About Crawlexa

Engineered by Data Practitioners,
Built for Enterprise Scale

Crawlexa eliminates the fragility, proxy maintenance overhead, and developer burnout associated with web and mobile data harvesting. We deliver clean, structured data directly to where your decision-makers and AI models operate.

Founder & Principal Data Architect

Sagar Ahirrao

Former Enterprise Data & Systems Engineer at KPMG & Larsen & Toubro (L&T)

With over a decade of hands-on data systems architecture across global audit, advisory, and infrastructure conglomerates, Sagar founded Crawlexa to solve the foundational challenge of modern market intelligence: web scraping breaks, proxies fail, and internal teams waste thousands of hours fixing selectors rather than building products.

Our Philosophy

The Four Pillars of
Production Data Extraction

1. Strict Ethical Compliance

We harvest solely publicly accessible web & mobile app data. We never bypass authentications, scrape private user credentials, or collect protected personally identifiable information (PII).

2. Autonomous Self-Healing

When target websites alter their frontend DOM, schema layouts, or API response formats, our continuous telemetry detects drift within seconds and patches extractors before platforms stall.

3. Flat, Transparent Pricing

No proxy gigabyte surcharges, no surprise cloud bills, and no hidden setup penalties. Our universal baseline rate is $500 per 1M records ($0.0005/record) with 1st project development fee at $0.

4. Multi-Cloud Readiness

Direct native sync to Amazon S3, Google BigQuery, Snowflake, Azure Data Lake, PostgreSQL, and streaming REST webhooks in JSON, NDJSON, CSV, or Parquet.

Verified Corporate Presence

Global Engineering & Operations

Crawlexa operates engineering and Site Reliability Engineering (SRE) infrastructure globally, with our primary verified office registered in Ahmedabad, India.

Crawlexa Headquarters
Ghatlodia, Ahmedabad, Gujarat 380061, India
Direct: sales@crawlexa.com | Phone: +91 8160867884

Ready to Automate Your
External Data Pipelines?

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