Managed web scraping: custom pipelines we build, run, and repair.
Stop maintaining brittle scrapers and start using clean, reliable web data. We build custom collection pipelines that scale across complex public sources, adapt when websites change, and deliver structured data directly into your stack.
What teams use this data for
Five high-intent use cases we deliver to B2B teams across industries.
Competitive & Pricing Intelligence
Track competitor prices, product changes, promotions, availability, and positioning across public sources, and surface market moves when they happen.
Market & Industry Intelligence
Monitor companies, products, locations, listings, reviews, and category signals across fragmented public sources. Turn scattered data into a structured view your strategy team can use.
Digital Shelf & Product Data
Collect product titles, prices, availability, ratings, reviews, sellers, and content attributes across e-commerce sites and marketplaces.
AI-Ready Web Data
Build refreshed, domain-specific datasets for LLM applications, RAG workflows, classification, enrichment, and ML models. Cleaned, normalized, and delivered on your schedule.
Lead Enrichment
Enrich company and prospect records using public business directories, professional sources, and industry databases, with structured context your sales team can actually use.
Why teams choose Mint Data
Six concrete benefits of a managed pipeline over DIY scrapers or one-shot vendors.
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Done-for-you delivery
You get structured data, not scraping infrastructure. No internal team to assemble, monitor, or maintain.
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Resilient collection
Source-specific logic, monitoring, retries, and fallback strategies keep delivery stable as websites change.
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Quality at scale
Automated validation, deduplication, normalization, and human QA layered on top of raw collection.
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Stack integration
Webhook, S3, BigQuery, Snowflake, Postgres, Google Sheets, your CRM, or an API endpoint you define. Data lands where you need it.
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Recurring delivery
Monthly, weekly, daily, or near-real-time schedules. Your data refreshes when your decisions need it.
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Reviewed before we build
We assess source terms, data sensitivity, and applicable privacy requirements separately from whether the data can be collected at all. Both reviews happen before we build the pipeline, and you receive them in writing.
Data engineering challenges we solve
Four problems teams hit when they try to build web data pipelines internally, and how we handle them.
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Challenge 1
Resilient collection from complex sources
Complex public sources require resilient collection design. We build pipelines with source-specific logic, responsible request patterns, monitoring, retries, and fallback strategies so data delivery remains stable as websites change.
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Challenge 2
Data quality at scale
Raw scraped data is rarely usable as-is. We layer automated validation, deduplication, normalization, and sampled human review on top, so your team gets data that is actually ready for analysis.
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Challenge 3
Pipeline reliability
Scrapers break. Our infrastructure handles retries, schema drift, source changes, and monitoring, with alerts when record volume, completeness, or validation rates move, and a named engineer responsible for investigating and resolving the issue.
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Challenge 4
Integration into your stack
We deliver data the way your team consumes it: webhook, S3, BigQuery, Snowflake, Postgres, Google Sheets, CRM, or custom API. The data shows up where you need it, in the format you need.
What a delivery carries
Every record carries its source URL and collection timestamp. You can receive full snapshots or only what changed since the last run. When the same product appears under different names across sources, we match it so your comparisons hold. The delivered schema is versioned, with notice before any change that could break what consumes it.
How a Mint Data engagement works
Five phases from first conversation to ongoing delivery.
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1
Discover
We map the business question, the sources involved, and the systems the data needs to feed.
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2
Design
We define the schema, delivery format, refresh cadence, and quality rules before any build.
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3
Build
We build the collection, validation, normalization, and delivery layers with monitoring built in.
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4
Deliver
You receive a first sample or proof of concept for sign-off, then production-grade delivery.
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5
Maintain
We monitor sources, fix breaks, adapt to changes, and own the pipeline as it runs in production.
Where a tested component already fits your source, we reuse it instead of rebuilding it. Custom means the pipeline fits your sources and schema, not that every line is written from scratch.
When a source changes
Sites change their structure without warning. That is the normal condition of this work, not an exception.
Our collectors are monitored. When one stops returning what it should, we tell you as soon as we see it, investigate the cause, and prioritize the fix by how much of your delivery it affects. You know what broke, what we are doing about it, and when to expect the data.
If a source becomes unavailable, or its terms change in a way that affects your pipeline, we pause collection from that source, tell you what changed, and propose alternatives.
Recent work, described on a call
Daily competitor pricing across 12,000 SKUs for a retail group in Latin America.
Weekly market signals from more than 40 public sources for a research team in Europe.
A refreshed, validated dataset feeding a retrieval workflow for a client in a regulated industry.
We share the sources, the schema, and the quality numbers on a discovery call.
Frequently asked questions
Seven questions buyers usually ask before a first call.
What types of sources can Mint Data scrape?
We work with e-commerce sites and marketplaces, business directories, review platforms, listing sites, news and media sources, public registries, and industry-specific portals. Every project starts with a source feasibility assessment to confirm what can be collected responsibly and reliably before any build work begins.
How does Mint Data handle websites that change their structure?
Every pipeline is monitored for the symptoms of a structural change: drops in record volume, field completeness, or validation pass rate. When monitoring flags one, the engineer responsible investigates and deploys a fix, and you receive a notice with what broke and when to expect the data. This is part of the managed service, not an extra charge.
What formats and destinations does Mint Data support for delivery?
We deliver data via webhook, S3, BigQuery, Snowflake, Postgres, Google Sheets, your CRM, or an API endpoint you define, in the format your team consumes: CSV, JSON, or Parquet, with the schema agreed in the design phase. The delivery method is defined based on your existing stack.
How quickly can Mint Data deliver a first sample?
Most projects move from first conversation to an initial sample or proof of concept in 2–6 weeks, depending on source complexity, volume, data quality requirements, and delivery format. Full production pipelines typically require a separate implementation timeline of 4–12 weeks. We share a clear timeline before any work begins.
Does Mint Data work with personal data?
We focus on responsible collection from public sources and evaluate each project for source terms, data sensitivity, copyright, privacy, and applicable regulations such as GDPR and CCPA. We do not collect personal data without a valid lawful basis. When data requires consent, licensing, or another authorized access path, we use the appropriate channel.
How is pricing structured for web scraping services?
Each engagement is custom-scoped, so there is no fixed price list. Most of our work falls into one of three structures: a fixed-price proof of concept, an ongoing monthly retainer for managed data pipelines, or a milestone-based delivery for full-build projects. We share a written proposal with clear pricing before any work begins.
What happens if a source blocks collection?
Two different situations. If a source adds technical measures such as rate limits or bot detection, we first confirm that collection remains appropriate under its terms. If it does, we adjust request rate, infrastructure, and page rendering so delivery continues. If it does not, or if the terms change, we pause collection from that source, tell you what changed, quantify the impact, and propose alternatives: another source, a licensed dataset, or an API.
Send us a URL and we will tell you how we would collect it
Pick a site you need data from and share a business email. We will come back with a short written note: what is extractable, what makes it hard, and roughly what a pipeline for it looks like. No commitment.
How we engage
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1
Fixed-price proof of concept
A working sample or prototype in 2 to 6 weeks, at a price agreed before we start. You evaluate real output before committing to more.
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2
Managed service
A recurring pipeline or AI workflow, monitored and maintained by our team under a managed service agreement that defines what is included.
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3
Milestone-based project
For one-time builds or handovers to your team. Scope, price, and acceptance criteria are defined per milestone.
Tell us what you need to collect
Book a 30-minute discovery call. We will discuss the data you need, the sources involved, the systems it needs to feed, and whether Mint Data is the right fit. No pitch, no slides.
Book a discovery call