The challenge
- Gathering data manually from multiple websites was slow, resource-intensive and produced inconsistent output quality.
- Traditional scraping scripts could not handle concurrent, large-scale crawling reliably or without constant maintenance.
- Raw HTML had to be cleaned and formatted by hand before feeding AI pipelines, adding lag between crawl and training.
- Connecting crawled content to LLM APIs required custom transformation, deduplication and schema normalisation.
- Secure API key management, encrypted transit and IAM-controlled access to cloud resources were non-negotiable.
How the platform runs
Containerised crawler on EC2
Crawl4AI runs in an isolated Docker container, decoupled from the host, which enables fast rollbacks and horizontal scaling.
Job queue with status tracking
Each crawl request gets a unique job ID. An asynchronous queue ensures ordered execution and prevents resource exhaustion.
Post-crawl LLM enrichment
After each page is fetched, content is passed to the LLM API for entity extraction and summarisation before being written to S3.
Scheduled crawls and monitoring
Recurring batch crawls run on schedule. CloudWatch metrics and alarms track success rate, error counts and LLM API latency, with instant alerts on anomalies.
What Cloudlit delivered
AWS deployment
Crawl4AI deployed on Amazon EC2 using the official Docker image for portability, version pinning and environment consistency.
REST API
FastAPI endpoints let engineers submit crawl targets, track job status in real time and retrieve structured JSON results on demand.
LLM integration
GPT-4 and Claude APIs classify page intent, extract named entities, summarise content and tag sentiment automatically after each crawl.
Security and compliance
API key authentication, HTTPS-only endpoints, VPC isolation, security-group rules and IAM roles enforce least privilege end to end.
Storage and data management
Crawled outputs stored in Amazon S3 with lifecycle policies. Structured metadata indexed in DynamoDB for fast retrieval.
Results
- Automated crawling eliminated manual data collection, reduced overhead and significantly increased processing speed.
- LLM integration enabled real-time content analysis, sentiment detection and intelligent summarisation.
- The Dockerised architecture scales across on-premise, cloud and hybrid environments.
- High-quality, LLM-enriched datasets ready for analytics, modelling and conversational AI assistants.