The same model. Every discipline.
Aether is one foundation model. Here's how it shows up across the disciplines we serve today — the incumbent stacks it replaces, the workloads it covers, the numbers customers actually saw. Pick the discipline that looks like yours.
One AI for every physical-world discipline.
Three things we hold true across every industry we ship into. The rest of this page is the proof — discipline by discipline, workload by workload.
One AI, every discipline
Aether is one foundation model. The same weights solve docking, structural FEA, chip signoff and wet-lab campaigns. Specialisation lives in the agents and the workloads — not in different models with different blind spots.
The autonomy is the product
We aren't selling a chat interface for your engineers. Aether plans the study, picks the method, runs the agents, validates against measured reality, and writes the memo. The work that took a senior engineer a quarter takes the model an afternoon.
Earn each industry one workload at a time
We don't pitch the platform — we pilot the workload. Scope one painful workload, deploy alongside the incumbent, co-author the comparison. If we aren't better on the metric by quarter end, the rest of the quarter is on us.
Four shapes the conversation usually starts with.
The teams that pilot Aether first tend to look one of four ways. If any of these feels familiar, we're probably the right next conversation.
- 01
You renew a seven-figure stack you don't love
Commercial CAE, EDA, computational chemistry, lab orchestration — the stacks are mature and the renewals are eye-watering. We replace the spine of the stack, not the niches that work.
- 02
Your iteration loop is the bottleneck
Not the solve — the hand-offs around the solve. Case setup, queue, re-mesh, comparison, memo. Aether collapses that loop into an autonomous agent flow. Iteration count per quarter compounds.
- 03
Your data is closed and your boundary is real
ITAR, GxP, export-control, sovereign — your data does not leave the perimeter you set. Aether runs on-prem, air-gapped, in your VPC. Same runtime, your boundary.
- 04
You've been waiting for the AI that does the work
Not the one that suggests next tokens — the one that lands the chip, ships the molecule, signs off the wing. Aether is built to do the work, not narrate it.
The same model, five surfaces.
Each discipline is a surface on top of one foundation model. Different agents, different benchmarks, different pilot patterns — same weights underneath.
- 01
Engineering
CFD, FEA, electromagnetics, multiphysics, acoustics, thermal, fatigue, additive — unlimited workloads across every discipline, in one project file. CAE renewals retire.
- 02
Semiconductors
RTL through signoff on one database. Specialist agents drive synthesis, P&R, timing, power, EM/IR and verification. Tape-out without an incumbent-EDA renewal.
- 03
Drug discovery
Docking, FEP, MD, QM/DFT, ADMET, pKa, pharmacophore, generative, biologics — specialist agents end-to-end. Hypothesis to measured hit in nine weeks.
- 04
Autonomous labs
Closed-loop wet-lab campaigns. SiLA-2 drivers, plate logistics, the Apex world model on imaging, eBR / 21 CFR 11 audit. The bench runs unattended overnight.
- 05
Software
Whole-repo planned edits, repo-aware review, cross-language migration, CVE-aware patches. Fifteen IDE and hosted-agent surfaces. Same memory, same review history.
The numbers customers actually saw.
A slice of the headline results across the first wave of pilots. Each one is reproducible — the cases, scripts and configurations are documented on the discipline pages they live on.
- 62%wall-clock reduction · aerospace CAE
- 9 weekshypothesis → measured hit · pharma
- 1.06×perf at iso-area vs commercial EDA
- 0 bytesleaving the boundary · sovereign deploy
- 10×plates per scientist · autonomous lab
- 4×PRs per engineer · software
Pick the one that looks like your workload.
Each discipline page names the incumbent stack, the workloads Aether covers, the specialised agents at play, the pipeline we run end-to-end, the compliance posture, and a customer who has already done it.
Drug discovery
From target to wet-lab in the same loop.
The incumbent computational-chemistry stack + the wet-lab, replaced by one model.
Computational-chemistry suitesDocking + FEP toolsMolecular-dynamics packagesPharmacophore software+2Chip design
RTL to signoff, under one platform.
The incumbent EDA spine, collapsed.
Logic synthesisPlace-and-routeStatic timing analysisFunctional verification+2Aerospace
Ascent to signoff, on one model.
CFD, FEA, EM and aeroelasticity — one project, one model.
Structural FEAComputational fluid dynamicsFull-wave electromagneticsExplicit-dynamics crash codes+2Energy
From grid to reactor, on one model.
Fusion, fission, grid, renewables — under one foundation model.
Plasma-physics codesReactor-physics & neutronics suitesGrid-simulation platformsWind-turbine multiphysics tools+2Materials & quantum
Multi-scale, in one model.
DFT, MD and the bridge between them.
VASPGaussianQ-ChemORCA+3Autonomous labs
The lab as a loop, not a queue.
Liquid handlers + dispensers + open-source robotics, with audit out of the box.
Vendor lab schedulersScientific-data lake platformsClosed-source LIMS automationBespoke instrument schedulersSoftware & SRE
The coding agent your seniors trust.
The incumbent coding-agent stack, behind one model.
Editor-embedded coding assistantsLong-horizon hosted coding agentsHosted PR-review automationOpen-source autonomous agents+1Manufacturing
Process control that learns from every batch.
Coupled thermal-fluid-chemical, in production.
AspenTech process modellingPolyflowRocky DEMin-house APC scripts+1Education & research
A simulation-native model that universities can teach with.
Free academic tier, classroom-safe deploys, citation-grade reproducibility.
Per-seat commercial CAE in teaching labsPer-paper computational-chemistry licencesLocally-maintained legacy codesClosed cloud notebooksFinancial services
Quantitative engineering, with audit baked in.
Coupled physics + numerical methods + memory provenance for regulated quants.
In-house quant libraries (decades old)Commercial pricing librariesBespoke risk-modelling stacksCustom backtest infrastructureHealthcare & life sciences
From discovery to clinical pipeline, under one model.
Drug discovery + ADMET + biostatistics, with HIPAA-grade deploys.
Per-domain commercial bio softwareBespoke clinical-trial design toolsSpecialised biostatistics packagesVendor pharmacovigilance systemsLegal
AI for legal work where citation and reasoning matter.
Long-horizon legal reasoning with provenance and audit baked in.
Legacy contract-review toolingPer-seat litigation databasesRegulatory-compliance spreadsheetsBespoke matter-management systemsRetail & commerce
Inventory, pricing and demand as a coupled dynamical system.
The same coupling that powers multiphysics, applied to commerce.
Per-vendor demand-planning toolsBespoke pricing enginesLegacy inventory-management systemsPer-channel marketing analyticsMedia & entertainment
Production, distribution, and rights — coordinated by one model.
Long-running productions, audience modelling, rights-aware planning.
Bespoke production-management softwareVendor audience-modelling toolsPer-platform distribution dashboardsManual rights-and-clearance workflowsGovernment
Mission-grade AI for federal and state programmes.
Sovereign deploys, FedRAMP-roadmap, audit-grade trace.
Per-agency procurement of point toolsLegacy federal-civilian computing stacksBespoke ITAR-bound analysis softwareClosed cloud-AI offeringsLife sciences
Biotech and pharma — discovery, development, manufacturing.
Discovery through CMC, on one substrate with ALCOA+ audit.
Vendor-locked computational-chemistry stacksBespoke clinical-trial platformsLegacy CMC and stability-modelling toolsManual quality-control documentationNonprofits
Mission-aligned AI for non-commercial work.
Donated and discounted access for nonprofit programmes.
Donated commercial tooling with strings attachedVolunteer-maintained internal scriptsPer-volunteer cloud notebooksCybersecurity
AI for defenders, at the speed of attackers.
Long-horizon analysis, CVE-aware review, capability-gated tool use.
Per-tool security analytics stacksBespoke SOAR playbooksVendor-locked threat-intelligence platformsManual vulnerability-research workflowsCustomer support
Long-horizon support agents that don't drift.
Customer-state-aware automation, capability-gated refunds.
Per-vendor support automation suitesOff-the-shelf chatbot platformsBespoke ticketing-system integrationsManual macro-and-template workflowsCoding
The coding workflow your senior engineers will actually use.
Whole-repo edits, CVE-aware review, fifteen adapter surfaces.
Editor-embedded coding assistantsHosted long-horizon coding agentsPull-request automationManual code-review workflowsCode modernization
Cross-language migrations that semantically preserve.
Legacy-codebase migrations across six languages, diffable.
Bespoke per-language migration consultanciesMechanical transpilersRewrite-from-scratch programmesBuilding agents
Production agents on the substrate that ships them.
Long-horizon runtime, capability gating, eval-gated deploys.
Open-source agent frameworks (notebook-grade)Vendor-locked agent platformsBespoke orchestration codePublic sector
Sovereign, air-gapped, ITAR-clean.
Where zero bytes leave the boundary.
fragmented per-domain procurementlegacy national-lab codesbespoke air-gapped toolingRobotics
The world model robots train in.
The bespoke sim, RL and sim-to-real stack, collapsed into one world model.
Bespoke simulation rigsHand-tuned domain-randomization scriptsSeparate perception-sim pipelinesIn-house RL training infrastructure+2Critical infrastructure / OT
Where a packet moves a turbine.
IT/OT security on the one model that already simulates the physics.
Passive OT-monitoring appliancesSeparate IT and OT SIEMsBespoke ICS asset-inventory toolsTabletop-only consequence analysis5G & telecom
A digital twin of the network, radio to core.
RF, RAN and slicing on the one model that already does the physics.
Per-vendor RF planning toolsStandalone RAN optimization suitesDrive-test-only validationVendor-locked OSS analytics
Six reasons teams pick Aether over the incumbent.
We earn each pilot on the metric. These are the reasons teams choose Aether when the comparison is honest and the numbers are public.
One model, not five
The same foundation model serves every discipline. No siloed surrogate per workload.
Autonomy by default
Aether plans and runs the study — not a copilot waiting on instructions.
Numbers, not narratives
Every claim ships with a benchmark you can rerun. Reproduction scripts are public.
Your boundary, your data
Managed cloud, your VPC, on-prem, or air-gapped. We deploy where you already work.
GxP-grade audit
Per-run immutable traces, eval-gated promotion, signed by model version and agent ID.
We pilot first
Scope a workload, prove it, then sign anything. We don't ask for renewal money up front.
Don't see your discipline?
If your workload doesn't map cleanly to one of these, send us a note. New solutions usually start as a single customer with an unusually hard problem.