Turning Scientific Tinkering into Scalable Biotech Businesses

The Bridge Between Lab and Market

Biotech innovation often begins with a scientist tinkering at the lab bench, experimenting with a new DNA analysis method, a novel compound, or a breakthrough therapy idea. But turning that scientific tinkering into a scalable business is a challenging journey. In a recent "talk is biotech!" podcast episode, Guru Singh, Founder & CEO of Scispot, spoke with Ivan Liachko, Founder & CEO of Phase Genomics, about exactly this challenge. Scispot, known for offering an AI-driven "tech stack" for life science labs, provides digital infrastructure to help labs scale their scientific operations.

Phase Genomics specializes in genome assembly and analysis using innovative proximity ligation techniques. The insights below draw from Guru and Ivan's conversation and expand with industry best practices and case studies. They shed light on how to bridge the gap between pure scientific exploration and building a commercial biotech venture, emphasizing the need for scalability, repeatability, and standardization in turning lab discoveries into real-world products.

From Lab Bench to Market: Bridging Two Worlds

Science vs. Business: Different Mindsets

Academic science prizes curiosity and novelty, whereas a biotech business demands focus, customer value, and repeatability. Liachko spent 20 years as a researcher before leaping into entrepreneurship, without a traditional business background. He notes that in academia, scientists are incentivized to "invent something one time and then go on and do something else," whereas in business success comes from refining one idea and delivering what other people need. In other words, a clever lab result isn't enough; it must solve a real problem in a reliable way. Guru Singh and Ivan Liachko both stress that making this mental shift is crucial. A useful metaphor is comparing a lab discovery to a concept car prototype: it's a brilliant one-off creation, but turning it into a commercial product is like re-engineering that prototype for mass production with consistent performance, safety standards, and an assembly line to build it at scale. The scientist-turned-founder must start thinking about scalability and usability rather than just the thrill of discovery.

"If we kept it in academia, nobody would see the light of day," Ivan said about his team's DNA technology. "We have to turn this into a commercial product. Otherwise it's basically a crime against science." This sentiment captures why many scientists feel compelled to commercialize breakthroughs: only through a company can a lab invention reach patients, customers, or industry at large. Pure research might yield a great paper, but a business can turn that innovation into an accessible solution. Biotech history provides similar examples; consider how Genentech was born in 1976 when venture capitalist Robert Swanson convinced scientist Herbert Boyer that recombinant DNA technology could create products to help society. Their 10-minute meeting famously stretched to three hours, and by the end Genentech was formed, despite heavy skepticism from both academics and industry peers. This blend of scientific insight with entrepreneurial drive became a template for biotech startups.

The "Valley of Death": From Idea to Proof

Yet, bridging science and business is often called "crossing the valley of death." Promising lab discoveries can languish without the funding, validation, or practical development needed to become products. One big issue is reproducibility. In academia, an experiment might be done a few times under ideal conditions to publish a paper. In industry, that result needs to be reproducible hundreds of times, by different people, and preferably faster and cheaper. It's sobering that many flashy findings don't replicate robustly. For instance, pharmaceutical scientists at Amgen tried to reproduce 53 landmark cancer studies and 47 of them could not be replicated. "These are the studies the industry relies on to identify new drug targets. If you're going to place a $5 million bet on an observation, you need to be sure it's true," wrote one researcher, underscoring that you "can't take anything at face value" in early research. Biotech startups must therefore rigorously validate their science. Before scaling up, successful founders often invest significant time in de-risking the technology, repeating experiments, troubleshooting inconsistencies, and sometimes redesigning the method so it works reliably. This push for repeatability is not just scientific rigor; it's a business necessity when investors and customers are on the line.

Scaling Up the Science: Technical Foundations for Repeatability

Scalability and Standardization

A hallmark of a biotech business is that the science has been engineered into a repeatable process or product. In the lab, a brilliant result might hinge on one expert's hands-on technique. In a company, that result needs to be reproducible by a junior technician or a machine on a production line. This requires standardizing protocols and embracing automation. Ivan Liachko's experience turning a genomics discovery into a product illustrates this well. His company Phase Genomics took a cutting-edge DNA sequencing method (Hi-C proximity analysis) from an academic setting and made it a commercial platform. They had to develop kits and software so that other labs (customers) could get the same genomic insights without needing Ivan's personal supervision. In the podcast, Ivan uses a simple analogy to explain their technology's impact: traditional genome sequencing is like shredding a house's blueprints into pieces; technically all the info is there, but you can't assemble it properly. The Hi-C approach is like having clues for which pieces belong together, so you can reconstruct the entire blueprint.

Achieving this consistency often means implementing Standard Operating Procedures (SOPs) and quality controls early. Biotech startups transitioning from "project" to "product" phase often adopt principles from manufacturing. Techniques like Design of Experiments (DOE) can systematically optimize processes to work robustly under different conditions, rather than only under one scientist's perfect conditions. Many startups also preemptively consider regulatory standards (for example, Good Manufacturing Practice (GMP) in pharma or diagnostics) even when in R&D, because these standards enforce the reproducibility and documentation needed for scale. In diagnostics or therapeutics, experimental results must hold up under regulatory scrutiny and in diverse real-world scenarios, a far higher bar than a one-time publication.

Digital Infrastructure

Modern biotech companies are increasingly leaning on digital tools to aid repeatability. Lab data management systems and automation can remove human error and variability. Guru Singh's company Scispot, for instance, provides an AI-powered Lab Operating System that helps standardize how data and workflows are handled in the lab. Such platforms ensure that experimental data, protocols, and inventory are all tracked in a consistent way. This kind of unified data environment makes it easier to scale, because everyone is following the same playbook. "Create consistent data structures across experiments, teams, and operations to support scalable workflows," advises one Scispot case study on lab automation. Consistency in data and method not only speeds up research, it also makes automation and AI tools more effective. In fact, AI-driven lab platforms can dramatically improve reliability, boosting data quality three-fold and cutting down manual tasks by 70% according to industry reports. This means experiments are more likely to yield the same results every time, and scientists are freed from tedious manual steps, focusing instead on innovation. Adopting such infrastructure early is a best practice: it's easier to build a scalable process from the start than to retrofit standardization into a chaotic, ad-hoc workflow later.

Case Study: From Service to Product

An example of scaling technical operations comes from Phase Genomics' early strategy. Their first paying work was not selling a product off the shelf, but providing a service, essentially doing genomics analysis for a client (Driscoll's, the berry company) using their unique methods. This approach allowed them to bootstrap: they earned revenue and refined their techniques in a real-world context. "They wanted to do a bunch of genomes with us... 'can you do this for money?' And so they became one of our first customers," Ivan recalls. By treating their process as a service, they could ensure it worked repeatably for an external partner. That experience then fed into the development of a product (kits and software) that could be sold at scale. This is a common path for biotech startups: start with consulting or contract research using the novel technology, prove its value and reliability, and use that foundation to design a more standardized product. Once the packaged product was ready, Phase Genomics could reach many more customers without having to do all the work themselves, effectively scaling the impact of their innovation. The key was that those early service projects forced them to make the science work under different conditions and with someone paying for results, which is the perfect pressure-test for scalability.

Operational Shifts: Building a Biotech Business Engine

Humble Beginnings and Iteration

It's easy to imagine biotech startups in sleek labs with futuristic equipment. The reality in the early days is often far more scrappy. "A lot of people think of startups as high-tech things with shiny labs, but really it's more like a garage full of crap," Ivan jokes about Phase Genomics' start. His first lab space was literally a closet-sized room for two people. "The desk was too low to be a lab bench, so I bought bricks at Home Depot and put them under the desk," he recalls. This vivid image shows an important truth: successful biotech founders start lean and focus on function over form. In the beginning, it's about getting the science to work with minimal resources and improvising solutions, much like the archetypal tech startup in a garage. This lean mentality is actually an operational advantage. It forces the team to prioritize what matters most (the core experiments, securing the first customer) and not waste money on fancy but unnecessary setups. Many biotech startups begin in incubators or shared lab spaces for this reason, reducing upfront costs. The "garage mindset" also encourages a culture of problem-solving and adaptability, traits that remain valuable even as the company grows.

The First Customer: Gaining Credibility

One of the biggest operational hurdles is convincing others to believe in your technology before you have a proven track record. Guru Singh and Ivan Liachko highlight the importance of landing that initial customer or partner to gain credibility. Early adopters are often taking a risk on an unproven startup, so founders must invest considerable effort to secure them. "You need someone to start the process... to bet on you in a way early on before you have credibility," Ivan emphasizes. For Phase Genomics, the first customer in agriculture gave them validation that opened doors to others. This creates a snowball effect: after a couple of successful projects, it becomes much easier to persuade new customers or investors, because you have references and case studies to point to. The lesson for scientists-turned-entrepreneurs is to actively seek out these collaborations or pilot projects. Often, this means adapting your offering to early customers' needs, maybe performing a custom analysis or co-developing a solution, rather than insisting on a fully formed product from day one. It also means being willing to deliver value as a service when needed, as described earlier. Operationally, founders should prioritize a few pilot projects or contracts that demonstrate their concept in a real use-case. These early wins are more valuable than any press release; they validate that the science works outside the original lab and that someone is willing to pay for it.

Building for Growth

As the company gains traction, operations must scale beyond the founders' direct lab work. This involves hiring and delegation, establishing processes for routine tasks, and often, implementing software systems to manage increasing complexity. Many biotech startups reach a point where they transition from an informal "all-hands-on-deck" mode to a more structured organization. This is where adopting tools like electronic lab notebooks, inventory management systems, and project management frameworks becomes crucial. For example, tracking reagent lots and experiment results in spreadsheets might suffice for two people, but not for a team of 20. Companies that scale smoothly tend to institutionalize knowledge early; they document protocols, record experiment data in shared databases, and set up standard inventory processes. By doing so, they reduce the risk that growth will lead to chaos (lost data, inconsistent practices, new hires repeating old mistakes). As an added benefit, these systems make the startup "audit-ready" for eventual regulatory reviews or partnerships. In the long run, a biotech company is not just advancing science; it's also building a production and delivery capability. Whether the product is a physical kit, a diagnostic test, or a therapy, scaling up means ensuring supply chains for materials, quality testing of outputs, and customer support are in place. Founders often bring in or consult with operations experts at this stage, people experienced in biotech manufacturing, clinical trial management, or quality assurance, to fortify the operational backbone of the company. In summary, moving from a project to a product business requires developing an engine that reliably delivers your science to customers. It's a shift from improvising in a makeshift lab to running a well-oiled operation that can grow exponentially.

Cultural Transformation: From Scientist to Entrepreneur

Team and Talent

A common mantra in biotech is that "it's a team sport." No single person, no matter how brilliant a scientist, can handle all aspects of scaling a biotech venture. Ivan Liachko attributes much of Phase Genomics' success to finding the right co-founder and team early on. He partnered with a software engineer (who he originally knew from a Dungeons & Dragons gaming group!) to complement his wet-lab expertise. "I was the wet lab side... he was the software side. We split all the other tasks," Ivan says, illustrating how complementary skills are essential. This pattern repeats across the industry: you'll often see a biotech founding team that includes, say, a PhD scientist alongside an MBA or an engineer. Each brings a piece of the puzzle, scientific depth, technical development, business strategy, operations know-how. As the company grows, hiring people who bridge worlds (like scientists who understand marketing, or businesspeople familiar with biotech regulations) becomes crucial. Building a culture that values cross-disciplinary collaboration is key. Everyone needs to appreciate each other's domain: the researcher must value the importance of sales, the salesperson must understand the product's scientific nuances, and so on. Successful biotech companies often foster a learning culture where the science team and business team educate each other, aligning goals around the product and mission.

Changing Incentives and Attitudes

One of the hardest shifts for scientist-founders is embracing areas that academia traditionally undervalues: marketing, sales, and customer feedback. "For the first several years, I was allergic to sales and marketing, it felt wrong. If this [technology] is good, it should be obvious, people should just get it," Ivan admits of his early mindset. This sentiment is common among researchers starting companies. In academia, overt "selling" of your work is rare; in business, you must actively persuade others, whether investors, partners, or customers, of your solution's value. The cultural shift involves recognizing that a great product alone is not enough. Communication, branding, and customer engagement are not dirty words; they are how your innovation makes impact in the real world. Scientists often have to unlearn the idea that merits will speak for themselves and learn the art of storytelling and value proposition. One way to ease this transition is to reframe outreach as education; many scientist-founders shine when they treat marketing as teaching the audience about the science and its benefits, rather than "selling" in a shallow sense.

Along with embracing the commercial side, the company culture must shift toward customer-centric thinking. In research, projects are driven by the scientist's curiosity. In a business, R&D is driven by customer needs and market gaps. Guru Singh's perspective as both a scientist and a tech CEO reinforces this: labs that transition successfully to businesses tend to obsess over solving a specific problem that customers face, not just pursuing science for science's sake. This may mean narrowing focus. A lab might explore ten different avenues; a startup must choose one or two that it can execute well and deliver consistently. The team must regularly ask, "How does this experiment or feature make life better for our target customer or end-user?" and be willing to cut projects that don't align. Adopting methodologies from the startup world, like customer discovery interviews and iterative product design, can inform scientists whether their "cool idea" truly addresses a pain point. Many biotech founders wish they had engaged with end-users earlier to guide their development, something Ivan Liachko and other guests on the "talk is biotech!" podcast series have noted.

Resilience and Adaptability

Culturally, moving from academia into entrepreneurship also demands resilience and comfort with failure of a different sort. In academia, a failed experiment might delay a paper; in a startup, a failed project could mean running out of cash. The stakes and stress are often higher. Entrepreneurs quickly learn to pivot; if data doesn't support the hypothesis, maybe the product needs tweaking or the target market changes. This adaptability, combined with a refusal to quit, is frequently what separates successful biotech ventures from those that fall into the "valley of death." In the conversation, Ivan half-jokingly described "entrepreneur" as a kind of "mental disorder that makes you have to do this and you can't stop." The drive to push on despite obstacles is indeed a hallmark of company builders. However, it's not madness so much as passion coupled with flexibility. Great biotech entrepreneurs maintain belief in their mission, for example, using genomics to cure diseases or improve agriculture, but remain flexible about the path to get there. They will alter the product, business model, or science approach as needed to achieve the mission. Culturally, instilling that sense of mission in the team helps everyone weather the ups and downs. Unlike an academic project that might dissolve after a publication, a company's mission can inspire people to solve hard problems and persist for years. Many founders say that embracing a mission larger than one's own research agenda was a motivating shift; it's not just about publishing papers, but potentially saving lives or creating value at scale, which can be profoundly energizing.