01
Reliable data extraction and browser automation
Service 08
Data & Scraping
Structured public data collection for monitoring, research, and decision support.
Public web data can be strategically useful for research, monitoring, comparisons, and operational awareness, but only when it is collected responsibly, structured properly, and maintained as a real pipeline rather than a one-off script.
Decision-making focus
A clearer engagement around the business problem, the current setup, and the smallest workable change that still improves the system.
Problems solved
Reliable data extraction and browser automation
Normalization, enrichment, and change monitoring
Structured outputs for reporting and analysis
Core outcomes
What this service is designed to improve.
The work is structured around delivery outcomes that are easier to understand, scope, and act on than a generic feature list.
02
Normalization, enrichment, and change monitoring
03
Structured outputs for reporting and analysis
What this work covers
Public web data can be strategically useful for research, monitoring, comparisons, and operational awareness, but only when it is collected responsibly, structured properly, and maintained as a real pipeline rather than a one-off script.
What this service covers
Scraping and data mining are useful when a business needs better visibility into changing public information, competitive environments, catalog differences, or market signals.
I help design collection pipelines, define what is worth extracting, set quality expectations, and shape the resulting data into something practical for reporting, monitoring, or downstream analysis.
The work can include extraction design, browser automation, anti-fragile scraping patterns, data normalization, enrichment, change monitoring, and using LLM-supported classification or structuring where that genuinely reduces manual review.
Typical outcomes
- cleaner extraction pipelines for public data
- repeatable monitoring of changes across sources instead of brittle manual checks
- more useful structured datasets for internal analysis and reporting
- stronger visibility into market, pricing, catalog, or content signals
- less cleanup work caused by fragile scripts and inconsistent output formats
Typical fit
This service is relevant when teams need structured public data, recurring monitoring, or market intelligence and do not want brittle one-off scripts that fail quietly and create cleanup work later.
Relevant reading
Blog posts that support this service.
Selected from the archive based on the service topic, outcomes, and the blog categories most closely tied to this work.
How to Build a Scraping Pipeline With Playwright, HTTPX, and Pandas
Playwright, HTTPX, and Pandas form a practical scraping pipeline when the data source needs both browser automation and clean analysis.
When Prefect and Polars Are Better Than a Cron Job
Prefect and Polars are worth it when a data workflow has retries, dependencies, and analysis work that should not live in a cron script.
How to Build a RAG Pipeline with PostgreSQL and pgvector
A practical RAG architecture using PostgreSQL, pgvector, embeddings, and a model that answers from retrieved context.
Next step
If Data & Scraping looks close to the current bottleneck, start with context.
Share what the team is building, where delivery or operations are getting stuck, and what constraints already exist. The goal is to turn that into the clearest first move instead of a vague engagement.