ATPL

AI at ATPL

AI that does a job, not a demo.

From agents and document AI to consulting and operations. Every page says where the capability fits, where it does not, and what happens to your data.

Start a projectSee the work
16
Years building technology
Derived from the start year
2014
Centralised cloud reporting since
Company history timeline

01

Fifteen capabilities

Build · See · Start

Hover or focus a row to see where it fits. Every page also says where it does not, and what happens to your data.

Build with AI

  1. 01AI agentsSoftware that plans, acts and reports
  2. 02Generative AIDraft, summarise and answer at scale
  3. 03Knowledge assistantsAnswers from your own documents
  4. 04ChatbotsSupport and sales conversations, automated
  5. 05AI integrationAdd AI to systems you already run
  6. 06Workflow automationRemove the repetitive steps

See and predict

  1. 07Computer visionCameras that notice what matters
  2. 08Predictive analyticsForecast demand, stock and risk
  3. 09Machine learningModels trained on your data
  4. 10Document AIRead invoices, forms and contracts
  5. 11Data engineeringClean pipelines before clever models

Start right

  1. 12AI consultingDecide where AI pays off first
  2. 13Proof of conceptTest the idea in weeks
  3. 14Model customisationTune models to your domain
  4. 15AI operationsMonitor and improve in production

02

How we approach this

Approach

ATPL has built business systems since 2010 and applies the same discipline to AI: one job at a time, tested against real cases, supported after launch. Named AI work is published here as clients confirm it.

  • 01

    Scoped to one job first, so the first release is small and real.

  • 02

    Source code in a repository you control from day one.

  • 03

    The same engineers support what they built after launch.

03

Questions people ask

FAQ

The questions asked before buying, answered plainly. If yours is not here, ask it on the contact page.

ATPL’s AI practice covers building with AI (agents, generative AI features, knowledge assistants, chatbots, integration and workflow automation), seeing and predicting (computer vision, analytics, machine learning, document AI and data engineering) and starting right (consulting, proofs of concept, model customisation and AI operations).

A business is ready when a repeatable job costs real hours, its inputs already exist digitally, and someone can judge whether the output is right. If the data is on paper or the process changes every week, start with data engineering or workflow mapping instead.

It can be, with the right set-up: the data stays in infrastructure you control, model calls go to a provider under terms that exclude training on your data, and the system logs what it did. Every ATPL AI page has a “Your data” section that states this for that capability.

ATPL does not publish a price list for AI work. The cost depends on the job and its volume, so the quote follows a conversation about both, and work normally starts with a small proof of concept on your own data before anything larger is priced.