How Import.io Processes 20M+ Data Points Per Day with AI Extraction
Rebuilt the core extraction engine and enterprise data pipeline for one of the world's most advanced web data platforms.
Import.io is an enterprise-grade web data platform used by Fortune 500 companies to extract structured data from the open web at scale. With a reported $38M in annual revenue and clients across financial services, retail, and market intelligence, their platform needed an extraction engine that could outpace increasingly sophisticated anti-scraping measures and handle enterprise-scale data volumes reliably.
20M+
data points processed daily
30+
BI and data warehouse integrations
99.8%
extraction accuracy post-rebuild
6 mo
from kickoff to enterprise launch
The challenge
Import.io's extraction accuracy was degrading quarter over quarter as target websites deployed more sophisticated bot-detection measures. The existing rule-based extraction engine required constant manual maintenance for every site change. At the same time, enterprise customer data volumes were growing faster than the pipeline could scale, and the self-service portal was too technical for business users to operate without engineering support.
Rule-based extraction failing under anti-scraping pressure
CSS selector-based extraction broke whenever target sites updated their layouts or deployed bot detection. Engineering was spending 40% of capacity on extraction maintenance rather than product development.
Pipeline throughput bottleneck at enterprise scale
The data pipeline was synchronous and single-region. Enterprise clients hitting 20M+ daily records were experiencing multi-hour processing delays and data freshness failures.
Self-service portal required engineering intervention
Business analysts at client companies could not configure or schedule their own extractions without help from Import.io engineers. Every new data source required a professional services engagement.
Brittle BI connector integrations
Tableau, Power BI, and Salesforce connectors were maintained as individual bespoke integrations that broke with every platform API change, generating high-priority support tickets from enterprise accounts.
What we built
We rebuilt the extraction core around an ML-based DOM pattern recognition system that adapts to layout changes without manual updates, paired it with a horizontally scalable Kafka-backed streaming pipeline, and redesigned the self-service portal around business user workflows. The result was a platform that could grow without growing the engineering team proportionally.
AI-powered extraction engine
ML model trained on millions of successful extractions that identifies semantically meaningful data elements by content type and visual position rather than brittle CSS selectors. Accuracy maintained even when page layouts change.
Distributed streaming pipeline
Kafka-based ingestion layer with Elasticsearch indexing, multi-region deployment on AWS, and horizontal autoscaling. Capable of processing 20M+ records per day with sub-30-minute data freshness SLA.
Enterprise self-service portal
Redesigned data source configuration with visual schema mapping, point-and-click field selection from a live preview, scheduling, and output format management. Business users now configure new extractions independently.
Integration marketplace
Standardised connector framework with native integrations for Tableau, Power BI, Looker, BigQuery, Snowflake, Salesforce, and S3. Each connector versioned and automatically tested against platform API changes.
Data quality monitoring
Real-time extraction health dashboard with per-source accuracy scoring, schema drift detection, failure alerting, and automatic retry with escalation. Clients see data quality metrics alongside their extracted data.
Results
Measurable outcomes delivered, not projected.
99.8%
extraction accuracy
ML-based extraction lifted accuracy from 94% to 99.8% across the active source library. Engineering time spent on extraction maintenance fell by over 60%.
20M+
records per day
The distributed Kafka pipeline processes over 20 million data points daily with consistent sub-30-minute freshness, handling enterprise clients that previously caused pipeline congestion.
30+
BI integrations live
The connector marketplace launched with 30+ production-ready integrations. Enterprise onboarding time for new data destinations fell from weeks to hours.
60%
less engineering overhead
The self-service portal and adaptive extraction engine together reduced the engineering support burden per enterprise client by 60%, freeing the team to focus on new capabilities.
Technologies used
AI / Extraction
Data Pipeline
Backend
Infrastructure
“The team's attention to detail, creativity, and technical expertise exceeded our expectations. We have received so much positive feedback from our customers already.”
Hilla Pedramparsi
CTO, Import.io
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