Financial services. Done by the model.
Financial-services quant work has a lot in common with engineering simulation — stochastic dynamics, calibration to measurement, signoff under regulation. Aether brings the same architecture: deterministic memory, audit-grade trace, capability gating between model and tool. Quant teams use it for derivative-pricing model research, risk-system development, and infrastructure-grade backtesting.
What the model actually computes.
These are the workloads the AI runs in financial services — 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 financial services 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 financial services. Not a wish-list. Each ships with validated benchmarks and traces a senior engineer can read end-to-end.
- 01
Derivative-pricing research
Monte Carlo, finite-difference and PDE-based pricing under realistic dynamics. The same numerical-stability machinery that handles transient CFD.
- 02
Risk-system development
Stress-testing, scenario analysis, VaR / Expected Shortfall computation across portfolio. Auditable trace per scenario.
- 03
Backtest infrastructure
Long-horizon strategy evaluation with versioned data and provenance-tracked decisions. "Why did the agent recommend X on day Y" is answerable.
- 04
Model risk management
Documented model lineage, validation logs, and explainable failure modes. Aligns with SR 11-7 model-risk-management practice.
- 05
Quant research review
AI-assisted code review for quant codebases with CVE-aware findings and behaviour-preservation tests.
- 06
Regulated deployment
Run on-prem in your firm's environment. No customer data leaves the boundary; audit-trail format integrates with existing SIEM.
“We are building the AI that does financial services — 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 financial services — 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 financial services. 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 financial services.
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.
ITAR-clean compartments for defence and aerospace work
Export-control gating on agent capabilities by jurisdiction
Sovereign / air-gapped deployments — zero outbound traffic
Reproducible runs signed by model-version hash for design-review
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 financial services.
What does Aether replace in financial services?
The incumbent stack here — In-house quant libraries (decades old), Commercial pricing libraries, Bespoke risk-modelling stacks, Custom backtest infrastructure — collapses into one model and one project file, with Aether for drug discovery doing the work under one safety story.
What outcomes should we expect?
Audit-grade (memory and trace provenance); Same model (for pricing and backtest research); Your boundary (VPC, on-prem, or our managed cloud · FedRAMP on roadmap) — 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 financial services.
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.