The lab, closed into one loop.
Protocols, instruments, results, and models running as one continuous cycle — where every experiment's evidence improves the next decision automatically.
Built for hospital, academic, translational, and clinical research labs.
Right now, the loop is broken.
Design happens in one tool, testing happens at the bench, and optimization happens in someone's head, weeks later, if it happens at all. Every step in between is a handoff, a file, a spreadsheet, a hallway conversation, and every handoff strips out the reasoning that made the data useful: the hypothesis, the exact conditions, the deviation nobody wrote down. What survives is a number without its context, and a model that has to be told, again, what already happened at the bench.
FluenticOS closes each of those gaps at the source: one system that carries a protocol from design through execution, keeps every result tied to the hypothesis and conditions that produced it, and feeds evidence straight back into the next decision, continuously, with a tamper-evident record as a byproduct, not an afterthought.
Five ideas. One system that never stops watching.
Design
Lay out a protocol once in a typed step editor, timing, dependencies, and instrument I/O checked as you build it, then run it across as many experiments as you need.
Connect
Incubators, imagers, liquid handlers, and other lab hardware link in through a lightweight edge agent per system, so the platform always knows the true state of every instrument and sample.
Orchestrate
A scheduling engine sequences and times every step across every active experiment, like air-traffic control for your lab, so runs never collide.
Monitor
A single dashboard shows every experiment, instrument, and sample's status and location, with timelines and ETAs, flagging problems the moment they happen.
Trace
Every sample, measurement, and step is tracked end to end in a hash-chained, tamper-evident log, full lineage from source to result, for reproducibility and audit.
The loop, in motion.
A simulated pass through all five stages, plan drifts into a deviation, the assistant proposes a fix on the record, and the next version of the protocol carries it forward.
Every instrument, one surface. Every AI action, one leash.
This is where the loop actually closes. Instruments plug in through the same control surface a material-handling system would use, and an AI assistant reaches the bench through the exact same door as everyone else, not a side channel that quietly disconnects prediction from experiment.
MHS-style device integration
Every instrument, whether it's a liquid handler, an incubator, or a mobile relocation robot, speaks the same out-of-band control surface: describe, run a procedure, read a live state key, write a setpoint. One path, callable identically from the UI, the REST API, MCP, or an edge-agent script, and validated on every call against the instrument's own self-reported procedure list and safety envelope. Heterogeneous fleets slot in without bespoke glue code per vendor.
Agentic control, on a leash
The built-in assistant doesn't get a side channel: it calls the same policy-checked, event-sourced capabilities a person does, over MCP tool-calling, one registry, no shadow API. Every AI write is checked against your access rules, deferred for a human's approval when it touches something sensitive, like moving a plate, and logged as an action taken on behalf of a specific person. "The AI did it" is never where the audit trail ends.
A few more things quietly doing the work
Ontology-linked catalog
Compounds, treatments, and conditions linked to the standard vocabularies (OBI, RO, PROV-O) your other systems already speak.
Sample lineage
Every sample, plate, and well traced back through every derivation, pool, split, and transfer, all the way to its source, with a unique accession ID for life.
Reagent registry
Every working reagent traced back to its source lot, concentration, and expiry.
Consumable registry
Every plate, tube, and kit tracked well by well, from the moment it's registered to the moment it's discarded.
Data & screen explorers
Query results like a spreadsheet, plot them like a scientist, and turn a compound screen into viability, Z'-factor, and dose-response fits automatically.
Edge-agent fleet
One lightweight agent per system bridges every instrument to the platform, driver-connected or run by hand, with no single point of failure across the fleet.
Compliance & audit trail
A hash-chained, externally anchored event log; role- and rule-based access control with separation-of-duties and time-boxed break-glass overrides; and one-click FAIR evidence packages (RO-Crate + PROV-JSON) for every project.
What changes when the loop closes
These aren't automation wins alone. They're what becomes possible once design, testing, and optimization stop being separate hand-offs and start being one system that remembers.
Faster decision cycles
The result of one experiment updates the next decision as soon as it lands, not after someone writes a report and someone else reads it.
Models learn from what actually happened
Every result keeps its hypothesis, conditions, and deviations attached, so the next model refinement trains on context, not a bare number.
Protocols that adapt with evidence
When a run drifts from what was planned, that's a signal the loop can act on: propose the fix, version the protocol, and use it on the next run.
No re-entry, no reformatting
Results move from instrument to record to model in their native shape. Nobody retypes a plate layout or reconciles three spreadsheets by hand.
Higher instrument utilization
Every instrument's real capacity is known and booked precisely, so expensive equipment sits idle less often.
Audit-ready by default
A hash-chained, externally anchored event log and full lineage are captured as a byproduct of running the work, not a separate task afterward.
Grows with your lab
Start with a single instrument and expand to a full automated fleet on the same system, without re-platforming.
Works with what you own
Built to connect heterogeneous instrument fleets, no rip-and-replace, no single-vendor lock-in.
Research labs running real experiments, not just single assays: high-throughput compound screening, longitudinal culture studies, multi-step sample-prep pipelines, and regulated clinical workflows, each with its own instruments, timing, dependencies, and compliance requirements.
Ready to see your lab run itself?
We're onboarding a small number of research labs ahead of general availability.