Web data your team can actually build on.
Pyronets builds and runs the extraction pipelines you'd otherwise maintain yourself. You name the sources, the fields, and the schedule: validated records arrive in your schema, on time, without a scraper in your codebase.
Scoped sample and fixed quote before you commit, no retainer to find out whether it's feasible.
Totals across every pipeline we run.
Validation rules per delivery
Type, range, null-rate, drift, and duplicate checks run before anything ships.
Delivery targets supported
Parquet, CSV, JSON Lines, S3, REST callback, or straight into your warehouse.
Fastest refresh interval
From quarter-hourly price polling to monthly full-catalog snapshots.
Maintenance handled by us
Layout changes, blocks, and schema drift are our problem, not your backlog.
Scraping is easy to start and expensive to keep alive.
The first version takes an afternoon. The maintenance takes a headcount. Most teams discover the difference about four months in.
Scrapers break every week
A layout change on a single source silently corrupts a field. You find out when a dashboard looks wrong, usually days later, usually from someone else.
Nobody owns data quality
Raw HTML becomes a CSV nobody trusts. Prices arrive as strings, currencies get mixed, duplicates inflate counts, and every downstream team writes its own cleanup.
It eats engineering time
Proxy rotation, headless browsers, retries, backoff, monitoring. Real infrastructure work that has nothing to do with the product you're actually building.
Typed records and a QA report, every batch.
You agree the schema before we write a line of extraction logic. Every field has a type, a unit, and a null policy, so the first delivery drops straight into your pipeline instead of starting a cleanup project.
- Field types and units fixed in writing up front
- Currency, date, and unit normalization applied per source
- Duplicates resolved against a key you choose
- Coverage and null-rate reported per batch, per field
- Schema drift flagged before it reaches your warehouse
{
"sku": { type: "string", key: true },
"title": { type: "string", nullable: false },
"price": { type: "decimal(10,2)" },
"currency": { type: "iso4217" },
"in_stock": { type: "boolean" },
"seller": { type: "string", nullable: true },
"source_url": { type: "url" },
"collected_at": { type: "timestamp_utc" }
}Illustrative schema and batch metrics from a price-tracking pipeline.
From first call to first delivery.
Scope the dataset
You tell us the sources, the fields, and the cadence. We come back with a schema, a sample, and a fixed price, before you commit to anything.
Typically 2–3 working days
We build the pipeline
Extraction logic per source, normalization into your schema, and the validation suite that guards it. You review a real sample against real records.
Sample delivered for sign-off
Data lands on schedule
Into S3, your warehouse, an API callback, or a bucket you name. Every batch is validated first, and every batch carries a QA report.
Your cadence, your format
We keep it running
Sources change constantly. We monitor, detect drift, and repair extraction before it reaches you. That maintenance is the service, not an add-on.
Monitored continuously
Pipelines by capability, or by dataset.
By capability
By dataset
What you're actually buying.
You talk to the people building it
No account layer between you and the engineers. The person who scoped your schema is the person who fixes it when a source changes.
We tell you when something won't work
If a source is unreliable, a field isn't consistently available, or a request isn't something we'll take on, you hear it during scoping, not after an invoice.
Small on purpose
A four-person team means every project gets senior attention. We take on work we can do properly rather than filling a pipeline.
The maintenance is the commitment
Anyone can deliver a dataset once. We stay responsible for it: monitoring sources, catching drift, and repairing extraction before it reaches you.
Built for data-dependent teams.
Retail & e-commerce
Competitor pricing, assortment gaps, stock levels
AI & research teams
Training corpora, benchmark sets, deduplicated text
Real estate
Listing aggregation, price history, agent coverage
Recruitment tech
Job postings, employer signals, salary bands
Market intelligence
Category tracking, brand presence, share of shelf
Finance & investing
Alternative data, pricing signals, web footprint
Rated by clients who paid for the work.
We could write our own testimonials. Instead, here is a public profile anyone can open and check: 37 contracts, 3.3k+ hours, and a rating awarded by Upwork rather than by us.
Expert Large-Scale Web Scraping & API Reverse Engineering
“I am very satisfied. Communication was excellent and the final product was exactly what I was looking for.”
“The freelancer did a great job and delievered more than promised. I can recommend him to everybody”
“Syed is an outstanding freelancer! He delivered exactly what was promised with great attention to detail and professionalism. Communication was always smooth.”
“Superb freelancer, and I highly recommend hiring him, don't search further this is the one you want to work with!”
“He was great to work with! This was my first Upwork contract, and I would work with him again. Very positive experience.”
“Syed was very hard working and completed my work in no time.”
Reproduced verbatim from the public Upwork profile, spelling and all.
Tell us the sources. We'll send back a sample.
Describe the sites and the fields you need. You'll get a proposed schema, a real sample extracted from those sources, and a fixed price, before any commitment.