Case study Concept build

Calder & Rowe

AI-drafted replies with a human sign-off, for an insurance broker. A concept where routine client emails are drafted by AI from the broker’s own policy documents, and every reply is approved by a person before it is sent.

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The code, from day one
Type
Concept build (fictional brand)
Industry
Finance and insurance
Services
AI & automation
Calder & Rowe: the finished build
Calder & Rowe: 3D cover illustration for this concept build
Concept build

A concept build: a fictional brand we designed to show what this looks like. It is not a client engagement and the numbers are modelled, not measured.

The challenge

What needed solving.

Account handlers spend much of the day answering the same questions about cover and renewals, retyping details found in policy documents.

The approach

What we did.

Each step below is something you can see and approve before the next begins.

  1. Draft from the documents

    The assistant reads the policy and proposes a reply, citing the clause it relied on.

  2. Human approval always

    Nothing goes to a client until a handler has reviewed and approved it.

  3. Corrections become training

    Every edit a handler makes improves the next draft.

  4. Audit trail

    Who drafted, who approved and what was sent are recorded.

What was built

The deliverables.

  • Policy-grounded reply drafting
  • Human review screen
  • Citation of source clauses
  • Edit-learning loop
  • Audit log

Stack

  • LLM
  • Document index
  • Review UI
  • Email integration

Worked example

time returned to handlers

Worked example

Assumptions

Routine replies per day
30
Minutes saved per reply by drafting
5
Working days per year
230
Loaded cost per hour
£22

The arithmetic

  1. Minutes saved per day30 × 5 = 150
  2. Hours per year150 × 230 ÷ 60 = 575
  3. Value per year575 × £22 ≈ £12,650

Modelled from the assumptions shown. Actual time saved depends on the mix of queries and how much review each draft needs.

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