Customer support. Done by the model.
Customer support is one of the most-deployed agent use cases — and one of the most likely to fail in production. The failure modes are well-understood: agents drift across conversations, take actions outside their authority, and break under edge cases. Aether for customer support pairs the long-horizon agent runtime with capability-gated tool use, deterministic memory, and explicit approval gates for sensitive actions.
What the model actually computes.
These are the workloads the AI runs in customer support — 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 customer support 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 customer support. Not a wish-list. Each ships with validated benchmarks and traces a senior engineer can read end-to-end.
- 01
Long-running case state
A customer's history across all prior interactions, in audit-grade memory. The agent answers "what did you tell me last time" without inventing answers.
- 02
Capability-gated actions
Refunds, credits, account-modifications gated by policy. The agent can process small refunds autonomously and escalate large ones.
- 03
Multi-channel handoff
Email, chat, voice, in-product — one conversation across channels with consistent memory and policy.
- 04
Tone-aware response
Customer-state-aware tone calibration. The agent treats a frustrated long-tenure customer differently from a first-time inquiry.
- 05
Escalation routing
Smart routing to human agents with full context, expected outcome, and provenance-tracked reasoning.
- 06
Quality monitoring
Every conversation is recorded, scored against quality criteria, and surfaced for review. Issues are caught at the agent layer, not after the customer complaint.
“We are building the AI that does customer support — 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 customer support — 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 customer support. 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 customer support.
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.
SOC 2 Type II managed cloud, your VPC, or on-prem
CVE-aware review at PR time with patch suggestions in-line
Capability gating on every shell command and tool call
Atomic, revertible commits signed by agent ID and model version
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 customer support.
What does Aether replace in customer support?
The incumbent stack here — Per-vendor support automation suites, Off-the-shelf chatbot platforms, Bespoke ticketing-system integrations, Manual macro-and-template workflows — collapses into one model and one project file, with Aether for software doing the work under one safety story.
What outcomes should we expect?
Customer-state-aware (memory across sessions); Capability-gated (refunds, credits, escalations); Approval-gated (high-impact actions) — 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 customer support.
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.