from traditional biotech roll-ups toward digital-first infrastructure plays, such as LIMS and automation platforms. Valuations are compressing for single-asset biotech firms but expanding for multi-modal platforms with in-house AI/ML capabilities. IPO and exit readiness now require both clinical and tech maturity, while firms lacking digital infrastructure face the greatest funding risk.

With TechBio firmly entrenched, here’s what the not-too-distant future looks like:

• Prioritizing Platform over Pipeline: Investors are favoring companies with data platforms or AI discovery engines that can generate multiple assets, rather than a single-drug pipeline. Look at Flagship and Andreessen Horowitz (a16z) continuing to back repeatable discovery systems, such as Generate Biomedicines and Inceptive, instead of molecule-first approaches.
• Computational Biology at a Premium: Companies with ML-native workflows, structured data ontologies, and in silico design capabilities are commanding higher valuations. Recursion Pharmaceuticals’ IPO and valuation, for example, were tied more to its image-based AI infrastructure than its lead program.
• Cross-Disciplinary Teams as a Signal of Quality: Interdisciplinary founding teams that blend machine learning, systems biology, and engineering are seen as higher-execution risk mitigators. PE firms are increasingly conducting technical due diligence not just on pipelines, but also on the infrastructure stack, data operations, and software engineering.
• Shift Toward B2B and SaaS Models in Life Sciences: A wave of investment is flowing into companies that serve the TechBio ecosystem, including cloud-native LIMS/ELN platforms, computational CROs, and automated lab systems. These provide recurring revenue, faster sales cycles, and infrastructure lock-in, metrics that closely align with the tech sector's investment benchmarks.
• AI as a Defensibility Layer: VCs are heavily weighing proprietary AI models as part of the IP moat. It's no longer enough to own a sequence; firms must own the system that designs or predicts the sequence. Investors now look at data exclusivity, model performance, API extensibility, partner integrations, and model improvement over time.

TechBio: Redefining the Future of the Life Sciences

The life sciences funding environment is undergoing the same disruption that has reshaped fintech, media, and cybersecurity: from asset-centric investing to platform- and systems-centric investing. As biology becomes programmable, investors no longer seek the best drug; they seek the best engine for discovering, designing, and optimizing drugs.

The overarching implications of this include a shift to:

• Hiring more software engineers and ML experts than lab techs
• VCs seeking platform-first models with recurring data assets
• Companies prioritizing cloud-native, ML-enabled workflows.
• Faster, reproducible, and AI-augmented discoveries.

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For companies, this means:

• Building infrastructure before pipelines
• Valuing reproducibility as a product
• Prioritizing software engineers and bioinformaticians as co-founders
• Designing business models around feedback loops, not just endpoints

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The leaders of this next chapter won’t just discover, they’ll design biology as an engineered system, built on platforms, powered by data, and scaled with AI.

While Biotech commercialized biology, TechBio will make biology computational.

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