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Discipline/data-automation

Data Automation & AI

We build web scrapers and data parsing pipelines that extract clean information without manual cleaning steps.

Our crawlers track 500 product sites daily, and our text parsers process resumes in 250ms.

Scraping & Data Processing

We write data pipelines and crawlers in Python using Pandas and Streamlit. We avoid fragile HTML selectors and instead use pattern-matching regex and layout-aware models to keep scrapers running when websites change.

NLP & Classification

  • Custom text classification models
  • spaCy and NLTK text processing
  • Sentiment analysis and extraction

Data Pipe Standards

Deterministic Runs
We write idempotent jobs. Running the parser twice yields the exact same dataset without duplicate records.
Failure Throttling
We set up user-agent rotation and automatic retry delays so crawlers do not trigger rate limits or IP blocks.
Rapid Inference
We package machine learning models inside lightweight FastAPI servers to classify incoming text under 250ms.

Shipped Work

Competitive Intelligence Dashboard

We built a Python scraping system to crawl competitor prices and track market trends daily.

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NLP for Business Intelligence

We built a Python NLP service to parse candidate resumes and score matching skills in 250ms.

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Zabdoc

We built a Go utility on Cloudflare Workers to parse student grades and generate PDFs in 280ms.

View project

Standards & Protocols

operationsPick tools that disappear and stick with them.

Understand the tools we use, why we chose them, and how we expect them to be used.

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engineeringInstrument your code before you ship it

We set up logging, error tracking, and metrics dashboards for every service before it handles production traffic.

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