Autonomous labs. Done by the model.
Biofactory turns a wet-lab into a closed loop. Active-learning agents propose experiments, the planner binds reagents and plates, the instruments execute, and the readout updates the model — with chain-of-custody that satisfies regulated environments.
What the model actually computes.
These are the workloads the AI runs in autonomous labs — rendered live in SVG. Each panel is the geometry the model is reasoning about, the physics it solves for, and the validation it lands against measured reality.
Aether is the AI doing the autonomous labs work.
Three things to know about the AI underneath every workload on this page. We aren't building a chat surface for your team — we are training a foundation model that plans, runs and reports.
The AI does the discipline
Not a chat interface over your engineers. A foundation model that plans the study, picks the method, runs the agents, validates against measured reality and writes the report. The same shape an experienced practitioner would follow — done by the model.
One model, your context
We don't ship a sibling model per industry. The same Aether weights serve every discipline, but the agents on top are trained against your standards, your historical data and your regulatory posture.
Reality is the regulariser
We train on simulation, instrument and design data — not scraped text. The model gets better the more reality it touches. Reality is what tells it when it's wrong.
The work, done by Aether.
These are the workloads Aether already runs in autonomous labs. Not a wish-list. Each ships with validated benchmarks and traces a senior engineer can read end-to-end.
- 01
Assay scheduling
Plate logistics, reagent-lot lineage, instrument allocation, cycle-time estimation. Gantt-grade scheduling with re-planning when an instrument falls over.
- 02
High-content imaging
Cell-painting hit calling, segmentation, batch correction. Pairs with the in-silico discovery loop for hit triage.
- 03
Biosecurity
Dual-use refusal, controlled-pathogen and select-agent lookups before a robot is allowed to start.
- 04
eBR + audit
Electronic batch records, immutable chain-of-custody, exportable as SIEM events.
- 05
Failure memory
Every botched run is captured, classified, and surfaced. The lab gets smarter; new hires inherit the lessons.
- 06
Federation
Multi-site campaigns with shared model state and per-site policy. Federated active learning across a network of labs.
“We are building the AI that does autonomous labs — not the one that talks about it. Aether doesn't ask your engineers to drive its solvers. It runs the work, validates the artefact and writes the memo.”
The stack today.
The typical stack in autonomous labs — a half-dozen seven-figure renewals stitched together with internal glue code. We replace the spine; we don't touch the niches that work.
The agents in the mix.
Aether coordinates these specialist agents for autonomous labs. Each is independently deployable, but the coordination — and the foundation model underneath — is what makes them more than a folder of scripts.
How we ship into regulated autonomous labs.
The pieces that matter to procurement, security review and the regulator. We ship into industries where the boundary is real and the audit is unforgiving.
21 CFR Part 11 electronic batch records out of the box
ALCOA+ data integrity by construction, not retrofitted
Chain-of-custody from reagent to plate to result
Biosecurity guardrails the runtime cannot disable
Other companies shipping with us.
A slice of the customers we're allowed to name. The pattern repeats: one workload, one pilot, one comparison memo we co-author.
- Aerospace
Northwind Aerospace
Retired a $1.8M CAE renewal across two quarters.
62%wall-clock reductionRead the story - Drug discovery
Lyrebird Therapeutics
Closed the discovery loop on a fibrosis programme.
9 weekshypothesis → measured hitRead the story - Chip design
Helix Silicon
RTL to signoff on a 4nm SoC — without an incumbent-EDA renewal.
0EDA license serversRead the story
The AI is also running here.
A foundation model that serves twenty-two industries today. The same weights, different agents, different pilot patterns. Browse the ones nearest yours.
Questions about autonomous labs.
What does Aether replace in autonomous labs?
The incumbent stack here — Vendor lab schedulers, Scientific-data lake platforms, Closed-source LIMS automation, Bespoke instrument schedulers — collapses into one model and one project file, with Aether for autonomous labs and Aether for drug discovery doing the work under one safety story.
What outcomes should we expect?
Endless (wet-lab campaigns · one model); SiLA-2 + protocol export (instrument drivers); ALCOA+ / 21 CFR 11 (compatible) — scoped to your workload and measured against your own acceptance criteria.
How does a pilot work?
Three to eight weeks against a workload and a win condition you co-author. We run alongside your incumbent stack on real cases; if we aren't better on the metric by quarter end, the rest of the quarter is on us.
Can it deploy inside our boundary?
Yes — managed, in your VPC, on-prem or fully air-gapped on Aether Cloud, with the audit trail and provenance the regulated parts of this work require.
The AI is ready for autonomous labs.
Send us the workload that hurts. We'll come back with a scoped pilot — three to eight weeks, win condition co-authored. If we aren't better on the metric by quarter end, the rest of the quarter is on us.