In the first weeks of 2020, a lab test in the Philippines still moved on paper. A swab went into a machine, a number came out, and somewhere a person copied that number by hand onto a form, then into a spreadsheet, then into a report that a physician would sign hours later. When a country needs a few thousand results a week, that pipeline holds. When it needs a hundred thousand, it snaps.
Dashlabs.ai was built in the gap where that pipeline snapped. It started as a volunteer effort - a handful of technically minded people trying to help their country test faster during the pandemic. It became a Y Combinator company, a seed-funded business, and the quiet software layer under a national testing program. The founders did not set out to build a health-tech startup. They set out to fix a queue.
01What Dashlabs.ai actually is
Strip away the branding and Dashlabs.ai is an operating system for a medical lab. It is cloud-based software that ties together the parts of a laboratory that usually do not talk to each other: the analyzers on the bench, the patient's record, the cashier at the front desk, and the report that goes out at the end. A test ordered at registration flows to the machine that runs it; the result flows back automatically, gets checked against normal and critical ranges, and lands in the patient's file without anyone retyping a digit.
That last sentence is the whole product. "Without anyone retyping a digit" is the difference between a lab that scales and one that drowns. The company calls its category a laboratory information system, or LIS, but it has grown past the acronym. There is an electronic health record for storing and pulling patient history. There is a PACS viewer that lets a clinic open DICOM imaging files - X-rays, ultrasounds - inside the same screen as the bloodwork. There is billing, booking, and queueing. The pitch, in the founders' own shorthand, is "end-to-end DIY lab software."
The "DIY" part matters more than it sounds. Traditional lab systems arrive as heavy installations sold to hospital IT departments over long procurement cycles. Dashlabs.ai is meant to be switched on by the lab itself - a base subscription, a few add-on modules, and a workflow that a small clinic can stand up without a systems integrator on retainer.
Consider what "bi-directional machine integration" means on a busy morning. A patient is registered, and their demographics travel to the analyzer before a single vial is loaded. The machine runs the sample and sends the number straight back into the record, tagged to the right person, checked against the reference range for that test. If a value is abnormal or critical, the system flags it instead of trusting a tired human to notice at 2 a.m. None of this is glamorous. All of it is the difference between a report you can trust and one you have to double-check by hand.
The imaging side follows the same logic. A clinic that adds the PACS module can open an X-ray or an ultrasound in the same window where the bloodwork lives, rather than shipping DICOM files to a separate viewer or, worse, a stack of film. For a small provider, collapsing four tools into one login is not a convenience - it is the only way the numbers work at all.
02The Red Cross, and the test that got the product real
Every startup wants a case study. Few get one the size of a national emergency. When the Philippine Red Cross needed to scale COVID-19 testing, Dashlabs.ai's software became the machinery that made the scaling possible. The numbers from that period read like a stress test, because that is what they were.
Read those bars slowly. The point of automation here was not to remove people - it was to move them off the parts a computer does better. A pathologist should spend five careful minutes on the results that need judgment, not forty-five distracted ones shuffling batches. A med-tech should run the machine, not transcribe it. The software absorbed the copying, the checking, and the routing, which is exactly the work that fails first under volume.
There is a distribution lesson buried in the Red Cross work, too. After a deployment that public, Dashlabs.ai did not have to go door to door. Other healthcare providers came looking for the founders, having watched what the software did at scale. A result nobody can argue with is a better sales team than a sales team.
03Who uses it, and how it makes money
The pandemic put Dashlabs.ai on the map, but a company cannot live on a crisis. What has kept it running is the ordinary work of ordinary labs. The platform now sits inside diagnostic laboratories, medical centers, hospitals, dialysis centers, corporate clinics running annual physical exams - and, in a detail that says a lot about how general the software is, veterinary clinics. A lab is a lab, whether the patient walks in on two legs or four.
The business model is deliberately unglamorous: software as a service, priced for the small operator. A monthly base plan - in the range of PHP2,500 - covers patient registration and the cashier, linking payments to patients and saving records to a database. From there a lab bolts on what it needs: an accounting and ledger module, machine integration, a queueing system, each roughly PHP2,500 to PHP5,000. You pay for the shape of your own lab.
This is the part competitors find hard to copy, because it is a choice, not a feature. The incumbents in lab software optimize for the large hospital buyer, where a big contract justifies a long install. Dashlabs.ai optimized for the clinic that could never afford that - and there are far more of those. Low price, fast setup, and end-to-end coverage aimed squarely at Southeast Asian labs is a narrow lane, and narrow lanes are where focused companies win.
The add-on structure also does something quiet for retention. A lab that starts with registration and a cashier has already put its patient records inside the platform. When it later needs machine integration or a ledger, the path of least resistance is to switch on the next module rather than migrate to a rival. The product grows with the lab, and each module makes the next one stickier. That is not a trick; it is what happens when the base plan is genuinely useful on day one.
04The people who built it
Dashlabs.ai was founded in 2020 by a group whose resumes have almost nothing to do with running a laboratory. Bryan Giger, the CEO, came out of economics and had already co-founded a data lab in Switzerland. Martin Gomez, the COO, studied finance and social impact and had led technology work at a Philippine non-profit. Weston Coleman Lim, the CTO, is the engineer of the founding team. Miguel Gemotra and Philly Tan round out the group that got it off the ground.
The absence of lab veterans was not a gap - it was the edge. People who have run labs for twenty years stop seeing the forty-five-minute batch as strange; it is just how the day goes. Outsiders ask the naive question - why does this take so long? - and then, being technical, they go build the answer. That is the recurring shape of the company: a hard, unfashionable problem, approached by people who did not know they were supposed to accept it.
It helped that the team started in the field rather than in a boardroom. The company grew out of on-the-ground pandemic response, which meant the first users were not focus groups but real med-techs under real pressure, telling the founders exactly where the software broke. Building that way is slower than writing a spec, but it produces a product shaped by the job instead of by a guess about the job. By the time Dashlabs.ai reached Y Combinator's Winter 2021 batch - as one of only three Filipino startups in that cohort - the product had already been tempered by volume most startups never see.
05Where it sits in the market
Health-tech built in an emerging market gets treated, sometimes, as a discount version of the real thing. Dashlabs.ai reads the other way. The constraint - labs that cannot afford bloated on-premise systems - forced a leaner, cloud-first product that happens to be what a lot of the world actually wants. Build for the customer who has no slack, and you tend to build something honest.
The company raised its PHP65M seed round in November 2021, with backers including ADB Ventures (the venture arm of the Asian Development Bank), the conglomerate JG Summit Holdings, Immeasurable, and Good News Ventures. That mix - a development-finance investor beside a large local conglomerate - fits a company whose value is measured partly in access to healthcare, not only in revenue.
By 2024, Dashlabs.ai had moved well beyond its pandemic origins, positioning itself around everyday clinic and lab automation with AI-assisted diagnostics workflows. The open question is the interesting one: can a lab operating system proven in one country's labs become the default across the region? The ingredients are there - a general product, a low price, a track record at scale. What remains is the unglamorous grind of signing labs one at a time, which is, fittingly, the same kind of work the software was built to make easier.
For now, Dashlabs.ai occupies a specific and defensible spot: the end-to-end software layer for the small and mid-sized labs that the big vendors were never built to serve, in a part of the world where the need is enormous and the incumbents are thin. It is not a flashy place to stand. It is a durable one.