Your lab, running as one closed loop.
FluenticOS plans, schedules, runs and records every experiment in a single system, then feeds the results back into the next version of the protocol. Nothing is retyped, nothing loses its context, and the audit trail writes itself.
- Any instrument, one interface
- AI assistant, human approval
- Tamper-evident record, built in
Today, the loop is broken.
Design lives in one tool, testing happens at the bench, and optimization happens in someone's head, weeks later. Every handoff in between is a PDF, a spreadsheet or a hallway conversation, and each one strips out the reasoning that made the data useful.
FluenticOS closes each of those gaps at the source. One system carries a protocol from design through execution, keeps every result tied to the hypothesis and conditions that produced it, and feeds the evidence straight back into the next decision.
One experiment, start to finish.
A simulated pass through all five stages. A protocol is pulled from a paper, scheduled, run, and reconciled against the plan. The run drifts, the assistant proposes a fix on the record, and a person approves the next version.
- PlanExtract, design, compile against real inventory
- ScheduleSimulate the whole run before anything moves
- ExecuteRun exactly what was approved
- ObserveReconcile results, flag what drifted
- OptimizePropose the fix, version the protocol
Four layers, one system.
Under the loop sits a platform that models your lab as it really is: protocols, instruments, samples and the record of what happened to them.
Design
Write a protocol once in a typed step editor. Timing, dependencies and instrument inputs and outputs are checked as you build, so a protocol that compiles is a protocol that can run.
Connect
Incubators, imagers, liquid handlers and robots each link in through a lightweight edge agent, so the platform always knows the true state and location of every instrument and sample.
Orchestrate
A scheduler sequences every step across every active experiment so runs never collide. One dashboard shows status, timelines and ETAs, and flags problems the moment they happen.
Record
Every sample, measurement and step is written to a hash-chained, tamper-evident log with full lineage from source to result. Reproducibility and audit come for free.
The assistant works inside the system, not around it.
Instruments and the AI assistant both reach the bench through the same door as everyone else. There is no side channel, so nothing the AI does escapes the record.
Every instrument, one interface
A liquid handler, an incubator and a mobile robot all speak the same four verbs: describe, run a procedure, read a state, write a setpoint. The same path is callable from the UI, the REST API, MCP or an edge-agent script, and every call is validated against the instrument's own procedure list and safety limits. Mixed fleets slot in without vendor-specific glue code.
An AI assistant on a leash
The built-in assistant calls the same policy-checked capabilities a person does, over MCP tool-calling. Every action is checked against your access rules, held for a human's approval when it touches something sensitive like moving a plate, and logged as taken on behalf of a named person. "The AI did it" is never where the audit trail ends.
The registries and tools a real lab needs.
Sample lineage
Every sample, plate and well traced through each derivation, pool, split and transfer back to its source, with a unique accession ID for life.
Reagent & consumable registries
Reagents traced to source lot, concentration and expiry. Plates, tubes and kits tracked well by well from registration to disposal.
Ontology-linked catalog
Compounds, treatments and conditions linked to the standard vocabularies (OBI, RO, PROV-O) your other systems already speak.
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.
Compliance & audit trail
Hash-chained, externally anchored event log. Role- and rule-based access with separation of duties and time-boxed break-glass. One-click FAIR evidence packages (RO-Crate and PROV-JSON).
Open API & edge agents
Everything in the UI is also a REST and MCP call. One lightweight agent per system bridges each instrument, whether driver-connected or run by hand, with no single point of failure.
What changes when the loop closes.
These are not automation wins alone. They are what becomes possible once design, testing and optimization stop being separate handoffs and become one system that remembers.
Faster decision cycles
One experiment's result 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 the plan, the loop acts on it: 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.
Instruments that sit idle less
Every instrument's real capacity is known and booked precisely, so expensive equipment spends more of its life running.
Works with what you own
Start with one instrument and grow to a full automated fleet on the same system. No rip-and-replace, no single-vendor lock-in.
Hospital, academic, translational and clinical research labs running real experiments: high-throughput compound screens, longitudinal culture studies, multi-step sample-prep pipelines and regulated clinical workflows.
Ready to close the loop in your lab?
We are onboarding a small number of research labs ahead of general availability.