AI discovery and value mapping

Find the use case worth building, before anyone writes code.

Most AI budgets are spent on the first idea someone had in a meeting. We map the jobs your business needs done, weigh each against what it would take and what it would pay, and leave you with a ranked list and one pilot you can defend to your board.

See it in our work

At a glance

Length
Two weeks to start
Your time
About six hours, from two or three people
You leave with
A ranked list and a priced pilot
Afterwards
Build it with us or with anyone else

What it covers

What the two weeks cover.

  • Jobs to be done interviews

    We sit with the people who do the work today and ask what they did the week before, not what they would like. The job that keeps coming up is the one worth automating.

  • Value against complexity

    Every candidate is scored on what it saves or earns and on how hard it is to get right in production. The first pilot is the one most likely to pay, not the most impressive.

  • A pilot scope in writing

    One narrow question, the data it needs, how success will be judged, and what stays out of version one. Written down before any build starts.

  • The honest no

    Sometimes the answer is that the data is not there yet, or the job is better done with a rule than a model. We say so, and you have saved a quarter.

How we do it

From the first interview to a priced pilot.

  1. 01

    Two weeks, on site or on calls

    Discovery sessions with operations, finance and whoever owns the outcome. We are looking for the moment work stalls, not for places to put AI.

  2. 02

    A user story map of the current job

    Every step, old and new, drawn out so the first release is a thin slice of the whole and nobody discovers a missing step in month three.

  3. 03

    Feasibility on your own data

    A day or two testing whether your documents, recordings or records can actually answer the question, before anyone commits to a build.

  4. 04

    A ranked list and a first pilot

    You get the candidates, the scores, the reasoning, and a pilot scope with a price. Take it to us or to anyone else.

What you get

Yours to keep, whoever builds it.

  • A ranked list of AI candidates with value and feasibility scores
  • A user story map of the job the first pilot replaces
  • A written pilot scope, including what stays out of version one
  • A feasibility note on your data, with what is missing
  • A fixed price for the pilot

Where we have done it

Real engagements, named.

Questions

Questions we get asked.

How long does discovery take?
Two weeks to start. That is enough to map the job, test your data and scope the first pilot, and it is deliberately short: you are not paying for a long study before anything gets built. After that, discovery does not stop. It runs alongside delivery, because the things we learn about your domain in month two change what is worth building in month three. Continuous discovery is how the pilot stays pointed at the problem that matters.
Do we have to build with you afterwards?
No. The output is yours and it is written so any competent team can act on it. Most clients do continue with us, because the pilot is already scoped and priced.
What if you find nothing worth building?
Then we say so. It happens, and it is a cheaper way to learn it than a failed pilot.
Who from our side needs to be involved?
The person accountable for the outcome, and two or three people who do the work today. About six hours of their time across the two weeks.

What clients say

In their words.

01 / 04

Life Insurance Direct
Before Metricsense, checking calls was a manual process: we selected files for review. Now the platform analyses every call, and we review by risk. It has identified customer experiences we would not have picked up before, and it lets us scale our quality assurance program without increasing headcount, with our team still the human in the loop. It helps us protect our customers, our business and our agents.

Russell Cain

Founder and CEO, Life Insurance Direct

Also under AI engineering

Where it leads next.

All of AI engineering

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