"We're Data-Rich but just drowning in reports and tables of data, without any insights."
Simple, Tangible & Actionable Data-Analytics are the Core of Our Business.
"We do XYZ Process this way because that's the way we've always done it."
Straightforward, best-practice Automations & Procedural Improvements.
"We're looking to use AI, but are unsure where it makes sense in our business."
AI Training, Use-case definition and Simple AI Text Parsing Projects.
Simplicity
Predominantly taking system exports from Accounting/ERP Software to deliver Superior Reporting & Analytics.
CRM/ERP Exports, with Historical Database Enrichment. And Web-Scraping for New Contacts sometimes.
Approach of One-Source-of-Truth, with different views of the Same Data.
Very Dependant on the Business/Industry. Operational Non-Financial Analytics is a Broad Topic.
Dealing with Messy Data is part of the Job Description. No Business is aware of just how much of their data needs cleaning.
The majority of Automation, Large & Small. Come from assessing the Spreadsheet Related Tasks & Workflows within a Business.
Establishing, Improving & Simplifying Procedural Docs. Related to our Data-Processing Automations.
Processes across things like
We bridge the gap between automating the Active Operational Data and the High-Level Analytics & Reporting Data.
Alot of common business processes are triggered via inbound Email. (With or Without Attachments)
For Example
1. When a Work-Order is Raised, an Email is sent to the Contractor & Manager is CC'd.
2. Automatically Sort the email to a 'WO-Raised' folder. Which kicks off other processes.
3. Scrape fields from the email or attached PDF. Fields like 'Contractor', 'WO-Num', 'WO-Raised Date' 'Urgency Level'.
Save attachment to a folder in SharePoint.
4. Add Row to Outstanding work-order's table.
5. If 'Days Since Raised' is > 7 AND Status shows no quote received. Then generate a draft email to the Contractor requesting a quote.
Outside of Core Automated Reporting & Active Operational Data, some common themes emerge.
1. Scraping fields from Structured & Semi-Structured PDF's/Emails.
a. Structured PDF's can be Parsed via Python scripting.
b. Semi-Structured PDF's require AI Pre-Processing and/or Abstraction.
It's a spectrum, Data.
Highly Structured VS Unstructured
Same applies to Database operations in the future.
ie, "Hi chatGPT, can we pls get XYZ Report"
2. Web-scraping for CRM or other data-gathering requirements.
3. Template Document Design & Population.
Start Small, Keep it Simple, Practical & Low-Risk.
The Concept of a Large-Language-Model (like ChatGPT) has only been around since 2017.
And most businesses only really heard about & begun using AI from 2022/'23 onwards.
Many businesses still lack a unified understanding of:
Strong Focus on Practical Outcomes from Agentic AI Workflows.
Custom LLM API Calls, for Processing and Abstracting over Semi-Structured Text.
© 2025 Automated Data Pipelines Pty Ltd (ABN 16 687 204 691). All Rights Reserved.