Personalization.
Real-time recommendations and next-best-action from your interaction data, served at low latency.
Recommendations, ranking and next-best-action from your own interaction data, served in real time — with cold-start handling and A/B-ready outputs, on infrastructure that sits next to your data.
- Category
- Applied AI
- Deployment
- Managed → air-gapped
- Governance
- IAM · encryption · audit
Three steps to running.
Send user events; the model learns preferences as they arrive.
Low-latency recommendations and next-best-action per user and context.
A/B test against your current approach and measure lift.
Personalization, in full.
Low-latency recommendations per user and context.
Sensible results for new users and items.
Rank actions, not just items, against an objective.
Built to A/B test against your current approach.
Provision it in a few lines.
Every service is reachable from the same SDK, CLI and infrastructure-as-code — one identity, one bill, one audit trail across the whole catalog.
# Provision personalization on Aether Cloud
aether applied-ai create \
--service personalization \
--name app \
--region us-1 \
--deploy managed # or vpc | on-prem | air-gappedAt a glance.
- Latency
- Real-time
- Cold-start
- Handled
- Output
- Recs + next-best-action
- Experiments
- A/B-ready
- Data
- Yours, in-boundary
Built for real work.
Product recommendations
Content personalization
Targeted offers
On one model, not stitched together.
The usual stack runs personalization in one product, the model in another and the data in a third — and the seams between them are the cost. Aether Cloud runs it on the same platform that serves the model, governs your identity and deploys into your boundary, with the rest of the catalog one hop away.
No stitching a vector DB to one place, a warehouse to another and a model to a third — personalization sits next to the rest of the catalog, one identity, one bill.
The provider that runs Aether runs your personalization — so the data and the model never leave the same governed boundary to talk to each other.
Need a capability that isn’t here yet? The model writes and deploys it into the same boundary — the catalog is a starting point, not a ceiling.
Good to know.
Real-time recommendations and next-best-action from your data — served next to the model and your data, with cold-start handled.
Cold-start handling gives sensible results before behaviour accumulates.
Yes — it's built to A/B test against your current approach.
Your boundary, your choice.
Pairs well with.
Detect, classify and read images and video through one API — grounded in the multimodal model, not a fixed label set.
Speech-to-text and text-to-speech in one service — transcription, diarization and natural voices, on the model that reasons over what's said.
Real-time and batch translation across 100+ languages, with glossaries and the context the model carries — not phrase-book substitution.
Turn documents into structured data — forms, tables, invoices, contracts — grounded in the model, not brittle templates.
Time-series forecasting as a service — demand, capacity, load — with uncertainty intervals and what-if scenarios.
Run Personalization on Aether Cloud.
Real-time recommendations and next-best-action from your interaction data, served at low latency. Deployable managed, in your VPC, on-prem or fully air-gapped — talk to us about the configuration your workloads and your boundary require.