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.
Five high-intent use cases we deliver to B2B teams across industries.
Track competitor prices, product changes, promotions, availability, and positioning across public sources — and surface market moves when they happen.
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.
Collect product titles, prices, availability, ratings, reviews, sellers, and content attributes across e-commerce sites and marketplaces.
Build refreshed, domain-specific datasets for LLM applications, RAG workflows, classification, enrichment, and ML models. Cleaned, normalized, and delivered on your schedule.
Enrich company and prospect records using public business directories, professional sources, and industry databases — with structured context your sales team can actually use.
Six concrete benefits of a managed pipeline over DIY scrapers or one-shot vendors.
You get structured data, not scraping infrastructure. No internal team to assemble, monitor, or maintain.
Source-specific logic, monitoring, retries, and fallback strategies keep delivery stable as websites change.
Automated validation, deduplication, normalization, and human QA layered on top of raw collection.
Webhook, S3, BigQuery, Snowflake, Postgres, Google Sheets, CRM, or custom API — data lands where you need it.
Monthly, weekly, daily, or near-real-time schedules. Your data refreshes when your decisions need it.
Source terms, GDPR, CCPA, and data sensitivity evaluated per project. No collection without a valid lawful basis.
Four problems teams hit when they try to build web data pipelines internally — and how we handle them.
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.
Raw scraped data is rarely usable as-is. We layer automated validation, deduplication, normalization, and human QA on top — so your team gets data that's actually ready for analysis.
Scrapers break. Our infrastructure handles retries, schema drift, source changes, and monitoring — with alerts when something looks off and a team responsible for investigating and resolving issues quickly.
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.
Five phases from first conversation to ongoing delivery.
We map the business question, the sources involved, and the systems the data needs to feed.
We define the schema, delivery format, refresh cadence, and quality rules before any build.
We build the collection, validation, normalization, and delivery layers with monitoring built in.
You receive a first sample or proof of concept for sign-off, then production-grade delivery.
We monitor sources, fix breaks, adapt to changes, and own the pipeline as it runs in production.
Three representative engagements. Full case studies coming soon — meanwhile, ask us about any of these during a discovery call.
Replaced an in-house scraper that broke every 2 weeks with a managed pipeline delivering clean, validated price data daily into BigQuery.
Full case study coming soonConsolidated scattered listings, reviews, and company data into a single normalized dataset feeding the client's market intelligence dashboard.
Full case study coming soonBuilt a refreshed, deduplicated, and normalized web data feed powering a RAG workflow — delivered weekly with strict quality gates.
Full case study coming soonSeven questions buyers usually ask before a first call.
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.
Our pipelines include source-specific logic, monitoring, retries, and fallback strategies. When a website changes its structure, our monitoring detects the change, alerts the team responsible for that pipeline, and we deploy a fix — usually before the client notices. This is part of the managed service, not an extra charge.
We deliver data via webhook, S3, BigQuery, Snowflake, Postgres, Google Sheets, CRM, or custom API — in the format your team consumes (CSV, JSON, Parquet, or a custom schema). The delivery method is defined during the design phase based on your existing stack.
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.
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.
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.
Our collection design uses responsible request patterns, source-specific logic, and fallback strategies to maintain stable delivery as websites change. When a source becomes unavailable or terms of service change, we evaluate alternatives and communicate the impact on the pipeline — we do not silently continue collection against source terms.
Book a 30-minute discovery call. We'll 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.
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