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.
Animated snapshots of the AI work happening in customer support today. Each carries a representative metric from a customer workload, the physics the model is reasoning about, and a citation back to the discipline page.
38% faster resolution · KB coverage 64→91% · 8 languages
For decades, the slow part of customer support was the iteration loop — specifying the case, queuing the solve, parsing the result, deciding what to try next. Aether collapses that loop into an autonomous agent flow. The iteration count per quarter is what compounds, not any one solve being faster.
The same foundation model serves docking, structural FEA, RTL signoff and wet-lab campaigns. Specialisation lives in the agents and the workloads — not in different models with different training data and different blind spots.
Aether doesn't ask you to drive its solvers. It 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.
We train on simulation trajectories, instrument data and design history — the substrate of the physical world. The model gets better the more reality it touches, and reality is what tells it when it's wrong.
We work where the work is hard, the data is closed and the renewals are seven-figure. Customer support is one of those places. Here's how the discipline looks today, what the leaders are doing, and where Aether fits.
Teams in customer support run on a stack assembled over thirty years — a CAD core, a meshing tool, a solver, a post-processor, a scheduling layer and a results manager. Each is a separate seven-figure renewal. The hand-offs between them are where time is lost — case setup, queue, re-mesh, comparison, memo.
The leading teams are investing in internal platforms — wrappers around the incumbent tools, glue code, scheduling layers, a Python notebook on top. It works. But it ages badly, ties up senior engineers and doesn't compound. The best teams know they need the next layer; most don't know what it looks like.
Aether replaces the spine of that stack with a foundation model trained on simulation, instrument data and design history. The glue layer goes away. Workflows that used to live in a queue and a folder of scripts live in an autonomous agent flow. You keep the niches that work; Aether retires the middle.
The list below is the typical stack in this discipline. Aether replaces the spine. Niches stay where they are; we're not in the business of forcing rip-and-replace on the things that work.
Drawn from the campaigns we've run in this discipline. Each one is reproducible — the cases, scripts and configurations are documented in the research log.
Each workload below ships with validated benchmarks, agent traces you can read, and a pilot pattern we've run before. Many per discipline — not a wish-list.
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.
Refunds, credits, account-modifications gated by policy. The agent can process small refunds autonomously and escalate large ones.
Email, chat, voice, in-product — one conversation across channels with consistent memory and policy.
Customer-state-aware tone calibration. The agent treats a frustrated long-tenure customer differently from a first-time inquiry.
Smart routing to human agents with full context, expected outcome, and provenance-tracked reasoning.
Every conversation is recorded, scored against quality criteria, and surfaced for review. Issues are caught at the agent layer, not after the customer complaint.
The depth behind the six workloads above. Grouped by where the work happens — modelling, workflow, signoff, instrumentation. Every line below is a capability that exists in production today, not a roadmap promise.
Severity, routing, SLA management. The model reads the ticket, the customer's history and the product context — in one pass.
Whole-context retrieval over your docs, runbooks and prior tickets. Citations back to the source so the agent can explain why.
Tone-matched, policy-checked replies in your voice. Reviewer agents catch style, accuracy and compliance issues before send.
Tickets likely to escalate flagged early. The model proposes the intervention that resolves them without losing the customer.
Every conversation reviewed against your quality rubric. Coaching themes surfaced for your team leads, not as a top-down audit.
Gaps in the KB detected from real ticket patterns. The model proposes the article; the writer reviews it.
Real-time mood across every conversation. Trend signals for product, not just for individual reps.
Same agent, every language your customers speak. Translation grounded in your product vocabulary.
Aether coordinates these specialised agents for the workloads above. Each is independently deployable with stable contracts, but the coordination is what makes them more than a folder of scripts.
We aren't building a chat surface over your existing tools. We are training a foundation model that does the work — plans the study, picks the method, runs the agents, validates the artefact, writes the report.
Decomposes a one-sentence brief into a DAG of agent calls and posts the plan for your approval before any compute runs.
Knows when one solver suffices and when a more expensive one is required. The judgement of a senior practitioner, encoded.
Dozens of specialised agents execute the plan in parallel. Long runs check-point. Reversible patch sets if anything has to be undone.
Cross-checks every artefact against the regression suite for that discipline: published benchmarks, your historical data, customer-validated studies.
Signed memos with figures, tables and the model-version hash. Ready for design review without rebuilding the deck.
Failed cases enter the eval corpus. Successful pilots become regression tests. The model your next study uses is better than the one this study used.
Not a roadmap. Concrete results we've put in front of customers, on benchmarks you can rerun and on deployments now in production. Each one names the work, the number and where the receipts live.
Median time-to-resolution cut by 38% across a 1,200-agent support org. The agent drafts; the human approves; the customer waits less.
Auto-detected KB gaps from real ticket patterns lifted coverage from 64% to 91% in one quarter.
Every conversation reviewed against the customer rubric. Coaching themes surface as patterns, not as audit findings.
Eight languages added to coverage in the first month — same agent, same memory, no separate model per locale.
Tickets likely to escalate flagged in the first response. Intervention proposed before the customer asked twice.
Customer satisfaction up across surveyed conversations. The improvement compounds as the model learns the team's voice.
We pilot first, always. The scope is one workload, the win condition is named up-front, and the comparison is co-authored with you. If we aren't better on your metric by the end of the quarter, the rest of the quarter is on us.
We sit with your engineers, name the workload that hurts, agree the comparison data, the boundary the model runs inside, and the metric we will be measured on.
Aether deploys into your environment of choice — managed cloud, your VPC, on-prem, or air-gapped. We connect to the data we agreed on; nothing else.
Aether runs the workload in parallel with your incumbent. Every artefact carries the model version that produced it. You see every trace.
We co-author the comparison memo. If we aren't better on the metric you picked, we say so on the same page — and the rest of the quarter is on us.
Production deploy with eval-gated promotion, on-call coverage, change management aligned to your release calendar. The pilot's traces become the regression suite.
The same workloads that make this useful are the ones with auditors, regulators and standing data boundaries. The platform is designed for that — not retrofitted for it.
Managed cloud (SOC 2 Type II), your VPC with private networking, on-prem on your hardware, or air-gapped behind a regulator's boundary. Same runtime, your perimeter.
Customer-VPC and on-prem deployments keep training and inference inside the boundary you set. Air-gapped deployments produce zero outbound traffic by construction.
Immutable per-run traces. GxP / 21 CFR Part 11 / ALCOA+ patterns where the regulation applies. Exportable as OpenTelemetry, SIEM events or JSONL.
Every output is signed by the model version that produced it. Promotion through eval gates is recorded; old versions are reproducible by hash.
Every tool call is gated by an explicit capability the model has to request and your policy has to grant. The refusal corpus is versioned alongside the model.
ITAR-clean compartments, export-control gating, separation of duty. The geometry, the chemistry and the design never leave the boundary you set — period.
Aether is the one product Apex ships. The named surfaces below are how Aether shows up in this discipline — same model, same runtime, different workloads. Each has its own page with depth: coverage, validation, replaces, pilot pattern.
If yours isn't here, send it to hello@apexworldlabs.com and we'll answer it in plain language — usually same day.
The incumbent stack here — Per-vendor support automation suites, Off-the-shelf chatbot platforms, Bespoke ticketing-system integrations, Manual macro-and-template workflows. Aether collapses them into one model and one project file, with Aether for software doing the work under one safety story.
In customer support: 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, not a public benchmark.
A copilot suggests text; Aether does the work. It runs the simulation, drives the instrument, taps out the chip, lands the PR. The artefacts you act on are produced by the model — not by an engineer prompting it for hints.
No. Aether is a foundation model we train ourselves on simulation trajectories, instrument data and design history. We don't call third-party chat APIs as part of the product.
Pilots are scoped to a specific workload and a specific win condition. Pricing is fixed-fee for the scope; if we aren't better on the metric by the end of the quarter, the rest of the quarter is on us.
Yes. Aether reads the formats your team already uses, runs alongside your incumbent stack during the pilot, and integrates with the data system you already trust. We don't expect anyone to throw away ten years of tooling on day one.
Stand-up is typically two weeks once we've agreed scope, data and boundary. Faster if you're already in our supported deployment topologies; slower for sovereign/air-gapped environments where the security review is the long pole.
Send us the workload that hurts. We'll come back with a scoped pilot — three to eight weeks, win condition defined together.