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Data Engineering & Machine Learning
ETL pipelines and models that retrain on your own data, without anyone pressing "run".
What it is
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We design automated ML pipelines. Models that train and retrain on your actual data (financials, invoices, customer behavior) without someone having to manually hit "run" every time. We build ETL pipelines that pull from SQL databases, CSV exports, third-party APIs. Clean it. Normalize it. Feed it into dashboards, reports, or ML models.
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What this looks like built.
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Data
Automated Tabular ML for Financial Data
Feed your last 3 years of financial data into a system that learns the patterns. It automatically predicts next month's revenue, flags late-paying customers, and forecasts cash flow. The model retrains as new data comes in.
Machine learning models that train on structured data (CSVs, SQL exports, financial records) and produce predictions on a recurring schedule.
ForFinance departments, CFOs, operations managers.
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Data
Data Pipelines (Collection, Aggregation, Cleaning)
Every morning, the system connects to every source (HubSpot, QuickBooks, spreadsheets), pulls the latest data, reconciles it, and produces a clean dataset ready for dashboards. It runs whether you're in the office or on vacation.
Automated systems that pull data from multiple sources (databases, APIs, spreadsheets), clean and normalize it, and deliver it in a consistent format.
ForCompanies with data scattered across multiple systems.
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Data
API Aggregation (Bridging Old Tools to Modern Reporting)
Instead of replacing a 15-year-old system with no modern API, we build a custom API layer on top of it. You get modern analytics in Power BI without a rip and replace disaster.
Custom built API endpoints that connect legacy systems to modern reporting and analytics platforms.
ForCompanies with legacy systems or niche industry software.
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Where it fits.
Work from the industries this service is built for.
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Often paired with.
ATDT XIOSCONNECT 28800
Let’s build something.
Tell us what’s slowing the business down. We’ll tell you what we’d build.
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