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for our clients
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How we apply our
expertise
We combine 15+ years of software engineering heritage with deep expertise in modern data platforms, applied AI and product-led delivery — to build the data systems behind world-class businesses.
Data Engineering & Platforms
01.Scalable data infrastructure on Snowflake, Databricks and AWS that your teams can actually use.
Data Engineering
& Platforms
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01
AI, ML & Generative Intelligence
02.Custom ML models, LLM applications, agentic AI and decision intelligence systems — from prototype to production at scale.
AI, ML & Generative
Intelligence
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02
Custom Data Products
03.Tailored data applications, MVPs and external-data pipelines that turn raw information into a competitive advantage.
Custom Data Products
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03
Ready to begin building your custom web scraping solution?
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Solutions
Our knowledge and experience, packaged into solutions addressing the most popular challenges
Data
Aggregator
We develop a multi-source data scraping platform exclusively owned by you
Insights
Radar
We develop custom software to extract insights from publicly available data exclusively for you.
Compliance
Leaders in
Data Security
We’ve set the gold standard for ethical web data practices, ensuring compliance and trust. With a commitment to zero personal data collection, your privacy and security are our top priorities.
GDPR
Compliant
ISO 27001
Certified
HIPAA
Compliant
Our partnerships and awards
We extract structured data from complex, geo-restricted sources—eCommerce platforms, mobile apps, marketplaces, real estate directories, and review or comparison sites. Each system is built for adaptability. We replicate actual user behavior across regions using rotating residential and mobile proxies. IP bans, CAPTCHAs, or dynamic JavaScript are anticipated. If a proxy fails, we reroute. If access rules change, we adapt. Resilience is engineered from the start, not patched later.
Yes. We can match your data schema or create a structured format aligned to your objectives. Every column, header, and data type is defined before launch, so nothing is left to assumption.
Once the scope is confirmed, delivery begins immediately. The launch pipeline includes technical discovery, scraper engineering with QA, a proof-of-concept in a test region, and full deployment. Results start appearing within days. Our systems scale from 10,000 to over 900,000 daily requests, rolled out in phases to ensure reliability and compliance.
The proof-of-concept includes 10K–100K targeted requests in a defined region or category, executed over several days. It delivers clean datasets, QA logs, and diagnostics, validating technical readiness and business value before scaling.
We follow a stepwise model. The pilot runs at 10K–100K daily requests for tuning. Next, the infrastructure scales to 300K per day. We then trial 900K+ per day under high load for one to two months. Long-term operations stabilize around 300K per day, balancing throughput and system integrity.
Rarely—and never without a fallback. Scraping traffic is spread across a proxy mesh designed to mimic real users. No IP, region, or fingerprint is overloaded. The load is ramped gradually. If a provider blocks traffic, we reroute instantly.
We build data pipelines—not just scrapers. During onboarding, we assess your stack (databases, APIs, BI tools) and configure delivery through ETL/ELT workflows, data lake ingestion, direct DB writes, or API syncs. Every integration is secure, efficient, and tailored.
Costs depend on source complexity, update frequency, request volume, anti-bot defenses (e.g., CAPTCHA, headless JS), and output format (JSON, CSV, API, or DB). We design for scale and longevity—short-term fixes aren’t our approach. Large, recurring operations see meaningful cost efficiency.
Support is entirely in-house. You receive technical documentation (source maps, schemas, workflows), a dedicated engineering team, and a clear SLA for issue response and continuity. We remain engaged after go-live, stabilizing and optimizing your system as it evolves.
Because real-time scraping needs real-time car, post-launch systems must adjust to frontend/API changes, rebalance loads, and evolve with business needs across teams. Part-time support may be possible later, but for the first 90–180 days, full-time attention prevents instability and data loss.
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