ScrapeZen is a boutique DaaS agency. We extract, normalize, and human-verify complex web data — transforming raw information into perfectly structured assets for your Large Language Models and RAG pipelines.
Trusted by AI teams at
The structural shift toward data-centric AI means model performance relies entirely on data quality. Generic scraping tools leave you with messy data — your AI requires precision.
Unstructured data introduces noise that causes LLMs to generate confident but incorrect answers.
Inconsistent formats, duplicate records, and missing fields silently erode your model's accuracy.
Your AI engineers spend 80% of their time cleaning data instead of building features.
Purpose-built data pipelines engineered for the demands of modern AI architectures.
We understand the regulatory constraints and data precision requirements of mission-critical sectors.
Industry research consistently shows that investing in optimized data is about more than cost savings — high-quality training data can improve predictive accuracy by over 20%.
Faster, more accurate insights from your AI models with clean, normalized input data.
Based on McKinsey research on data-centric AI adoption
Improvement in predictive accuracy. Reduced hallucinations and better compliance tracking.
MIT Sloan: 'How AI-Ready Data Improves Prediction Accuracy'
Less time spent on data cleaning. Accelerated time-to-market for your proprietary AI tools.
IBM: Data scientists spend 80% of time on data preparation
Our processes are strictly aligned with global regulations so you can scale your AI with complete confidence.
Full alignment with EU data protection regulations.
Proactive compliance with the latest AI governance frameworks.
Automated detection and redaction of personally identifiable information.
Complete provenance tracking for every data transformation step.
Domain experts dedicated to delivering AI-ready data with precision and care.
CEO & Data Engineer
Leads product vision and core data engineering architecture at ScrapeZen.
Developer & Data Engineer
Full-stack development and data pipeline engineering across the platform.
AI & DevOps Data Engineer
AI systems, model integration, and cloud infrastructure & DevOps.
A methodical, four-step approach designed for transparency and precision.
We consult with your engineering team to define exact data structure needs and AI goals.
We build resilient, adaptive extraction pipelines that bypass complex web blocks.
We use Evaluation-Driven Development (EDD) to continuously test and improve output quality.
Data delivered via your preferred cloud API with ongoing monitoring for consistent quality.
See how our pipeline transforms raw, messy data into structured, LLM-ready assets.
Healthcare Clinical Trial Data — Proof of Concept
Before
After
A healthcare AI startup needed to train their clinical decision support model on publicly available clinical trial data. The source data consisted of 50,000 HTML pages with inconsistent date formats, embedded PII in researcher notes, and no standardized schema. Our pipeline extracted structured records, normalized all dates to ISO 8601, performed automated PII detection and masking, and delivered a clean JSON dataset ready for fine-tuning — all within 5 business days.
Request Your Free PoCBased on an internal proof-of-concept using publicly available clinical trial data.
Flexible engagement models designed to match your data needs and budget.
One-off engagements
Flat-rate pricing for custom data extraction and historical dataset compilation.
Final pricing based on data volume, complexity, and delivery SLAs
Continuous pipelines
Predictable, usage-based pricing for real-time data pipelines and ongoing HITL maintenance.
Final pricing based on data volume, complexity, and delivery SLAs
Request a free, no-obligation Proof of Concept. We'll extract, normalize, and verify a sample dataset specific to your use case.
Technical questions from CTOs and AI engineers about enterprise data pipelines — answered.