Managed web data
Managed web data,
without the operational burden.
Tell us what data you need. Import.io handles access, extraction, validation, monitoring and delivery — so your team gets reliable, structured web data without building or maintaining the infrastructure behind it. Managed web scraping is the fully operated collection, validation, monitoring and delivery of web data without your team maintaining the extraction infrastructure.
Built for complex, high-value web data workflows — including dynamic, protected and constantly changing sites.
Run every day for enterprise data teams and the data companies that serve them Including three leading commerce-intelligence platforms that run their web capture with Import.io.
You define the data. We run the system.
From feasibility and extraction through validation, monitoring and delivery, Import.io operates the workflow end to end.
Scope
Describe the data in plain language. We turn it into a contracted schema and written acceptance tests before anyone builds.
Feasibility
Every source gets a yes or a no: reachability, anti-bot, login, geography, cadence. Answered in days, with a proxy cost estimate attached.
Build
Extractors drafted by our AI builder and finished by engineers. Three to four day turnaround per source on new scope.
Internal QA
The AI QA skill checks sample output against the source HTML and the contracted schema. A human reviews. Nothing moves without both.
Your UAT
You approve the sample. Change requests are tracked, rebuilt and re-verified through the same gate.
Production
Scheduled runs. Delivery to S3, SFTP, API or webhook with a manifest. Post-processing and enrichment where you need it.
Run
Daily monitoring and QA, alerts in your channel, weekly summary, monthly report, monthly call. A named team that knows your data.
Gate one internal QA passes before a customer sees a row.Gate two your UAT sign-off before a feed goes to production.Typical first delivery two to three weeks from signature.
Quality you can see.
Websites change without notice. Inputs change. Categories move. The only defence is checking every delivery against what was contracted and what was true yesterday, then telling you what we found. That is what the team does, every morning, with AI doing the reading and people making the calls.
A US retailer feed came in 40% short on rows one morning. Monitoring was green. The QA baseline was not. Investigation traced it to the customer's own input file: a different zip code that day, so thousands of products returned no data. Not a site failure, and the customer was told the same morning, with a re-run offered.
Real case, customer name withheld. It is why we flag drops even when the dashboard is green.Site changes are detected automatically and repaired the same day. AI proposes the fix, an engineer verifies it against the contracted schema, and the feed ships on schedule. You see it in your channel as a resolved item, not an outage.
Checks
- Sample output compared field by field against the extracted HTML of the source
- Every field checked against the contracted schema: type, presence, format
- Fields drafted by AI are reviewed and corrected by a human before release
- Fill rates and null patterns compared with what the site actually shows
Who acts
- AI QA skill runs the comparison and writes the findings
- Delivery engineer resolves and re-runs
- Ticket moves to UAT only when QA passes
- Your team approves the sample
Checks
- Row counts, failure rate and empty rate against each source's rolling baseline
- Field-level fill rates and value distributions, tuned to your schema and your business rules
- Anomaly checks specific to you: price bands, currency, category shape, input coverage
- Delivery manifest reconciled against what landed in your destination
Who acts
- Daily alerts to the delivery team, generated and refined by AI
- An engineer investigates before the delivery window closes
- You are informed in your shared channel, with cause and fix
- Correctness commitments available in the SLA, with fees at risk
You see what we see.
Fully managed does not mean opaque. The same monitoring dashboard our team opens every morning is yours to open. Under it sits the workbench, where every row can be traced to the run, the input and the page that produced it.
- Monitoring dashboardSuccess, failure and empty rates, row counts and delivery status by source and by day.
- WorkbenchEvery run, every snapshot, every task. Filter by input. Query a delivery with SQL before it lands.
- DebuggerA link from any record to the exact page as it was fetched, so a dispute takes minutes, not a meeting.
- ControlsRetry policy and proxy tier per source. Residential egress only where a standard route fails, with daily cost alerts.
- ReportsMonthly usage, query volumes, residential and captcha consumption, incidents versus service requests, lowest-success sources.
Reporting that runs on a calendar.
A shared channel for the day to day. A rhythm for everything else. You never have to ask how the feed is doing.
Daily
Monitoring and QA before the delivery window closes.
- Row, failure and empty checks per source
- Anomalies investigated, cause found
- You are told in your channel if anything moved
Weekly
A written status summary in your shared Slack or Chat channel.
- What ran, what changed, what is in progress
- Open requests with owners and dates
- Tag the team any time for anything urgent
Monthly
A report and a call. The report is generated by AI from your run data and reviewed by your CSM.
- Usage, queries, residential and captcha consumption
- Incidents versus service requests, time to resolve
- Lowest-success sources and what we did about them
Quarterly
Scope, roadmap and commercial review with your account team.
- New sources and schema changes
- SLA performance against commitments
- Renewal and expansion planning
Built for the hard web.
Dynamic, protected, authenticated and location-sensitive sources are where managed operations earn their keep.
Anti-bot and challenges
Fingerprinting, rate limits, reCAPTCHA, hCaptcha and geo-blocks handled inside the run, not by your team.
challenge → handled inside SLAResidential egress, governed
Standard routes first. Residential only when a source demands it, after a test day to size the cost, with daily cost alerts.
test day → cost known before commitLogins and sessions
Trade portals, distributor catalogs and authenticated pricing captured under governed sessions.
13 trade logins, 8 markets, monthlyStore and zip context
Prices as a shopper in a given store or zip code sees them. Availability by location, not by country—the context required for digital shelf monitoring.
store 45202 · pickup todayMarketplace scale
Sustained capture for eCommerce product-data workloads on the largest marketplaces without degrading.
450M queries a year, one feedMany sources, one SLA
Dozens of sources under a single contract, a single schema and a single set of commitments.
72 sources, 100M+ queries, one SLAPDFs, filings, documents
Dockets, filings, spec sheets and circulars. Tables come out as tables.
docket → structured rowsEnrichment and normalisation
Category alignment, attribute extraction, product matching and deduplication as part of the feed, with fill rates reported.
fill rate 70% → 95%, one cycleA service with
a track record.
Fortune 500 manufacturers, global media and compliance businesses rely on Import.io for data for analytics platforms and other production web data programmes.
Ask for a reference call450M queries/year. One production workload sustained at marketplace scale.
Commercials built around the workflow.
Priced for the work that actually costs money: keeping a source alive and correct. Scoped up front, billed monthly, with commitments we put fees behind.
- pricing basisPer source maintained, or per query for very high-volume feeds. Not per row.
- onboardingFixed, scoped fee covering feasibility, build, QA and UAT for the first order.
- slasCoverage, freshness, correctness and response times, with a share of fees at risk. Coverage commitments name their exclusions.
- termAnnual, billed monthly. Scope can grow mid-term through the same feasibility gate.
- proxiesStandard egress included. Residential metered, sized on a test day, daily cost alerts before it becomes a bill.
- accessMonitoring dashboard and workbench included. Your data is yours, delivered to your destination.
- first deliveryTypically two to three weeks from signature for a first order.
Questions we get.
Short answers. Longer ones on the call.
What is included?
The full lifecycle: feasibility, build, internal QA, your UAT, production runs, delivery to your destination, daily monitoring, weekly summary, monthly report and call, a named CSM and support with severity-based targets.
How do you guarantee correctness?
Every new feed is QA'd against the source HTML and the contracted schema before you see it. Every production delivery is checked against its own baseline before it lands. Correctness SLAs with fees at risk are available.
Can our team see what is happening?
Yes. The monitoring dashboard and the workbench are included. Filter runs by input, open the debugger for any record, query a delivery with SQL.
How do you handle sites that block scrapers?
Rendering, challenge handling, rotating and residential egress, authenticated sessions and store-level context, all inside the run. Residential is used only where a standard route fails, after a test day to size the cost.
How fast can we start?
Feasibility per source in days. New sources typically three to four days each to build. A first order is usually delivering within two to three weeks of signature.
Is this compliant?
Rate-aware collection, respect for robots and terms, data processing agreements, and a written policy on what we will and will not fetch. Sector-specific requirements are handled in scoping.
Scope a managed programme
Tell us the data.
We’ll tell you yes or no, per source, in days.
Tell us the sources, fields and cadence. We’ll show you what is feasible, how Import.io can operate the workflow, and what it takes to keep it running.