Summarization

Short, faithful summaries in the format your team reads.

Call notes, case summaries and ticket digests follow a house style. A model trained on summaries your team has written learns what to keep and how to say it.

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The problem

There is no single right answer

Two good summaries can differ word for word, so simple matching can't tell you which model is better.

Invented details are costly

A summary that adds a fact the source doesn't contain is worse than a short one.

Every conversation needs one

Summaries are written after every call or case, so volume follows your whole operation.

Where it applies

Call and meeting notes
Support case summaries
Ticket and thread digests
Clinical and case handover notes
Document abstracts

How the switch is checked

Before you switch, the fine-tuned model and the LLM you use today answer the same held-out examples from your data.

An LLM judge you choose compares the two summaries for each example, without knowing which model wrote which
Each pair is judged twice with the order swapped, to cancel the judge's bias toward the first answer
A warning when the judge is the same model as the incumbent, because models tend to prefer their own writing

What you need

Each example's text, and a good summary of it written by a person.

Other use cases

Classification and routing

Structured extraction

Tagging

Redaction

Regulated industries

How the switch is checked

Before you switch, the fine-tuned model and the LLM you use today answer the same held-out examples from your data.

An LLM judge you choose compares the two summaries for each example, without knowing which model wrote which

Each pair is judged twice with the order swapped, to cancel the judge's bias toward the first answer

A warning when the judge is the same model as the incumbent, because models tend to prefer their own writing

What you need

Each example's text, and a good summary of it written by a person.