AI-Driven Web Scraping Services

Client Background: A New York-based B2B wholesale distributor operating across 12 product categories, supplying retail chains across the eastern USA. Annual GMV exceeding $340 million, with a pricing team managing 85,000+ SKUs.

Challenge: The client’s pricing team was manually checking 200+ competitor URLs daily across Amazon Business, Grainger, and regional wholesale portals — a process consuming 14 analyst-hours per day, producing data that was already 18-24 hours stale by the time it reached decision-makers. Margin erosion from reactive pricing decisions was estimated at $2.1 million annually.

Solution: Hir Infotech designed and deployed a real-time competitive pricing intelligence pipeline covering 340 competitor URLs across 6 major B2B e-commerce platforms. The AI scraping system ran on a 2-hour refresh cycle, with NLP-enriched product matching to align non-identical SKUs across platforms. Outputs were delivered via API directly into the client’s existing pricing optimization platform.

Results: Within 60 days of deployment, the pricing team reduced manual monitoring effort by 94%. Average pricing decision latency dropped from 24 hours to 2 hours. The client reported recovering $1.4 million in recovered margin within the first two quarters, with the scraping pipeline processing 1.2 million data points per week without a single data quality incident.

Client Testimonial: “Hir Infotech didn’t just give us data — they gave us the ability to move at market speed. The pipeline runs flawlessly and the compliance documentation their team provided made our legal sign-off faster than any vendor we’ve worked with.” — VP of Pricing, B2B Wholesale Distributor, New York.

Client Background: A Frankfurt-based alternative data provider supplying structured datasets to 40+ asset management and hedge fund clients across the EU. Regulated under BaFin and required to document all data sourcing for client compliance audits.

Challenge: The client needed to scale their web data acquisition from 120 monitored sources to 800+ without proportionally scaling their internal engineering team. Critically, every dataset needed full provenance documentation — source URL, extraction timestamp, lawful basis assessment, and retention policy — to satisfy fund clients’ regulatory reporting requirements under MiFID II.

Solution: Hir Infotech built a white-label data acquisition platform with full provenance logging embedded in the pipeline architecture. Each dataset delivered included a machine-readable compliance manifest detailing source, timestamp, extraction method, and regulatory assessment. The system was EU-hosted with German data residency, satisfying BaFin and GDPR requirements. 847 sources were onboarded within 45 days.

Results: The client scaled from 120 to 847 monitored sources with zero increase in internal engineering headcount. Audit response time for fund client data requests dropped from 3 days to 4 hours. The compliance manifest system was specifically cited by two fund clients as a reason for expanding their data subscription agreements.

Client Testimonial: “The provenance documentation Hir Infotech built into the pipeline is unlike anything we’ve seen from other vendors. Our funds’ compliance teams now use it as a benchmark for what good data sourcing looks like.” — Chief Data Officer, Alternative Data Provider, Frankfurt.

Client Background: A London-based SaaS company providing competitive intelligence software to 300+ B2B technology companies across the UK and Northern Europe. Their platform required continuous ingestion of product update announcements, pricing pages, and feature release notes from 5,000+ software vendor websites.

Challenge: The client’s existing scraping infrastructure suffered 23% data loss monthly due to site structure changes, anti-bot blocks, and JavaScript-rendered content that legacy scrapers couldn’t parse. Customer churn was rising as product intelligence gaps became visible to end users.

Solution: Hir Infotech migrated the client’s data acquisition layer to a fully managed AI scraping infrastructure with adaptive CSS selector recovery (automatically detects and corrects for site structure changes), headless browser rendering for JavaScript-heavy SaaS pricing pages, and a dedicated quality assurance layer that flagged anomalous data before it reached the client’s platform.

Results: Data loss rate dropped from 23% to under 1.2% within 30 days of migration. Coverage expanded from 5,000 to 7,400 software vendors without additional cost. Customer support tickets related to missing or stale intelligence dropped by 78%. The client reported a 19% reduction in churn rate in the following two quarters, directly attributable to improved data completeness.

Client Testimonial: “We’d tried three other scraping vendors before Hir Infotech. The difference in data reliability is extraordinary. Our customers finally trust the intelligence we provide, and our NPS has improved significantly since the migration.” — Head of Product, SaaS Market Intelligence Platform, London.

Client Background: A Sydney-based proptech company providing automated valuation models (AVM) and market analytics to 45 mortgage lenders and real estate agencies across Australia and New Zealand.

Challenge: The client needed daily extraction of 600,000+ property listings, recent sales data, and rental market updates across Domain, REA Group, and 12 state-level government property registries. Their AVM model accuracy was degrading because data ingestion delays meant models were running on 48-72 hour stale inputs.

Solution: Hir Infotech implemented a distributed scraping architecture designed specifically for the Australian property data ecosystem — including government registry data formats specific to NSW, VIC, QLD, and WA. Crawls were scheduled to complete and deliver structured data by 6:00 AM AEST daily, ensuring AVM models refreshed before market opening. Output integrated directly with the client’s AWS data lake via a custom API connector.

Results: Data staleness dropped from 48-72 hours to under 6 hours. AVM model accuracy improved by 8.3 percentage points, a material improvement for mortgage lenders using the platform for loan approval decisions. The client’s enterprise sales team cited improved data accuracy as the primary factor in closing three new lender contracts within six months of deployment.

Client Testimonial: “Our lender clients have strict requirements on data freshness. Hir Infotech built a pipeline that meets those requirements reliably, every single day. The team’s understanding of the Australian property data landscape was impressive.” — CTO, PropTech Platform, Sydney.

Client Background: A Paris-based healthcare market research company providing pharmaceutical manufacturers with drug pricing, formulary coverage, and competitive pipeline intelligence across the EU-27 market. Regulated under French data protection law (CNIL) and subject to GDPR Article 9 provisions for health-related data.

Challenge: The client’s research analysts were manually compiling drug pricing data from 14 national health authority websites across France, Germany, Italy, Spain, Netherlands, Belgium, and Austria — a process requiring 6 dedicated analysts working full time and still producing data with 2-week latency.

Solution: Hir Infotech designed a multilingual web scraping pipeline covering 14 EU health authority portals in French, German, Italian, Spanish, and Dutch — with NLP-based field extraction calibrated for health regulatory document formats. All pipelines included GDPR Article 9 safeguards and CNIL-compliant data handling procedures. Outputs were delivered as structured Excel and API feeds compatible with the client’s existing research platform.

Results: Manual analyst effort for data compilation reduced by 82%. Data latency dropped from 2 weeks to 48 hours. The research team could now cover 4x more pharmaceutical compounds per quarter with the same headcount. Client cited the multilingual NLP capability as a differentiator no other vendor had offered.

Client Testimonial: “For pharma intelligence across multiple EU markets, language has always been the barrier. Hir Infotech’s multilingual extraction capability gave us data quality in French, German, and Italian that we previously thought was only achievable through human translation.” — Director of Research, Healthcare Analytics, Paris.

Client Background: A Miami-based travel technology company providing revenue management software to 280 independent hotel groups across the USA, Spain, Italy, and Mexico. Their dynamic pricing engine required competitor rate data from 8 OTAs refreshed every 4 hours.

Challenge: Rate parity monitoring across 280 hotel clients, each tracking 15-25 competitor properties across Booking.com, Expedia, Hotels.com, and Airbnb, required 50,400+ data points every 4 hours — a volume that overwhelmed their existing infrastructure and resulted in 31% data gaps during peak traffic periods.

Solution: Hir Infotech deployed a high-concurrency scraping cluster specifically architected for OTA rate extraction — including Booking.com’s dynamic pricing modules, Expedia’s availability grids, and Airbnb’s multi-currency rate displays across 6 geographic markets. The pipeline handled 60,000+ data points per 4-hour cycle with redundancy and automatic retry on extraction failures.

Results: Data gap rate reduced from 31% to under 0.8%. The client’s revenue management software customers reported average RevPAR improvements of 6-11% in the first quarter post-deployment. The solution supported the client’s expansion from 280 hotel clients to 410 within eight months, with no additional scraping infrastructure investment required.

Client Testimonial: “The volume and reliability Hir Infotech delivers is genuinely exceptional. We’ve grown our client base by 46% and our data infrastructure costs haven’t increased proportionally at all. That’s the kind of partnership that changes a business.” — CEO, Travel Technology Company, Miami.

Client Background: An Amsterdam-based HR technology company providing workforce analytics and talent market intelligence to enterprise HR teams across the Netherlands, Germany, Belgium, and Denmark. Their platform tracked job posting trends, salary benchmarks, and skill demand signals.

Challenge: The client needed continuous extraction from 34 job boards across 4 countries in Dutch, German, French, and English — including platforms with aggressive anti-scraping protections (Stepstone DE, Jobbird NL, Vacature.com BE) — while maintaining GDPR compliance, as some job postings contained personal data elements.

Solution: Hir Infotech built a multilingual, GDPR-compliant job intelligence pipeline with purpose limitation safeguards — extracting structural job market data (titles, skills, compensation ranges, geographic distribution) while applying automated PII filtering to strip personal identifiers before data delivery. The pipeline covered 34 platforms across 4 countries with daily refresh and full audit trail documentation.

Results: Platform data coverage expanded from 18 to 34 job board sources. GDPR compliance documentation satisfied due diligence requirements from two enterprise clients in the financial services sector. The HR analytics platform launched a new salary benchmarking product feature directly enabled by the expanded data pipeline, generating €340,000 in new ARR within 12 months.

Client Testimonial: “Hir Infotech understood our compliance requirements immediately — they came to the first call with a GDPR framework for job data already prepared. That level of expertise in both data engineering and regulatory compliance is genuinely rare.” — Chief Product Officer, HR Technology, Amsterdam.