Simple, transparent process from brief to live data
No black boxes. Every step is visible, every decision is explained, and every delivery comes with a quality report. Here's exactly how we work.
A scheduled delivery, from window open to landed.
- 06:00Collection window opens
- 06:41Extraction complete
- 06:52Validation suite passed
- 07:00In your bucket
Typical Project Timeline
Discovery Call
30-minute call to define data sources, fields, frequency, and delivery format.
Sample Dataset
We build and deliver a real sample from your target sources for your review.
Pipeline Live
Full production pipeline built, tested, and delivering on your schedule.
Maintained & Monitored
We monitor pipelines daily and fix any issues before they affect your data.
The Complete Pyronets Process
Seven stages from first contact to live, maintained data pipeline.
30-min call to define data needs, sources, fields, frequency, and format.
We audit your target sites: structure, dynamic content, anti-bot measures, login requirements.
We build a real sample from your actual sources: delivered in 3–5 business days.
Production-grade scrapers built with anti-bot bypass, JS rendering, proxy rotation.
48 automated checks: schema, completeness, dedup, range, freshness.
Data delivered to your endpoint: CSV, JSON, API, SFTP, S3, or database.
We monitor pipelines 24/7, detect site changes, and fix scrapers proactively.
From brief to approved schema in days
The first four steps establish requirements, validate our approach, and give you full control over what gets built.
Discovery Call
We start with a 30-minute call to understand your data goals, target sources, delivery requirements, and timelines. No lengthy questionnaires: just a direct conversation.
Define Target Sites & Fields
Based on the discovery call, we produce a structured brief listing every website, data field, crawl frequency, and output format. You review and approve it before any work begins.
Build Sample Dataset
We extract a representative sample (typically 500–2,000 records) from the agreed sources so you can validate the quality and structure before committing to full production.
Approve Schema & Format
You review the sample data, request any field adjustments, and sign off on the final schema. This locks in the output format and prevents scope drift during build.
From approved schema to production pipeline
Once the schema is locked, our engineering and QA teams take over. Here's what happens before any data reaches you.
Build & Test Scrapers
Our engineers build extraction scripts tailored to each target site: handling dynamic JavaScript rendering, pagination, login sessions, and anti-bot systems. Each scraper is tested against 500+ records before it goes live, and every data field is validated against the agreed schema.
Run QA Checks
Before any delivery, data passes through 48 automated validation checks: field completeness, deduplication, format validation, outlier detection, and cross-source consistency. Anything that fails is investigated and repaired before the batch ships, and you get told what happened.
First Delivery
Your first dataset is delivered to the agreed endpoint: S3, SFTP, API, database, or file format. You'll receive a data delivery report alongside it, including record counts, QA pass rates, and field coverage statistics.
Ongoing Monitoring & Maintenance
Post-launch, your pipelines are monitored continuously. We handle site changes, IP rotation, infrastructure scaling, and schema evolution automatically. Monthly reports give you full visibility into delivery performance, uptime, and data quality scores.
Data delivered your way
We support nine delivery methods. Choose the one that fits your infrastructure, or combine them for different use cases.
What makes our process different
Most data providers deliver a file and disappear. We build and run an ongoing data infrastructure for your business.
Proactive Maintenance
We monitor every pipeline 24/7 and fix structural breaks before you ever notice missing data. Most issues are resolved the same day they're detected.
Change Detection
Our systems automatically flag schema changes on target websites and alert our engineering team. Your data schema remains stable even as source sites evolve.
Dedicated Project Manager
Every engagement includes a named PM who knows your data requirements inside out. One point of contact, full accountability.
Flexible Scheduling
Real-time, hourly, daily, weekly, or custom, crawl frequency is set to your business rhythm, not a fixed tier.
What working with us is actually like.
We'd rather show you how we operate than fill this space with quotes. Every point below is something you can hold us to from the first conversation.
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.
Process FAQs
For most projects, we deliver a sample dataset within 3–7 business days of the discovery call. Full production pipelines typically go live within 2–3 weeks depending on the number of sources and complexity of the target sites.
Our monitoring systems detect structural changes automatically and alert the engineering team. Most site changes are resolved and pipelines restored within 24 hours, usually before you notice any gap in your data.
Yes. We handle change requests as part of the ongoing engagement. Adding new fields or sources is scoped and typically implemented within a few days. Your PM will advise on any timeline or pricing impact.
No. We can work from a simple brief, even just a list of websites and the data points you care about. The discovery call and sample review process is designed to capture requirements without needing technical documentation from your side.
We support real-time streaming, hourly, daily, weekly, and custom schedules. Frequency is set based on your business needs and the source site's update cadence. Some sources only update daily, so crawling more often than the source refreshes adds no value.
Connect to the Tools You Already Use
Data is delivered directly into your existing workflow, no new tools, no manual downloads.
Amazon S3
Cloud StorageAutomatic file drops to your S3 bucket on any schedule, hourly, daily, or weekly.
Google Sheets
SpreadsheetLive sync to your connected Google Sheet: always up to date for your team.
Snowflake
Data WarehouseDirect load to your Snowflake data warehouse with partitioned tables.
BigQuery
Data WarehouseNative BigQuery integration with schema-aligned table inserts.
PostgreSQL / MySQL
DatabaseDirect row inserts to your relational database via secure connection.
REST API
APIPull your dataset on-demand via a secure REST API endpoint with pagination.
SFTP
File TransferEncrypted file delivery to your SFTP server, fully automated.
Excel / CSV
FileStandard spreadsheet formats emailed, FTP'd, or dropped wherever you need.
Azure Blob
Cloud StorageAutomated delivery to your Azure Blob Storage container on schedule.
How We Guarantee Data Quality
Every delivery passes four layers of automated validation before it reaches your endpoint.
48 Validation Checks Before Every Delivery
- ›Field type checking
- ›Required field presence
- ›Enum value validation
- ›Nested structure validation
- ›Coverage rate per field
- ›Record count vs expected
- ›Missing value detection
- ›Partial record flagging
- ›Numeric range checks
- ›Date format validation
- ›Price sanity checks
- ›URL validity checks
- ›Cross-record dedup
- ›Timestamp verification
- ›Source freshness check
- ›Delta comparison
Ready to start your first project?
Book a 30-minute discovery call and we'll scope your first dataset, no commitment required.
No retainer required to find out whether your sources are feasible.