Source inventory and permissions
Define which public or authorized sources are in scope, the permitted fields, refresh frequency, and retention policy before collection begins.
RAG ingestion network layer
AI data collection proxies for traceable, schema-validated web pipelines
Collect authorized public sources with an explicit route policy, normalize each response, validate extracted fields, and preserve provenance for RAG or analytics.
01Collectpublic source
02Validatestatus + locale
03Transformstructured record
04DeliverAPI or pipeline
Design the pipeline around evidence, not raw page volume
Transport selection
Use direct HTTP for stable pages and browser execution only when rendering is required; avoid paying browser and bandwidth costs by default.
Locale-aware routing
Align requested geography, language headers, and validation markers so localized pages are not silently mixed into one dataset.
Schema and quality gates
Validate required fields, types, ranges, and source markers before records enter a vector store or analytics table.
Provenance and replay
Store source URL, timestamp, route mode, response status, parser version, and a compact artifact so a record can be explained later.
Cost per accepted record
Measure transferred bytes, retries, render cost, and rejected records instead of optimizing only requests per second.
A four-stage AI data ingestion path
Keep transport, extraction, validation, and publishing separate so each failure can be classified and corrected without re-running the entire pipeline.
Classify the source
Choose an authorized target, expected locale, content markers, update cadence, and the minimum fields required downstream.
Fetch with policy
Apply direct, rotating, or short sticky routing with explicit timeouts, bounded concurrency, and stop conditions.
Normalize and extract
Remove navigation and boilerplate, convert the useful content to a stable format, and extract into a declared schema.
Validate and publish
Reject incomplete or contradictory records, attach provenance, and publish only accepted data to the RAG or analytics layer.
AI data collection FAQ
Do all RAG pipelines need residential proxies?
No. Use direct access or official APIs where they are reliable and permitted. Residential routing is useful when a legitimate public workflow depends on consumer-market geography or when datacenter routes do not represent the required observation.
Should the LLM decide when to retry?
The execution layer should enforce retry classes and budgets. The model may explain or select from approved actions, but it should not override authorization, concurrency, or stop rules.
What should be embedded in a vector database?
Embed normalized, source-linked content that passed quality checks. Do not embed challenge pages, consent walls, navigation boilerplate, empty extractions, or records without provenance.
How is collection cost measured?
Track bytes, render time, retries, rejected records, and model tokens per accepted record. This exposes workflows that look fast but produce little usable data.