ATPL

AI · Start right

Prove the idea in weeks, on your own data.

For teams with one AI idea and no appetite for a year-long bet: a fixed-scope build on your real data, scored against a target agreed in advance, so the go or no-go is a fact.

Describe the problemAll AI capabilities

Brief to proof

A brief becomes a go / no-go

The success criteria and the stop line — a PoC you can kill without regret.

What a client typed

We think a model could flag which of our customer complaints are about a product defect versus delivery or billing, so the right team sees them first, but we want to know whether it actually works on our tickets before we commit to anything.

Brief ready

What it becomes

Problem
Complaints are routed by hand and reach the wrong team first.
Scale
A ticket history of some thousands of complaints with the team that finally resolved each one.
Systems
The support ticket system or helpdesk inbox, and the team mailboxes.
First step
A four-week proof of concept: classify the last year of tickets, score against the actual resolving team, decide.

PoC or product

Where it fits, where it doesn’t

A proof is not a product. Use this page when you need a decision, not a roadmap slide.

  1. Fits · 01

    One clear idea with a number that would settle it: accuracy, hours saved, matches found.

  2. Fits · 02

    Real data available within the first week, including the messy cases.

  3. Fits · 03

    A sponsor who will act on the result either way.

  4. Skip · 01

    Ideas with no data yet; collect first, then prove.

  5. Skip · 02

    A proof of concept meant to impress rather than decide; we do not build demos for board meetings.

  6. Skip · 03

    Problems already solved by a rule or a report; we will say so in the first week and stop.

Your data

Your data stays in your environment, or in a sandbox created in your cloud account and deleted at the end, as you choose. Model calls, where used, carry only the records needed under terms that exclude training. The prototype code and the results report are yours whether the answer is go or no-go.

Prove it cheap

Prove it before a full build

A working test of one AI idea on your data, with a measured result, in weeks.

Describe the problem

  • Demo

    A vendor demo worked on their sample data; you have no idea whether it works on yours.

  • Data

    You cannot sign off a large budget on a hunch, and the team cannot move without it.

  • Stop

    The last pilot ran for eight months and ended without anyone able to say whether it worked.

A go / no-go

A time-boxed proof on your records

  • Data access and sample

    A representative slice of your real records, including the ugly ones, prepared with your team in the first days.

  • A working prototype

    Enough of the system to run end to end on real cases: not a slide, not a notebook, a thing a user can try.

  • Measured result

    The prototype scored against the target on held-out cases, with the misses examined and explained.

  • Go, no-go, or change

    A short written recommendation: build it, drop it, or what would have to change for it to work, with the production estimate if it is a go.

Primary deliverable

Ships with the engagement

Live brief

01 · Lead outcome

A one-page scope

The question, the data, the target number, the deadline, and what we will not build; signed before work starts.

  • 01Data access and sample
  • 02A working prototype
  • 03Measured result
  • 04Go, no-go, or change

How it ships

Scope, build, decide

  1. poc/01

    Understand

    We spend the first days turning the idea into one question with a number attached, and if that cannot be done the proof of concept should not start.

  2. poc/02

    Design

    We pick the smallest system that can answer the question on your data and set aside test cases the build never sees.

  3. poc/03

    Build

    Three to five weeks of building, with a working version in front of your team by the end of the second week.

  4. poc/04

    Stay

    If it is a go, the prototype becomes the first milestone of the production build rather than being thrown away and restarted.

How we work

ATPL scopes every proof of concept with a target number and a deadline, and reports the misses as plainly as the hits. ATPL will confirm which proofs of concept may be named.

    Chosen for the job

    Python · Next.js · Node.js

    Decide the shape

    Before you start

    Plain answers on scope, data and what a first proof usually takes.

    An AI proof of concept is a short, fixed-scope build that tests one idea on a company’s real data against a target agreed in advance, ending in a measured result and a go or no-go recommendation. ATPL runs them in three to five weeks, with a working version in front of the client by the second week.

    Three to five weeks at ATPL, scope signed first: a few days to turn the idea into a measurable question and gather data, then building, then scoring against held-out cases. A proof of concept that cannot be scoped to a number in the first week is stopped before it costs more.

    The prototype becomes the first milestone of the production build rather than being discarded: ATPL hardens it, adds permissions, logging, fallbacks and monitoring, and runs it in shadow mode before it takes over. The production estimate is part of the proof of concept report.

    No. A proof of concept answers "does this work on our data?"; a minimum viable product answers "will people use this?". ATPL’s AI proofs of concept measure accuracy or hours saved on real cases, and only a go result leads to a product build.

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