ATPL

AI · See and predict

Clean, connected data before clever models.

For companies whose numbers live in a POS, an ERP, Tally, sheets and WhatsApp exports that never agree: one pipeline, one warehouse, one set of figures, ready for reports and models.

2014
Centralised cloud reporting since
Describe the problemAll AI capabilities

No single number

Three different numbers for one fact

One clean, connected set of data before any model is built on it.

  • 01 · Sheets

    Sales, stock and accounts each give a different number for last month, and the meeting argues about which is right.

  • 02 · Conflict

    The monthly report takes a week to assemble from exports.

  • 03 · Late

    An AI vendor asked for your data and you realised it was in nine places.

How it ships

Sources, then schedule

  1. pipe/01

    Understand

    We trace one number, such as last month’s revenue, back through every system that touches it and document where the versions diverge.

  2. pipe/02

    Design

    We agree the master definitions, the keys that join sources, the refresh frequency and who owns each table; the tools are chosen after that.

  3. pipe/03

    Build

    Pipelines are built source by source, and each one is reconciled to the source system’s own totals before the next is started.

  4. pipe/04

    Stay

    Quality checks run on every load, schema changes in a source are caught by tests, and we add sources as the business adds systems.

Pipelines you own

One place for the truth

  1. 01

    Source inventory

    Every system, sheet and export that holds data, who owns it, how it is keyed, and what it disagrees with.

  2. 02

    Pipelines

    Scheduled jobs that pull from each source through APIs, database access or exports, with retries, logging and an alert when a source goes quiet.

  3. 03

    A warehouse you own

    One database, in your cloud account, with a documented schema and the history kept, so a question answered today can be answered the same way next year.

  4. 04

    Data quality checks

    Tests that run on every load: missing days, duplicate IDs, totals that do not match the source, so bad data is caught before it reaches a report.

  5. 05

    Reporting layer

    Clean tables that a dashboard tool, a spreadsheet or a model can read without re-doing the joins.

Your data

The warehouse and pipelines live in your cloud account or on your server, under your credentials, and the schema is documented as yours. No data is sent to a model provider in the course of data engineering. Access is granted per table, and every load is logged.

Pipeline or sheet

Where it fits, where it doesn’t

  • Fits when
  • More than two systems that should agree and do not.
  • Reports that are rebuilt by hand every month from exports.
  • Any AI or forecasting plan; the model is only as good as the pipeline under it.
  • Doesn’t fit when
  • One system, one sheet. If everything is already in one place, build the report, not a warehouse.
  • Businesses not ready to name an owner for each data source; pipelines without owners rot.
  • A dashboard deadline next week; data engineering done properly takes longer than that and we say so.

How we work

ATPL has run centralised cloud reporting with database synchronisation across every Ethos terminal since 2014; the same pipeline discipline is applied here. ATPL will confirm which data engineering projects may be named.

    Chosen for the job

    Python · Node.js · PHP

    Decide the shape

    Before you start

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

    Data engineering is the work of pulling data from every system a business runs into one clean, documented, regularly refreshed store. It comes before AI because a model trained on nine disagreeing sources learns the disagreements; ATPL starts most AI projects by fixing the pipeline first.

    Through scheduled pipelines that read each system the way it allows: Tally through its export or XML interface, a POS through its database or API, Shopify through its API. ATPL builds one pipeline per source, reconciles it to that system’s own totals, and loads it into a warehouse you own.

    Only when more than two systems have to agree. A single shop can report from its own POS database; once sales, purchases, stock and online orders live in different systems, a small warehouse in your own cloud account is the cheapest way to stop re-doing the joins by hand. ATPL sizes it to the business.

    Four to ten weeks for the first three or four sources, most of it spent on reconciliation and definitions rather than code. ATPL delivers source by source, so the first clean report arrives within the first weeks, not at the end.

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