TeselaGen Biotechnology

Biology is code. Someone had to write the IDE.

Here is a thing that is true and slightly absurd about modern biotechnology: some of the smartest people on Earth, holders of doctorates in molecular biology, spend a meaningful fraction of their working lives doing spreadsheet arithmetic. They are planning experiments - which DNA fragment goes where, in what order, at what concentration - and the tool for this is often a grid of cells and a lot of squinting. It is 2026 and biology, the field that literally decodes life, has historically been run on the software equivalent of a paper napkin.

TeselaGen Biotechnology exists because three scientists found this unbearable. Eduardo Abeliuk was doing a PhD in electrical engineering at Stanford, camped out in the Beckman Center designing biological experiments, when he noticed he was wasting his days on tasks that software could obviously do. Michael Fero and Nathan Hillson - the latter had built genuinely complicated combinatorial DNA libraries at Berkeley Lab - had the same complaint from the wet-lab side. In 2011 they turned the complaint into a company.

The clever part is where the technology came from. TeselaGen's foundational tool, called j5, was born inside the Department of Energy's Joint BioEnergy Institute, a national lab trying to make fuel out of plants. j5 automated the design of combinatorial DNA assembly - the finicky logistics of stitching genetic parts together. TeselaGen commercialized it, which is a nice reminder that a surprising amount of deep-tech starts life as a grant-funded utility that someone eventually decides is a business.

The unifying idea is something biologists call the Design-Build-Test-Learn cycle, or DBTL, which is exactly what it sounds like: you design a genetic construct, you build it in the lab, you test whether it does the thing you wanted, and you learn from the result so your next design is smarter. In principle this is a beautiful loop. In practice, each step traditionally lives in a different tool, a different notebook, a different person's head, and the loop never actually closes. TeselaGen's whole thesis is that the loop should close automatically - that your test data should train your next design without anyone re-typing anything.

Four modules, one loop

The platform is organized, sensibly, into the four beats of that cycle. Each does one unglamorous job well.

Notice what the BUILD module actually produces. It does not write a paper or a dashboard. It writes instructions for a robot - the pick-list a liquid handler follows to move nanoliters of DNA around a plate. This is the unglamorous genius of the thing. The best automation is not the kind that impresses people at a conference. It is the kind that quietly disappears into a machine's workflow, and TeselaGen has spent more than a decade making itself disappear.

More recently the company has leaned into the language of the moment - AI agents for biological R&D - with tooling it calls Copilot that designs libraries, generates assembly protocols and optimizes biomolecules with less human hand-holding. This is not, to be clear, a company that discovered AI last Tuesday. It was building machine-learning models that understand biology years before every pitch deck had an agent slide.

A customer list like a hall of fame

You do not build an enterprise platform for biology with a growth hack. You build it one hard integration at a time, earning one lab, then the next. TeselaGen's roster is the kind that accrues slowly and then looks inevitable in hindsight:

The mix is the interesting part: hard-nosed industrial biotech and biopharma sitting alongside universities and a national lab. That spread is a decent proxy for a tool that solves a real workflow rather than a trend, because academics and Amgen do not usually agree on software.

What just happened

The last couple of years have been about deepening partnerships and sharpening focus.

• May 2023
  Partnered with NinthBio, folding its Homology Path design algorithm into the platform to speed DNA variant library construction.
• Sep 2023
  Renewed its Joint BioEnergy Institute partnership - a five-year contract running through 2027 - to advance carbon-neutral bioproducts and biofuels from non-food plant fibers.
• Dec 2023
  Spun DNA manufacturing out into a separate company, Built Biotechnologies, Inc. - a bet on staying a software business rather than owning the whole stack.
• 2024 -
  Repositioned around autonomous AI agents for biological R&D under the TeselaGen Copilot banner.

The spin-off is the quietly instructive move. Owning DNA manufacturing is tempting - it is where a lot of money and control lives. Deciding you are a software company and letting the manufacturing go is harder, and usually smarter, because it means you actually know which layer you are great at.

The shape of the thing

TeselaGen is not a unicorn and does not pretend to be. It is a small, grant-fed, deep-tech company whose value shows up in its customers' output, not its headcount. Reported figures below are third-party estimates and should be read as approximate.

Figures compiled from public third-party databases; funding and revenue estimates vary by source and are approximate.

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