Illustrative scenario · the operator · AI

An AI operations agent handling 60% of dispatch queries

A retrieval-grounded agent wired into dispatch tooling, with human checkpoints on every write action.

AI opportunity audit
RAG pipeline
Agent workflow
Evaluation harness
Production deployment

Illustrative scenario.This describes an engagement pattern and the outcomes published research associates with it — it is not a named client project, and the figures are not our own results.

Industrial warehouse interior with organised shelving

Typical outcome range — see sources below

0%
Queries resolved autonomously
0%
Evaluated answer accuracy
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Median response latency

01The challenge

Operations coordinators spend hours each day answering repeat questions about routing rules, customs paperwork and carrier SLAs — all documented somewhere, none of it findable at the moment it is needed.

02Our solution

We built a retrieval pipeline over 18,000 internal documents with citation-backed answers, then layered an agent that can draft carrier emails and update shipment records behind explicit human approval.

03The result

Sixty percent of inbound coordinator questions now resolve without a human, with answer accuracy held above 94% by a continuously-run evaluation suite.

Before / after

Drag to see what changed

Coordinator hours on repeat queries

After

1.3/day

Before

4.0/day

Coordinator hours on repeat queries

What the research shows

Gartner puts containment for a well-configured RAG assistant at 40–65% of routine queries.
Gartner, 2025
Forrester found strong RAG architectures reached 40–55% ticket deflection within 90 days of launch.
Forrester Total Economic Impact, 2025

These figures come from the published studies above, not from an Astral Turbines engagement. They indicate what this approach typically achieves.

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