Plain answers to the hard questions about automating a lab — written from the bench, not the brochure. Plus our protocol library and the latest from Robot on Rails.
What today's AI can genuinely operate, what it still can't, and where the honest line sits.
ExplainerThe 2023–2026 shift from chatbots to closed-loop lab agents — what's been demonstrated, what's still human, and what it means for your bench.
ExplainerAutonomous experimentation is real in narrow domains and growing fast — but “lights-out science” is still mostly marketing. Here's the honest map.
DefinitionThe category, defined — AI that perceives, reasons, and physically acts at the bench, and how it differs from cloud labs and autonomous megalabs.
ExplainerBeyond walk-away runs — the closed loop where experiments choose the next experiment.
ExplainerAutonomous labs need a “USB moment” — a common protocol so any AI agent can drive any instrument. It's an active 2025–26 research front.
ExplainerThe field is quietly abandoning “lights-out” for shared control — AI does the heavy planning and execution, a scientist keeps the judgment.
ExplainerThe same foundation-model wave reshaping language and robotics is arriving in the lab — as agents that reason about protocols and perceive a real bench.
ComparisonRenting someone else's automated lab vs. running physical AI on your own bench — when each makes sense.
GuideReal price ranges for liquid handlers and integrated systems — plus the hidden costs that dwarf the sticker.
GuideThe honest answer to the bench scientist's first question — and the three ways labs actually "program" a robot.
ExplainerWhy knowing your own position is easy, knowing the world is hard, and what closes the gap.
ExplainerThe hidden visual checks every scientist makes — and why automation without sight fails silently.
GuideThe four failure modes — and why the benchtop is where most automation quietly dies.
ComparisonA buyer's decision guide — what each approach costs you, what it's best for, and an honest "when not to use us."
GuideThe four things worth recording on every run — and why machines can finally capture them consistently.
GuideA four-part filter for picking your first automation win — and the gut-check that confirms it.
ExplainerWhy off-the-shelf workflows run in weeks and novel ones take far longer — the "march of nines."
ComparisonWhen each wins — and the one test that reveals whether "flexible" automation actually is.
DefinitionThe unattended part of a workflow — and why it often matters more than raw speed.
ExplainerHow unattended operation multiplies throughput without new instruments or a second shift.
ExplainerWhy the real gain is turning execution time into thinking time — and multiplying your best scientists.
ExplainerFor most biotech, the win isn't replacing technicians — it's producing more useful data per scientist, faster.
ExplainerWhen samples can safely wait between steps, automation can line them up and run them later — no one standing by.
GuideThree questions: can the robot handle it, see it, and confirm it worked?
ExplainerAlmost anything, in theory — but ease drops fast with dexterity, force, judgement, and live sensing.
ComparisonFlexible human capacity vs repeatable process capacity — and how to actually compare them.
GuideYou don't need a robot on day one — but you should know your automation path before the process hardens.
GuideStart with the business objective, not the robot — and ask what better, faster data is worth.
ExplainerThe protocol is rarely as fixed as it looks — hidden requirements are what stretch the timeline.
ExplainerA working machine goes unused when the cost to change it exceeds the value it provides.
