BREAKING  LatchBio raises reported $163M to scale the AI agent for biology  /// 4,000+ scientists now run pipelines without writing a line of code  /// 40+ kits & instruments wired into one data platform  /// Claims ~80% lower analysis costs vs. traditional methods  /// From stealth 2021 to 300+ biopharmas & labs  /// Built on Rust, Python, Nextflow & Kubernetes  /// BREAKING  LatchBio raises reported $163M to scale the AI agent for biology  /// 4,000+ scientists now run pipelines without writing a line of code  /// 40+ kits & instruments wired into one data platform  /// Claims ~80% lower analysis costs vs. traditional methods  /// From stealth 2021 to 300+ biopharmas & labs  /// Built on Rust, Python, Nextflow & Kubernetes  ///
Company Profile / Bioinformatics / San Francisco

LatchBio

The company teaching biology to read its own data - no code, no servers, no waiting in line behind a bioinformatician.

LatchBio logo over a snow leopard on a dark navy background

The snow leopard is the house mascot - fast, rare, and good at finding what it is looking for in a lot of noise. The logo, mercifully, requires no field guide.

YesPress Profile  |  Filed San Francisco  |  Subjects: Andere, Workman, Giffin

It is a Tuesday, and somewhere a biologist just dragged a folder of raw sequencing files onto a webpage. No terminal. No cloud console. No email to the lab's lone engineer asking, again, where the pipeline broke. A few minutes later, charts appear. This is the ordinary miracle LatchBio sells.

Who They Are Now

A data platform that happens to speak biology

LatchBio calls itself the AI agent for biology data analysis. Strip away the slogan and the job is plain: take the flood of data a modern lab produces - sequencing runs, gene-editing screens, spatial maps of tissue - and turn it into something a scientist can store, analyze, and actually read. Today that platform reportedly connects 40+ kits and instruments and is used by thousands of researchers.

The company is small for its ambition - roughly 30 people - and headquartered in San Francisco. What it lacks in headcount it makes up for in plumbing: storage, compute, pipelines, visualization, and metadata, stitched into one place so biologists stop gluing tools together by hand.

Most software promises to save you time. LatchBio's pitch is quieter and harder: stop paying scientists to babysit servers. - The LatchBio premise, paraphrased
The Problem They Saw

Biology got cheap to measure and expensive to understand

Sequencing a genome used to cost a fortune. Now it costs less than a nice dinner, and labs generate datasets that would have been unthinkable a decade ago. The bottleneck moved. It is no longer the experiment - it is everything that happens after.

The after is messy. Files land in inconsistent formats. Pipelines live in someone's head or a fragile script. Cloud bills surprise everyone. And the person who can run the analysis is usually a bioinformatician with a queue three weeks long. The science waits.

LatchBio's founders looked at this and saw a software problem wearing a lab coat. The tools existed; they were just hostile to the people who needed them. A wet-lab biologist should not need a DevOps certificate to look at their own results.

So the company's central tension is simple to state and hard to solve: how do you give a non-programmer the full power of cloud-scale bioinformatics without handing them a single thing that looks like code?

The data got democratic. The tools to read it did not. That gap is the whole business. - The bottleneck, in one line
The Founders' Bet

Three Berkeley dropouts, one stubborn idea

In 2021, Alfredo Andere, Kenny Workman, and Kyle Giffin left UC Berkeley to start LatchBio. They came from Big Tech - Andere had interned at Facebook and Google - and they had grown restless building systems that optimized advertisements. They wanted their infrastructure skills pointed at something that mattered more.

Their bet was contrarian. Plenty of companies were selling bioinformatics to bioinformaticians. Latch decided to build for the biologist who never wanted to touch a command line in the first place - the larger, more frustrated, and far less served audience.

Alfredo Andere

Co-Founder & CEO

Kenny Workman

Co-Founder & CTO

Kyle Giffin

Co-Founder & COO
They left the company that ranks your ads to build the one that ranks your genes. The irony was not lost on them. - On trading Big Tech for biotech
The Product

One platform, assembled like a lab bench

The product started narrow - a tool for CRISPR data - and grew into a full stack. The pieces are deliberately mundane in name, which is the point: they map to what a scientist already does.

Run

Workflows

Pipelines at scale, built on the Nextflow standard. Pre-built solutions for bulk RNA-Seq, ATAC-Seq, qPCR, gene editing and spatial transcriptomics.

Compute

Pods

Scalable, reproducible compute without provisioning a single server or guessing at instance types.

See

Plots

An interactive visualization builder for exploring results and dropping them into a figure.

Track

Registry

Structured capture of experimental metadata, so results stay organized and FAIR-compliant.

Store

File Storage

Centralized data management connecting output from 40+ kits and instruments in one library.

Assist

AI Agent

An assistant plus a library of ready-to-run analyses - and white-labeled portals so kit makers can ship Latch-powered software under their own badge.

Pods, Plots, Workflows, Registry. It reads like a lab notebook that finally learned to talk to the cloud. - The product line, named plainly
The Short History

From stealth to scale

2021 - OCT

Out of stealth, $5M seed

Lux Capital leads the seed round, with General Catalyst, Haystack, Fifty Years and Asimov's Alec Nielsen joining. The first product targets CRISPR data.

2022 - JUN

$28M Series A

Coatue and Lux Capital co-lead, joined by Hummingbird Ventures and Caffeinated Capital. The mission widens from CRISPR to a modern data stack for all of biology.

2022 - 2025

The platform fills in

Workflows, Pods, Plots and Registry come together. Support grows to 40+ kits and instruments; named clients reportedly include the Broad Institute, AstraZeneca, Amgen, GSK and Takeda.

2026 - APR

Reported Series B

A reported financing of roughly $130M brings total funding to approximately $163M - capital aimed at the AI-agent chapter. (Figures per third-party data; treat as approximate.)

The Proof

The numbers that carry the argument

A slogan is cheap. Adoption is not. LatchBio's case rests on a handful of figures - reported by the company and third parties, so read them as directional rather than audited.

4,000+
Scientists on the platform
300+
Biopharmas & R&D labs
40+
Kits & instruments supported
~$163M
Reported total funding

The funding curve

Reported round sizes, USD millions // sources: Crunchbase, PitchBook, press
Seed '21
$5M
Series A '22
$28M
Series B '26
~$130M

Bars scaled to the largest reported round. The Series B figure (~$130M, April 2026) comes from third-party records and has not been independently confirmed here - hence the tilde.

Each round is bigger than the last by an order of magnitude. Investors are not betting on a tool. They are betting on a layer. - Reading the curve
The Mission

Infrastructure for the biocomputing revolution

LatchBio frames its purpose in infrastructure terms: build and disseminate the data infrastructure for the biocomputing revolution. It is a grand phrase for an unglamorous job - being the boring, reliable layer everyone else builds on top of.

The economics follow the mission. The company claims roughly 80% lower analysis costs than traditional methods and over 72% savings versus rolling your own setup on AWS, GCP or Azure. Whether those exact figures hold, the direction is the selling point: cheaper, faster, and reproducible enough to be IND-ready.

There is a quieter business hiding here too. By white-labeling its analysis portal, Latch can sit behind kit and instrument makers - the engine under someone else's badge. That is how a 30-person company reaches thousands of scientists it never has to sell to directly.

The competition is real: DNAnexus, Seqera, Benchling, Form Bio, and the cloud giants themselves. Latch's wager is that being the friendliest to the actual scientist beats being the most powerful for the specialist.

The goal is not to be the smartest tool in the lab. It is to be the one nobody has to think about. - On the ambition of being infrastructure
Why It Matters Tomorrow

The AI agent chapter

The newest framing - "the AI agent for biology data analysis" - is a bet on where the work is heading. If models can read a dataset, propose an analysis, and run it, the scientist's job shifts from operating tools to asking good questions. Latch wants to own the surface where that conversation happens.

That is a crowded ambition and an unproven one. AI agents are easy to demo and hard to trust, especially when the output feeds a drug program. The companies that win will be the ones whose results are reproducible enough that a regulator, not just a researcher, will accept them.

In a field where a wrong answer can end up in a clinical trial, "good enough to demo" is not good enough. Reproducibility is the moat. - The bar for AI in biology

Back to that Tuesday. The biologist who dropped a folder onto a webpage is reading a chart that, a few years ago, would have meant a three-week wait and a favor owed to the lab's one engineer. The data did not get simpler. The wall in front of it got lower. That is the entire point of LatchBio - and, if the bet holds, the reason the next discovery arrives a little sooner.

The Rolodex

Where to find LatchBio

Website latch.bio LinkedIn /company/latchbio Twitter / X @latchbio GitHub /latchbio Blog Substack Crunchbase profile Demos YouTube search Email contact@latch.bio

Figures on this page - funding totals, customer counts, cost-savings claims - are drawn from public sources including the LatchBio website, Crunchbase, PitchBook, and press coverage, and several are company-reported or third-party estimates. The reported ~$130M Series B (April 2026) and ~$163M total are based on third-party data and should be read as approximate. Sources: latch.bio, businesswire.com, techcrunch.com, venturebeat.com, genomeweb.com, crunchbase.com, pitchbook.com.