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90% fewer model calls, and more accurate.*

3,000 customer-operations decisions: refunds, account unlocks, credit limits, payouts. Each has one right action out of about nineteen, or a refusal, and five times along the way the company changed a rule. Every approach used GPT-5.5; Duplex learned as it went.

See it happen →Every result

Internal benchmarks. Every figure on this page comes from tests Vareon ran itself and has not been independently verified. Read the disclaimer

  • 94.6%*Accuracy, the highest in the testThe model alone 59.8% · with a cache 77.3%
  • −90.3%*Model calls−84.8% cost · −81.9% latency
  • ~23×*Faster once learned≈97 ms against ≈2,231 ms

* Figures from Vareon’s internal benchmarks, not independently verified. Read the disclaimer

The longer it runs, the less it asks.

In its first 300 decisions, Duplex answered 73% itself and asked GPT-5.5 for the rest.*

Step 1 of 4: The start

3,000 decisions, and where each one went.

Answered by Duplex itself, passed to GPT-5.5, or refused.

Step 1 of 4: Arrive

3,000 decisions arrive: refunds, unlocks, credit limits and payouts, in more than 2,000 phrasings.*

After a rule changed, who gave the new answer.

Of every 100 decisions that a rule change had touched.

Step 1 of 3: The model alone

GPT-5.5 cannot know a company’s private rule change: it gave the old answer 97% of the time.*

The longer the stream, the better the numbers.

The same work on a 300-decision stream and on a 3,000-decision stream.

Step 1 of 4: Accuracy

Duplex was 85.0% accurate on a 300-decision stream, and 94.6% accurate on 3,000.*

The bill, and the wait.

GPT-5.5 alone against GPT-5.5 with Duplex, on the same 3,000 decisions.

Step 1 of 3: Per decision

Every 1,000 decisions cost $0.13 with Duplex, and $0.82 with GPT-5.5 alone.*

How it was run.

The work
Customer-operations decisions: refunds, account unlocks, credit limits, payouts, access and fraud escalation. One right action out of about nineteen, or a refusal.
The stream
3,000 decisions in more than 2,000 phrasings, 68% of them worded in a way not seen before, with new kinds of request arriving midway.
Rule changes
Five in-house rule changes, each flipping the right action for a kind of request; 1,073 decisions came after a change.
Feedback
The right answer was revealed for about one decision in ten, the same ones for every approach.
Models
GPT-5.5, with real API calls, for every approach.
Compared with
GPT-5.5 alone, and GPT-5.5 with a semantic cache at its best setting; on the 300-decision stream, also a cache with retrieval and a knowledge graph.
Kept equal
The same model, feedback and refusal rules for every approach.

What it means for your work.

  • Your bill shrinks as it learns.

    Duplex answered 86% of the decisions itself: 90% fewer model calls and 85% lower cost.*

  • A rule change reaches every answer.

    After the rules changed, Duplex gave the new answer 92.5% of the time; GPT-5.5 alone, 2.3%.*

  • It gets better with volume.

    From 300 decisions to 3,000, accuracy rose from 85.0% to 94.6% and the cost per decision fell by two thirds.*

Learns and unlearns →
Next: More accurate at every setting, at a seventh of the cost.Read it →

See it on your own work.

From your own assistant to every agent your company runs, a pilot puts Duplex on your own work, with the measures agreed before it begins.

Start a pilot →Every result

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