Applied AI studio · est. 2021

We build the AI systems that quietly run your business.

Not demos. Not pilots that die in a slide deck. Aether designs, builds and operates production AI — retrieval pipelines, agentic workflows and decision models that ship in weeks and hold up under real traffic.

See shipped work
Systems in production
0
Median time to launch
0wks
Ops cost removed
$0M
Northwind Logistics Kestrel Health Ardent Financial Lumen Retail Group Sable Insurance Verde Energy Harrow Legal Pinecrest Manufacturing

Four things we do exceptionally well.

Every engagement starts from a business constraint — a cost line, a backlog, a churn driver — and works back to the smallest system that can move it.

Retrieval & knowledge systems

Grounded search over the documents your team already has — contracts, tickets, specs, transcripts. Hybrid retrieval, re-ranking, citation-first answers, and permission-aware access that mirrors your existing org structure.

  • Hybrid BM25 + dense retrieval with re-ranking
  • Row-level permissions inherited from source systems
  • Answer quality tracked against a versioned eval set
Retrieval systems

Agentic workflows

Multi-step processes where the model plans, calls tools, and hands off to a human at the exact point where judgement matters. Deterministic where it must be.

Agent design

Decision models

Forecasting, scoring and routing models embedded into the tools your operators already use. Explainable outputs, drift monitors, and a retraining schedule.

Modelling

Platform & operations

The unglamorous half: model gateways with cost controls, prompt versioning, evaluation harnesses, tracing, red-team suites and on-call runbooks. This is what keeps an AI feature alive in year two.

  • Unified model gateway with per-team budgets
  • Prompt & config versioning tied to git
  • Trace-level observability and replay
  • Automated regression evals on every change
  • Cost dashboards per feature and per tenant
How we operate

Northwind cut claims intake from nine days to four hours.

A freight insurer was processing 3,100 claims a month through a queue of twelve adjusters and a shared inbox. We built a document-intake agent that classifies, extracts and pre-fills the claim record, then routes only genuine edge cases to a human — with every field traceable back to the source page.

Cycle time
0% faster
Straight-through
0%
Extraction accuracy
0.0%
Read the full case study
claim #NW-88214 auto-approved
PolicyholderVerde Logistics Ltd
Incident date14 Mar 2025
Extracted amount£18,420.00
Policy matchNW-CMR-2024/1187
Confidence0.986
Sources3 docs · 11 spans

Cited spans

“…repair estimate issued by Ashford Commercial, total £18,420 inclusive of VAT…”

“…incident occurred on 14 March 2025 at approximately 07:40…”

Redacted extract from the live adjuster console.

Three phases. No black boxes.

You see the eval scores, the cost per request and the failure cases before anything goes live. If the numbers don't support shipping, we say so.

  1. Constrain

    One week. We map the workflow, instrument the current baseline, and agree on a single number that defines success. Deliverable: a scoped brief with a pass/fail threshold.

  2. Build & measure

    Two to five weeks. Working software every Friday, evaluated against a held-out set. You get the eval dashboard, not a status update.

  3. Operate

    Canary rollout, drift monitoring, on-call rotation and a quarterly re-eval. We stay attached until your team owns it — usually 3–6 months.

Read the full operating model

Tell us the number you need to move.

Twenty minutes, no deck. We'll tell you honestly whether AI is the right lever — and what it would take.

Other ways to reach us