29-January-2026
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Unlock Smarter Decisions with AI-Powered Alternative Data Intelligence
Alternative Data
In a world where traditional data no longer provides a competitive edge, alternative data is the difference between leading the market and following it. Hir Infotech delivers enterprise-grade AI-driven alternative data services — combining intelligent web scraping, real-time signal extraction, and deep analytics to help businesses in the USA, Europe, and Australia act on insights competitors simply cannot see. With 13+ years of experience, 2,745+ happy clients, and a globally trusted delivery model, we empower CTOs, CDOs, Product Leaders, and Data Teams to make faster, more accurate, and more profitable decisions at scale.
38.4%
CAGR Market Growth
94%
AI Adoption
65%
Hedge Fund Usage
99.2%
Accuracy Rate
13+
Years of Expertise
Why Alternative Data Matters for Modern B2B Enterprises
The era of relying solely on earnings reports, press releases, and standard market feeds is over. Today's most successful enterprises — from hedge funds in London and New York to e-commerce leaders in Berlin and Sydney — are outpacing competitors by harnessing alternative data: non-traditional, unconventional, and often real-time signals harvested from the open web, satellite feeds, consumer transactions, and social ecosystems. Alternative data provides a panoramic view of market dynamics, consumer behavior, supply chain shifts, and macroeconomic trends that conventional datasets simply cannot offer. At Hir Infotech, we've spent over 13 years helping 2,745+ B2B clients across the USA, UK, Germany, France, Netherlands, Sweden, Switzerland, Austria, Denmark, Spain, Italy, Iceland, and Australia transform raw, unstructured alternative data into structured intelligence that drives decisions. Our AI-powered pipelines handle ingestion, normalization, enrichment, and delivery — so your analysts spend time acting on insights, not cleaning data.
- AI-Driven Web Scraping for Alternative Signals: Hir Infotech’s intelligent scraping infrastructure continuously harvests product pricing, review sentiment, job postings, SEC filings, news events, and geolocation signals from thousands of sources, delivering clean and structured datasets tailored to your analytical models.
- Real-Time Consumer and Transaction Intelligence: We extract and aggregate anonymized consumer spending patterns, point-of-sale activity, app usage trends, and credit card transaction proxies to help retail, fintech, and investment clients forecast revenue and identify market shifts before they surface in official reports.
- Satellite, Geolocation, and IoT Signal Processing: Hir Infotech integrates and processes location-based foot traffic data, satellite imagery signals, and IoT sensor streams into normalized datasets — enabling supply chain intelligence, retail performance monitoring, and commodity market insights.
- ESG, Sentiment, and News Analytics: Our NLP-driven alternative data pipelines extract sentiment scores, ESG performance signals, executive hiring trends, and media narratives from millions of web pages daily — giving investment managers, brand strategists, and risk teams a measurable informational edge.
AI-Scale Data. Enterprise-Grade Intelligence.
Hir Infotech operates a fully managed, AI-enhanced alternative data infrastructure — covering data sourcing, extraction, validation, enrichment, and structured delivery — built for mid-market and enterprise B2B teams that need accuracy, speed, and compliance at scale.
Intelligent Multi-Source Extraction
Our proprietary AI scraping engines simultaneously harvest structured and unstructured alternative data from news portals, financial platforms, e-commerce sites, government databases, and social media — across 30+ countries with consistent uptime and zero data gaps.
Real-Time Data Pipeline Delivery
Hir Infotech’s data pipelines deliver near-real-time alternative datasets via API, JSON, CSV, or direct cloud integration (AWS S3, Google BigQuery, Azure Blob) — ensuring your dashboards, quant models, and BI tools are always operating on the freshest available intelligence.
NLP-Powered Sentiment & Signal Enrichment
Using advanced Natural Language Processing and transformer-based AI models, we enrich raw web data with sentiment scores, entity recognition, topic classification, and trend signals — enabling investment-grade analytics for fintech, hedge funds, and enterprise strategy teams.
GDPR, CCPA & AI Act–Compliant Data Governance
Every alternative data workflow at Hir Infotech is architected with compliance at its core — including data minimization protocols, robots.txt adherence, personal data filtering, consent-aware extraction, and complete audit trails for EU, US, and Australian regulatory frameworks.
Trusted by leading brands
Popular Use Cases & Platforms for Alternative Data
Why AI-Driven Alternative Data Is the Future of B2B Intelligence
From Noise to Alpha: How AI Transforms Raw Alternative Signals
The sheer volume of available alternative data is both an opportunity and a challenge. Investment managers, enterprise strategy teams, and data-driven growth organizations are generating and accessing more non-traditional signals than ever before — from satellite imagery of oil storage tanks to real-time product review sentiment on e-commerce platforms. The difference between companies that profit from this abundance and those that drown in it lies in AI. Hir Infotech’s AI-driven alternative data platform applies machine learning, transformer-based NLP, and automated feature engineering to continuously turn unstructured web content into clean, enriched, and predictive intelligence. For a hedge fund in London, that means knowing a retailer’s foot traffic is declining three weeks before earnings. For a supply chain manager in Munich, it means monitoring shipping route disruptions before they cause delays. With 13+ years of technical delivery and 2,745+ global clients, Hir Infotech ensures every alternative data pipeline is production-grade, complaint-compliant, and built to scale.
Compliant, Scalable, and Always-On: Enterprise Alternative Data for Europe and the USA
Compliance is no longer optional in alternative data workflows. With GDPR enforcement intensifying across the EU, CCPA expanding its scope across US states, and the EU AI Act introducing new obligations for AI-assisted data processing, enterprise-grade alternative data providers must treat governance as a first-class product feature — not an afterthought. Hir Infotech has built GDPR-compliant, CCPA-aligned, and AI Act–aware data infrastructure from the ground up. Every scraping project starts with a legal review, data minimization specification, and documented lawful basis for collection. Our pipelines include automated personal data filtering, robots.txt compliance, rate-limiting logic, and full audit trail generation — making Hir Infotech the trusted alternative data partner for financial institutions in Frankfurt and New York, retail enterprises in Amsterdam and Sydney, and fintech innovators in Stockholm and Chicago. We deliver data via API, flat file, or cloud-native integration, with SLA-backed uptime and dedicated account management for enterprise contracts.
Industry We Serve
Real-World Success: Case Studies
Client Background:
A mid-sized quantitative hedge fund headquartered in New York, managing approximately $2.4 billion in assets under management, with a focus on long/short equity strategies across US consumer discretionary and retail sectors.
Challenge:
The fund’s quant team was relying on traditional earnings calendars and analyst reports to time entry and exit positions in retail stocks. This approach consistently placed them on the reactive side of market movements. They needed a way to detect revenue signals 2–4 weeks before quarterly earnings releases to improve prediction accuracy and reduce portfolio volatility.
Solution:
Hir Infotech implemented a multi-source alternative data pipeline targeting five major US retail brands. The pipeline harvested daily foot traffic proxies via geo-tagged review data and check-in activity from consumer platforms, product availability and pricing trends from e-commerce listings, and workforce signal data from job postings indicating expansion or cost-cutting phases. All data was normalized, enriched with NLP sentiment scoring, and delivered daily via API into the fund’s Python-based quant models.
Results:
Within two earnings cycles, the fund’s retail prediction accuracy improved from 61% to 79%. The team successfully anticipated an underperformance in two large-cap retail stocks based on foot traffic decline signals — closing short positions four days before earnings missed consensus. Over the following 12 months, the alternative data program contributed to an estimated 14% improvement in risk-adjusted returns on the retail sub-portfolio.
Client Testimonial:
“Hir Infotech gave us the signal layer our quant models were missing. The data quality, delivery reliability, and compliance documentation were exactly what an institutional team needs. They feel less like a vendor and more like a data engineering partner.”
— Head of Quantitative Research, New York Hedge Fund
Client Background:
A leading German FMCG company with a distribution network spanning DACH (Germany, Austria, Switzerland) and operations in France and the Netherlands. The company sells across 12 product categories through both retail and direct e-commerce channels.
Challenge:
The company’s category management team was manually monitoring competitor prices across three major e-commerce platforms — a process that consumed 40+ analyst hours per week and produced data that was already 48 hours stale by the time it reached decision-makers. With dynamic pricing becoming the norm in European e-commerce, their static weekly pricing reports were inadequate.
Solution:
Hir Infotech deployed an automated, AI-driven competitive price intelligence scraper covering 7 e-commerce platforms across DACH and France, extracting price points, promotional activity, stock availability, and seller ratings in real-time. Data was delivered into a custom-built Power BI dashboard with daily refresh cycles, enabling the pricing team to respond within hours to competitor promotions.
Results:
The company reduced pricing analysis labor costs by 73% and cut response time to competitor price changes from 48 hours to under 4 hours. In a key promotional period, the real-time intelligence enabled a dynamic promotion strategy that increased category revenue by 18% compared to the prior year’s equivalent period.
Client Testimonial:
“With Hir Infotech, our pricing team finally has the real-time intelligence to compete effectively across European markets. The GDPR-compliant delivery and seamless Power BI integration made onboarding fast and frictionless.”
— Head of Category Management, FMCG Enterprise, Frankfurt
Client Background:
A UK-based PropTech startup providing predictive real estate analytics to institutional property investors and mortgage lenders. The platform aggregates market signals to forecast residential price trends across 14 UK metropolitan regions.
Challenge:
The team needed a scalable, reliable pipeline to continuously extract property listing data, price change events, days-on-market metrics, and location-based demand signals from multiple UK property portals. Manual scraping attempts had been unreliable, frequently blocked, and returned inconsistent data formats.
Solution:
Hir Infotech built a fully managed alternative data collection infrastructure targeting Rightmove, Zoopla, and OnTheMarket — with anti-detection proxies, JavaScript rendering support, and structured data output in JSON and CSV. The pipeline monitored over 250,000 active property listings weekly, tracking price reductions, new listings velocity, and time-to-sale metrics segmented by postcode.
Results:
The PropTech client’s forecast model accuracy for 3-month price trends improved by 22% following alternative data integration. Lenders using the platform reduced manual property valuation overrides by 31% and the startup secured two new institutional investor contracts, citing the data quality and depth as a key differentiator.
Client Testimonial:
“Hir Infotech delivered exactly what our data team needed — high-frequency, structured property data with reliable coverage and a clean API. Their technical team understood our use case from day one and executed without hand-holding.”
— CTO, PropTech Platform, London
Client Background:
An Australian e-commerce intelligence platform serving mid-market retailers and private label brands across the Asia-Pacific region. The company provides competitive analytics and brand health monitoring to clients in retail, health, and consumer electronics.
Challenge:
The platform’s clients needed daily updates on competitor product launches, pricing strategies, review sentiment trends, and stock availability across Amazon AU, eBay AU, and Catch.com.au. Building and maintaining in-house scrapers had been costly and prone to breakage after platform updates.
Solution:
Hir Infotech took over the full data collection infrastructure, deploying AI-enhanced scraping agents that self-heal after platform structural changes, with fallback rendering via headless browser technology. Product data, star ratings, review text, and seller positioning were extracted daily across 18 product categories and enriched with NLP-based sentiment classification before delivery.
Results:
The platform’s data freshness SLA went from “best effort weekly” to guaranteed daily delivery. Client retention rate improved from 74% to 91% in the 12 months following the Hir Infotech engagement. One consumer electronics client used the competitive sentiment data to reformulate product messaging, driving a 23% improvement in conversion rates on their own D2C website.
Client Testimonial:
“Hir Infotech transformed our data operations. We went from unreliable scrapers and stale data to a professional-grade intelligence layer that our clients now depend on daily. The team is responsive, technically excellent, and understands our market.”
— CEO, E-Commerce Intelligence Platform, Sydney
Client Background:
A supply chain risk consultancy based in Amsterdam serving logistics, manufacturing, and retail clients across the Netherlands, Belgium, and Germany. The firm provides early-warning intelligence for supply chain disruptions, port delays, and geopolitical trade risks.
Challenge:
The team needed a continuous, multi-source alternative data feed covering shipping container tracking signals, port authority notices, trade regulatory announcements, and supplier financial health indicators across 40+ global source sites in multiple languages.
Solution:
Hir Infotech built a multilingual alternative data extraction pipeline spanning English, German, Dutch, and French sources — including shipping authority portals, logistics news feeds, customs regulatory websites, and corporate filing databases. The data was enriched with entity resolution, risk scoring, and change detection alerts, delivered into the client’s risk dashboard via real-time webhook.
Results:
The firm was able to issue supply chain disruption alerts an average of 6.2 days earlier than industry benchmarks. One manufacturing client used early disruption signals to reroute shipments in advance, saving an estimated €340,000 in demurrage fees and expedited freight costs. The consultancy increased their contract renewal rate by 38% over the following year.
Client Testimonial:
“The alternative data infrastructure Hir Infotech built for us is genuinely mission-critical. The multilingual coverage, early signal detection, and structured API delivery give us a capability that sets us completely apart from competing risk firms.”
— Managing Director, Supply Chain Risk Consultancy, Amsterdam
Client Background:
A Stockholm-based investment bank with significant exposure to ESG-linked investment products. The bank’s sustainability team needed continuous, scalable monitoring of ESG news, corporate governance events, environmental compliance filings, and social controversy signals across 300+ portfolio companies globally.
Challenge:
The ESG team was manually monitoring Google News alerts and occasional third-party ESG rating refreshes — a process that was too slow, too narrow in coverage, and insufficiently structured for integration into quantitative ESG scoring models. They needed a programmatic, always-on intelligence layer.
Solution:
Hir Infotech deployed a broad-coverage ESG alternative data pipeline extracting corporate news, regulatory filings, NGO reports, controversy signals, and executive statement analysis across 12 languages and 60+ countries. NLP classification tagged each data point by ESG pillar (Environmental, Social, Governance), controversy severity, and company entity — with daily delivery into the bank’s internal data lake.
Results:
The ESG team’s monitoring coverage expanded from 300 to 1,200 companies with no increase in headcount. The bank identified three material governance controversies — including an executive departure and a regulatory probe — an average of 11 days earlier than ESG rating agency updates, enabling proactive portfolio rebalancing. The program was shortlisted for an internal innovation award and expanded to two additional investment desks.
Client Testimonial:
“Hir Infotech gave our ESG team a capability we simply couldn’t build internally at this scale. The linguistic coverage, data structure, and speed of delivery have materially improved how we manage sustainability risks across the portfolio.”
— Head of Sustainable Investment Research, Investment Bank, Stockholm
Client Background:
A leading Spanish retail bank with over 4 million personal and SME customers, operating across Spain, Italy, and Portugal. The bank’s credit risk division was exploring alternative data integration to supplement traditional credit scoring models.
Challenge:
The bank’s legacy credit models were based entirely on traditional financial data — credit bureau scores, income documentation, and balance history. For thin-file customers and SMEs with limited credit history, the models produced high rates of false negatives, leading to over-rejection of creditworthy applicants and missed revenue.
Solution:
Hir Infotech designed a compliant alternative data enrichment layer combining web-scraped business performance signals (review activity, online trading presence, foot traffic proxies, digital presence scoring), public company filing data, and sector-level economic signals from Eurostat and national statistical portals. The data was anonymized, aggregated, and delivered to the bank’s credit team in structured JSON format for model augmentation.
Results:
In a 6-month pilot on SME credit applications, the bank improved approval rates for thin-file SME applicants by 19% while maintaining default rates within acceptable thresholds. The alternative data layer proved particularly impactful for micro-businesses in hospitality and retail — two sectors with historically limited financial documentation. The pilot is being expanded to consumer lending in 2026.
Client Testimonial:
“Hir Infotech’s understanding of GDPR compliance in credit risk contexts, combined with the quality and structured format of their alternative data delivery, gave us the confidence to move from pilot to production. They are a credible, enterprise-ready data partner.”
— Director of Credit Risk Analytics, Retail Bank, Madrid
Real-World Success: Case Studies
Client Background:
A mid-sized quantitative hedge fund headquartered in New York, managing approximately $2.4 billion in assets under management, with a focus on long/short equity strategies across US consumer discretionary and retail sectors.
Challenge:
The fund’s quant team was relying on traditional earnings calendars and analyst reports to time entry and exit positions in retail stocks. This approach consistently placed them on the reactive side of market movements. They needed a way to detect revenue signals 2–4 weeks before quarterly earnings releases to improve prediction accuracy and reduce portfolio volatility.
Solution:
Hir Infotech implemented a multi-source alternative data pipeline targeting five major US retail brands. The pipeline harvested daily foot traffic proxies via geo-tagged review data and check-in activity from consumer platforms, product availability and pricing trends from e-commerce listings, and workforce signal data from job postings indicating expansion or cost-cutting phases. All data was normalized, enriched with NLP sentiment scoring, and delivered daily via API into the fund’s Python-based quant models.
Results:
Within two earnings cycles, the fund’s retail prediction accuracy improved from 61% to 79%. The team successfully anticipated an underperformance in two large-cap retail stocks based on foot traffic decline signals — closing short positions four days before earnings missed consensus. Over the following 12 months, the alternative data program contributed to an estimated 14% improvement in risk-adjusted returns on the retail sub-portfolio.
Client Testimonial:
“Hir Infotech gave us the signal layer our quant models were missing. The data quality, delivery reliability, and compliance documentation were exactly what an institutional team needs. They feel less like a vendor and more like a data engineering partner.”
— Head of Quantitative Research, New York Hedge Fund
Client Background:
A leading German FMCG company with a distribution network spanning DACH (Germany, Austria, Switzerland) and operations in France and the Netherlands. The company sells across 12 product categories through both retail and direct e-commerce channels.
Challenge:
The company’s category management team was manually monitoring competitor prices across three major e-commerce platforms — a process that consumed 40+ analyst hours per week and produced data that was already 48 hours stale by the time it reached decision-makers. With dynamic pricing becoming the norm in European e-commerce, their static weekly pricing reports were inadequate.
Solution:
Hir Infotech deployed an automated, AI-driven competitive price intelligence scraper covering 7 e-commerce platforms across DACH and France, extracting price points, promotional activity, stock availability, and seller ratings in real-time. Data was delivered into a custom-built Power BI dashboard with daily refresh cycles, enabling the pricing team to respond within hours to competitor promotions.
Results:
The company reduced pricing analysis labor costs by 73% and cut response time to competitor price changes from 48 hours to under 4 hours. In a key promotional period, the real-time intelligence enabled a dynamic promotion strategy that increased category revenue by 18% compared to the prior year’s equivalent period.
Client Testimonial:
“With Hir Infotech, our pricing team finally has the real-time intelligence to compete effectively across European markets. The GDPR-compliant delivery and seamless Power BI integration made onboarding fast and frictionless.”
— Head of Category Management, FMCG Enterprise, Frankfurt
Client Background:
A UK-based PropTech startup providing predictive real estate analytics to institutional property investors and mortgage lenders. The platform aggregates market signals to forecast residential price trends across 14 UK metropolitan regions.
Challenge:
The team needed a scalable, reliable pipeline to continuously extract property listing data, price change events, days-on-market metrics, and location-based demand signals from multiple UK property portals. Manual scraping attempts had been unreliable, frequently blocked, and returned inconsistent data formats.
Solution:
Hir Infotech built a fully managed alternative data collection infrastructure targeting Rightmove, Zoopla, and OnTheMarket — with anti-detection proxies, JavaScript rendering support, and structured data output in JSON and CSV. The pipeline monitored over 250,000 active property listings weekly, tracking price reductions, new listings velocity, and time-to-sale metrics segmented by postcode.
Results:
The PropTech client’s forecast model accuracy for 3-month price trends improved by 22% following alternative data integration. Lenders using the platform reduced manual property valuation overrides by 31% and the startup secured two new institutional investor contracts, citing the data quality and depth as a key differentiator.
Client Testimonial:
“Hir Infotech delivered exactly what our data team needed — high-frequency, structured property data with reliable coverage and a clean API. Their technical team understood our use case from day one and executed without hand-holding.”
— CTO, PropTech Platform, London
Client Background:
An Australian e-commerce intelligence platform serving mid-market retailers and private label brands across the Asia-Pacific region. The company provides competitive analytics and brand health monitoring to clients in retail, health, and consumer electronics.
Challenge:
The platform’s clients needed daily updates on competitor product launches, pricing strategies, review sentiment trends, and stock availability across Amazon AU, eBay AU, and Catch.com.au. Building and maintaining in-house scrapers had been costly and prone to breakage after platform updates.
Solution:
Hir Infotech took over the full data collection infrastructure, deploying AI-enhanced scraping agents that self-heal after platform structural changes, with fallback rendering via headless browser technology. Product data, star ratings, review text, and seller positioning were extracted daily across 18 product categories and enriched with NLP-based sentiment classification before delivery.
Results:
The platform’s data freshness SLA went from “best effort weekly” to guaranteed daily delivery. Client retention rate improved from 74% to 91% in the 12 months following the Hir Infotech engagement. One consumer electronics client used the competitive sentiment data to reformulate product messaging, driving a 23% improvement in conversion rates on their own D2C website.
Client Testimonial:
“Hir Infotech transformed our data operations. We went from unreliable scrapers and stale data to a professional-grade intelligence layer that our clients now depend on daily. The team is responsive, technically excellent, and understands our market.”
— CEO, E-Commerce Intelligence Platform, Sydney
Client Background:
A supply chain risk consultancy based in Amsterdam serving logistics, manufacturing, and retail clients across the Netherlands, Belgium, and Germany. The firm provides early-warning intelligence for supply chain disruptions, port delays, and geopolitical trade risks.
Challenge:
The team needed a continuous, multi-source alternative data feed covering shipping container tracking signals, port authority notices, trade regulatory announcements, and supplier financial health indicators across 40+ global source sites in multiple languages.
Solution:
Hir Infotech built a multilingual alternative data extraction pipeline spanning English, German, Dutch, and French sources — including shipping authority portals, logistics news feeds, customs regulatory websites, and corporate filing databases. The data was enriched with entity resolution, risk scoring, and change detection alerts, delivered into the client’s risk dashboard via real-time webhook.
Results:
The firm was able to issue supply chain disruption alerts an average of 6.2 days earlier than industry benchmarks. One manufacturing client used early disruption signals to reroute shipments in advance, saving an estimated €340,000 in demurrage fees and expedited freight costs. The consultancy increased their contract renewal rate by 38% over the following year.
Client Testimonial:
“The alternative data infrastructure Hir Infotech built for us is genuinely mission-critical. The multilingual coverage, early signal detection, and structured API delivery give us a capability that sets us completely apart from competing risk firms.”
— Managing Director, Supply Chain Risk Consultancy, Amsterdam
Client Background:
A Stockholm-based investment bank with significant exposure to ESG-linked investment products. The bank’s sustainability team needed continuous, scalable monitoring of ESG news, corporate governance events, environmental compliance filings, and social controversy signals across 300+ portfolio companies globally.
Challenge:
The ESG team was manually monitoring Google News alerts and occasional third-party ESG rating refreshes — a process that was too slow, too narrow in coverage, and insufficiently structured for integration into quantitative ESG scoring models. They needed a programmatic, always-on intelligence layer.
Solution:
Hir Infotech deployed a broad-coverage ESG alternative data pipeline extracting corporate news, regulatory filings, NGO reports, controversy signals, and executive statement analysis across 12 languages and 60+ countries. NLP classification tagged each data point by ESG pillar (Environmental, Social, Governance), controversy severity, and company entity — with daily delivery into the bank’s internal data lake.
Results:
The ESG team’s monitoring coverage expanded from 300 to 1,200 companies with no increase in headcount. The bank identified three material governance controversies — including an executive departure and a regulatory probe — an average of 11 days earlier than ESG rating agency updates, enabling proactive portfolio rebalancing. The program was shortlisted for an internal innovation award and expanded to two additional investment desks.
Client Testimonial:
“Hir Infotech gave our ESG team a capability we simply couldn’t build internally at this scale. The linguistic coverage, data structure, and speed of delivery have materially improved how we manage sustainability risks across the portfolio.”
— Head of Sustainable Investment Research, Investment Bank, Stockholm
Client Background:
A leading Spanish retail bank with over 4 million personal and SME customers, operating across Spain, Italy, and Portugal. The bank’s credit risk division was exploring alternative data integration to supplement traditional credit scoring models.
Challenge:
The bank’s legacy credit models were based entirely on traditional financial data — credit bureau scores, income documentation, and balance history. For thin-file customers and SMEs with limited credit history, the models produced high rates of false negatives, leading to over-rejection of creditworthy applicants and missed revenue.
Solution:
Hir Infotech designed a compliant alternative data enrichment layer combining web-scraped business performance signals (review activity, online trading presence, foot traffic proxies, digital presence scoring), public company filing data, and sector-level economic signals from Eurostat and national statistical portals. The data was anonymized, aggregated, and delivered to the bank’s credit team in structured JSON format for model augmentation.
Results:
In a 6-month pilot on SME credit applications, the bank improved approval rates for thin-file SME applicants by 19% while maintaining default rates within acceptable thresholds. The alternative data layer proved particularly impactful for micro-businesses in hospitality and retail — two sectors with historically limited financial documentation. The pilot is being expanded to consumer lending in 2026.
Client Testimonial:
“Hir Infotech’s understanding of GDPR compliance in credit risk contexts, combined with the quality and structured format of their alternative data delivery, gave us the confidence to move from pilot to production. They are a credible, enterprise-ready data partner.”
— Director of Credit Risk Analytics, Retail Bank, Madrid
Working with Hir Infotech
Data you can trust
Rely on Hir Infotech for 95%+ accurate data, meticulously verified to fuel your B2B success. Our global scraping solutions deliver trusted insights for confident decision-making worldwide.
Decades of experience
With 12+ years of expertise, Hir Infotech has served 2745+ clients globally. Our proven scraping solutions drive B2B success across the USA, Europe, and Australia.
Legal peace of mind
Rely on Hir Infotech for 95%+ accurate data, meticulously verified to fuel your B2B success. Our global scraping solutions deliver trusted insights for confident decision-making worldwide.
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Ready to Turn Alternative Data into Your Competitive Advantage?
At Hir Infotech, we’ve spent 13+ years helping 2,745+ enterprise and mid-market clients across the USA, Europe, and Australia extract, enrich, and activate alternative data that drives real business results. Whether you’re a hedge fund seeking alpha signals, a retail brand tracking competitors in real time, or a fintech platform enriching credit models — our AI-powered alternative data infrastructure is ready to deliver.
Request your free sample dataset today and experience the quality, speed, and compliance that 2,745+ clients trust globally.
Unlock Business Growth with Expert Alternative Data Solutions
Benefits of Alternative Data Services by Hir Infotech
Flexible Pricing Models
Project-Based (Flat Fee) Pricing
A one-time fee is charged for a specific project, regardless of volume or duration, based on scope and complexity.
Hourly or Time-Based Pricing
Billed based on the time spent developing, running, or maintaining the scraper, often used for custom or consulting-heavy projects.
Pay-As-You-Go
Charged based on actual usage, such as per request, per GB of bandwidth, or per page scraped, with no fixed commitment.
Subscription-Based Pricing
pay a recurring fee (monthly or annually) for access to scraping services, often tiered based on usage limits like the number of requests, pages scraped, or data points extracted.
Hir Infotech’s Web Scraping Methodology
1
Understanding Your Goals
We begin by collaborating with you to define your data needs—be it for a one-time project, recurring insights, or custom solutions. Whether you opt for Pay-As-You-Go flexibility, a Project-Based Flat Fee, Hourly expertise, or a Subscription plan, we align our approach to your objectives.
2
Target Identification & Planning
Our team identifies the websites and data sources critical to your project. We analyze site structures, assess complexity (e.g., static vs. dynamic content), and plan the most efficient scraping strategy, ensuring compliance with public data access norms.
3
Advanced Data Extraction
Using cutting-edge tools and custom-built scrapers, we extract data at scale. We tackle challenges like JavaScript-rendered pages or anti-scraping measures with techniques such as:
- Rotating proxies to avoid blocks.
- Headless browsers (e.g., Selenium) for dynamic content.
- Optimized request pacing to mimic human behavior.
4
Data Processing & Quality Control
Raw data is parsed, cleaned, and structured into formats like CSV, JSON, or Excel. We remove duplicates, correct errors, and validate accuracy to ensure you receive reliable, ready-to-use datasets.
5
Delivery Tailored to You
Depending on your pricing model, we deliver results how and when you need them:
- Pay-As-You-Go: On-demand data for ad-hoc use.
- Project-Based: Complete datasets for one-off projects.
- Hourly: Iterative updates during custom development.
- Subscription: Regular, automated feeds for ongoing insights.
6
Ongoing Support & Optimization
We monitor site changes, adapt scrapers as needed, and provide support to keep your data flowing seamlessly. Subscription clients enjoy continuous updates, while Hourly clients benefit from hands-on refinements.
Let's build something great together.
Contact us for top-tier talent and exceptional results.
We’ve been working with Hir Infotech for our data scraping needs, and they have exceeded our expectations. The data they provide us is always accurate, timely and helps us make more informed decisions. The team at Hir Infotech is always responsive, and we appreciate their high level of expertise.
The data scraping services provided by Hir Infotech have been instrumental in helping us stay ahead of the competition. We now have access to real-time pricing and product data, allowing us to adjust our strategy and remain competitive.
we are incredibly grateful for the partnership we’ve developed with Hir Infotech. Their data scraping services have helped us improve our marketing strategies and drive growth for our clients. We highly recommend their services to any advertising & marketing company looking to gain a competitive edge.
Frequently Asked Questions
What exactly is alternative data, and how is it different from traditional data?
Alternative data refers to non-traditional, unconventional datasets sourced outside standard financial and business reporting channels. While traditional data includes earnings reports, balance sheets, and official economic statistics, alternative data encompasses web scraping signals, satellite imagery, social media sentiment, consumer transaction patterns, geolocation data, job posting analytics, ESG controversy monitoring, and more. For B2B decision-makers, alternative data provides a real-time, forward-looking lens on markets, competitors, and consumer behavior that traditional data simply cannot offer.
What industries does Hir Infotech serve with alternative data solutions?
Hir Infotech serves a wide range of industries with tailored alternative data services, including investment management and hedge funds, retail banking and fintech, e-commerce and consumer goods, supply chain and logistics, real estate and PropTech, media and brand intelligence, pharmaceutical and healthcare research, and B2B technology. Our datasets are customized to the specific signal types, compliance requirements, and delivery formats relevant to each sector — across the USA, Europe, and Australia.
How does Hir Infotech ensure GDPR and CCPA compliance in alternative data collection?
Compliance is a foundational design principle at Hir Infotech — not an add-on. Every alternative data project begins with a jurisdiction-specific legal review to establish the lawful basis for collection. Our pipelines apply strict data minimization, automatically filter personal identifiers before data enters our storage layer, respect robots.txt directives and platform terms of service, and maintain complete audit trails. We hold documented compliance records for every active engagement and can provide compliance documentation to enterprise procurement and legal teams upon request.
Can Hir Infotech build custom alternative data pipelines for proprietary use cases?
Yes. Hir Infotech specializes in bespoke alternative data pipeline design for clients with unique signal requirements. Our team works directly with your data science, quant, or strategy team to define the exact data types, sources, geographic coverage, refresh frequency, enrichment layers, and delivery format required. This includes building custom NLP classifiers, entity resolution logic, and change-detection algorithms tailored to your specific analytical models or product workflows.
. How quickly can Hir Infotech deliver a production-ready alternative data pipeline?
Delivery timelines depend on the complexity and scope of the use case. For standard alternative data scraping projects (pricing intelligence, competitive monitoring, review sentiment), we typically deliver a working pilot dataset within 5–10 business days. For enterprise-grade, multilingual, multi-source alternative data platforms with custom enrichment and API integration, a phased delivery model is used — with initial data delivery in 2–3 weeks and full production deployment within 4–8 weeks.
What delivery formats are available for alternative data from Hir Infotech?
Hir Infotech supports a full range of data delivery formats and integration methods to fit your existing data stack. These include REST API, webhook-based real-time push delivery, CSV, JSON, XML, Parquet flat files, and direct cloud-to-cloud delivery to AWS S3, Google Cloud Storage, Azure Blob Storage, Snowflake, BigQuery, and Redshift. We work with your data engineering team to configure the delivery method that minimizes integration effort and maximizes operational efficiency.
How does alternative data pricing work at Hir Infotech?
Hir Infotech offers flexible, transparent pricing tailored to the scope, volume, and complexity of each alternative data engagement. Pricing models include project-based (one-time extraction and delivery), subscription-based (ongoing data delivery with defined refresh cycles and SLAs), and fully managed service contracts (end-to-end data operations with dedicated account management). We offer a free sample dataset for qualified enterprise inquiries so your team can validate data quality before committing to a contract.
What makes Hir Infotech different from generic alternative data marketplaces?
Unlike off-the-shelf alternative data marketplaces that sell pre-packaged datasets to multiple buyers, Hir Infotech builds custom, client-specific alternative data pipelines that are designed around your exact signal requirements, industries, geographies, and delivery architecture. This means the data you receive is not simultaneously sold to your competitors, is built to your analytical schema, and comes with dedicated technical support and SLA-backed delivery. Our 13+ years of experience and 2,745+ client track record reflects the depth of execution — not just the breadth of a product catalogue.
Can alternative data from Hir Infotech integrate with our existing CRM, BI, or quant platforms?
Yes. Hir Infotech’s alternative data output is designed for seamless integration with leading enterprise platforms including Salesforce, HubSpot, Microsoft Dynamics, Power BI, Tableau, Looker, Snowflake, Databricks, AWS, and quantitative research environments (Python, R, MATLAB, Bloomberg Terminal). Our data engineers provide integration support as part of onboarding, including schema documentation, API connection guides, and test dataset validation to ensure immediate operational compatibility.
How does Hir Infotech ensure data quality and freshness in ongoing alternative data pipelines?
Data quality is maintained through a multi-layer quality assurance architecture: automated schema validation, anomaly detection algorithms, statistical consistency checks, and periodic manual audits by our data QA team. For real-time pipelines, we operate 24/7 monitoring with SLA-triggered alerts for any data gap or delivery delay. All pipelines include version control and change-log documentation so clients can track data schema evolution over time. Our standard SLA commits to 99.2% accuracy and 99.5% uptime for production alternative data feeds.