Insight Analytics
Service

Python Automation

When the job doesn't fit a drag-and-drop tool, we write Python the way a software team would — tested, logged, and scheduled.

There's a version of Python automation that's a notebook on someone's laptop, and a version that's a packaged service with tests, retries, structured logging, and alerting. We build the second one, because the first is how companies end up with silent data failures.

Typical work: pulling data from APIs that don't have a connector, transforming files that arrive in inconsistent formats, scoring records with a model, generating and distributing documents, or gluing systems together that were never meant to talk.

Everything ships with a README, environment configuration kept out of the code, and a scheduler — Airflow, Prefect, cron, or a cloud function, whichever suits your stack.

Problems this solves

What we usually walk into

  • A critical script runs on an analyst's machine and fails when they're away.
  • An API has no off-the-shelf connector, so someone downloads CSVs by hand.
  • Jobs fail quietly and stale data reaches the dashboard.
  • Credentials are hardcoded in a script sitting in a shared folder.

Technologies

What we build with

PythonpandasAirflow / PrefectFastAPIDockerAzure Functions / AWS Lambda

Industries that benefit

  • E-commerce
  • Logistics
  • Financial services
  • Healthcare
  • Technology

Implementation process

How the engagement runs

  1. 01

    Scope and interface design

    Define inputs, outputs, failure behaviour, and who gets alerted when something breaks.

  2. 02

    Build with tests

    Unit tests on the transformation logic and fixtures for the awkward edge cases in your real data.

  3. 03

    Hardening

    Retries with backoff, idempotent writes, secret management, and structured logs.

  4. 04

    Deployment

    Containerised and scheduled in your environment, with a rollback path.

  5. 05

    Monitoring and handover

    Alerting on failure and staleness, plus documentation your team can pick up.

Sample screens

What the finished work looks like

Representative layouts using demonstration data — client work is never shown without written permission.

Pipeline run monitor

Jobs

42

Uptime

99.8%

Avg

3.4 min

Pipeline run monitor

Every job with duration, rows processed, and retry count.

API ingestion volumes

Sources

11

Rows/day

8.4M

Alerts

0

API ingestion volumes

Records pulled per source with schema-drift alerts.

Model scoring split

Scored

126K

Precision

0.91

Drift

low

Model scoring split

Distribution of scored records by outcome band.

Illustrative example

How a python automation engagement typically plays out

Anonymised scenario · not a verified client record

Direct-to-consumer brand, 4 sales channels

Challenge

Someone downloaded order exports from four marketplaces every morning, pasted them into a master file, and reconciled fees by hand. If they were sick, the numbers stopped.

Solution

A Python service that pulls each marketplace API on a schedule, normalises fees and refunds into a single order model, writes to the warehouse, and alerts on schema changes or missing days.

Result

The morning routine disappeared entirely and channel profitability is now available before the team logs on.

Illustrative figures

90 min/day

Manual work removed

4

Channels unified

6am

Data ready daily

This is a composite illustration of the scope, approach, and range of results this service is designed to deliver. It does not describe a specific named client, and the figures are demonstration values rather than audited outcomes. We're happy to talk through real references under NDA on a call.

Deliverables

What you receive

  • Source repository with tests and CI
  • Containerised deployment configuration
  • Scheduler definitions (Airflow DAG, cron, or function trigger)
  • Secret management and environment configuration
  • Runbook covering failure modes and recovery

FAQs

Questions we get asked

Who owns the code?
You do, outright, in your repository from day one.
Can you take over an existing script?
Yes. We usually wrap it in tests first, then refactor, so behaviour is preserved while it gets safer.
Do you do machine learning?
Practical ML — forecasting, classification, scoring — with honest expectations about what your data volume supports.
Where does it run?
Your cloud, your on-premise server, or a managed scheduler. We don't require hosting with us.

Related services

Often scoped alongside this

API Integration

Connect CRM, ERP, and SaaS into one clean source of truth.

View api integration

AI Automation

LLM agents that draft, extract, classify, and triage.

View ai automation

ETL Development

Reliable ingestion pipelines from any source to any target.

View etl development

Talk through your Python Automation project

A 30-minute call is usually enough to tell you whether this is a two-week fix or a two-month build — and roughly what it costs.

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