FEEDS FOR AI AGENTS & RAG

Your agent is confident.
Make it correct.

// THE SHORT ANSWER

RAG and agent feeds are live web data prepared for retrieval: prices, availability, menus, reviews and catalog facts refreshed minutes-to-hourly, delivered as pre-chunked passages with embedding-friendly metadata over webhooks or API. Training data teaches your model the world as it was; these feeds let your product answer with the world as it is. One shopping-assistant client cut answer-time data staleness to under 15 minutes median — because a stale price quoted confidently is just a wrong answer.

Get free sample in 48h See delivery specs
99%+field accuracy, QA-verified
48hfree sample turnaround
24/7pipeline monitoring
ISO 27001+ 9001 certified

Key facts

  • Freshness: Minutes (campaign/price-critical) to hourly (standard) · per-field SLAs, reported
  • Delivery: Webhooks · REST API · queue/bucket drops (S3, GCS, Pub/Sub)
  • Format: Chunked JSON: text passage + metadata + stable ID · raw structured records also available
  • Scale: From 10K to millions of tracked entities; burst refresh for launches

Every AI product that touches commerce discovers the same failure mode in its first month: the model answers fluently with facts that expired yesterday. Screenshots circulate. Trust drains. The fix isn't a better model — it's retrieval running on data as fresh as the claim.

These feeds are that layer. We monitor the platforms your product speaks about, refresh on an SLA measured in minutes-to-hours per field, and deliver retrieval-ready: chunked passages sized for embedding, stable IDs for upserts, and metadata (platform, location, timestamp, entity) your retriever filters on. Push via webhooks into your vector store, or pull via API at answer time. The shopping-agent case study shows the shape of a production deployment; the AI-ready checklist explains why RAG preparation differs from training prep.

WHAT'S INCLUDED

Retrieval-ready,
not just recent.

Freshness as an SLA

Per-field refresh targets — minutes for prices in campaign windows, hourly standard — stated and reported.

Pre-chunked passages

Records rendered as retrieval-sized text chunks with consistent structure, ready for your embedding model.

Stable IDs & upserts

Deterministic chunk IDs so your vector store updates in place — no duplicate-answer drift.

Rich filterable metadata

Platform, geography, entity, timestamp and category on every chunk — your retriever narrows before it searches.

Webhook push or API pull

Standing pushes into your pipeline, or on-demand pulls at answer time — most products use both.

Entity-matched facts

The same product resolved across platforms, so 'cheapest price for X' is retrievable as one fact, not five conflicting ones.

SEE THE DATA FIRST

What you'll actually receive.

Real sample structure from this feed. Your free 48-hour sample comes in your category, in this shape — CSV, JSON or straight to your warehouse.

RAG-ready chunk — text passage + retrieval metadata + stable id.
field value
chunk_id sku_44921_p
text boAt Rockerz 450 — INR 1499, in stock...
entity boAt Rockerz 450
platform Amazon IN
price 1499.00
freshness_min 7
updated_at 2026-07-08 18:07
↑ Sample structure — illustrative values. Your data reflects your platforms and category. Get this for your data →
DELIVERY SPECS

The details procurement asks for.

FRESHNESS Minutes (campaign/price-critical) to hourly (standard) · per-field SLAs, reported
DELIVERY Webhooks · REST API · queue/bucket drops (S3, GCS, Pub/Sub)
FORMAT Chunked JSON: text passage + metadata + stable ID · raw structured records also available
SCALE From 10K to millions of tracked entities; burst refresh for launches
TIMELINE Sample feed in 48h · production integration typically 1–2 weeks
PRICING By entities tracked × refresh frequency; pilot tiers for pre-launch products
COMPLIANCE Public data only · PII-scrubbed · ISO 9001/27001 · NDA-first

Every engagement is NDA-first and starts with a free sample — judge the data before any commitment.

THE HONEST COMPARISON

Why a data layer, not a DIY script or a rigid tool

In-house DIY Generic SaaS tool iWeb Data Scraping
Setup & maintenance You build scrapers, fight anti-bot, fix breakages weekly Rigid templates, breaks on site changes, slow support Fully managed — we build, monitor and fix, you never touch a proxy
Data quality Best-effort, no QA layer, silent failures Generic parsers, frequent gaps 99%+ field accuracy, QA-verified, monitored 24/7
Coverage Limited to what you can maintain Only supported sites Any public site or app, at scale
Compliance Your legal risk to manage alone Often opaque about methods ISO-certified, PII-scrubbed, NDA-first, documented
Time to value Weeks to months of engineering Fast but inflexible Free sample in 48h, production in days
WHO USES THIS

Built for the person
who owns the number.

AI PRODUCT ENGINEER

Shopping & pricing assistants

Live price and availability behind every answer — the deployment pattern in this case study hit <15 min median freshness.

PLATFORM TEAM

Agents that check before they claim

Tool-calling agents pull current facts at decision time instead of trusting last week's index.

SEARCH & DISCOVERY

RAG over a living catalog

Product search that knows what's in stock now — retrieval quality follows data freshness.

RELATED:AI & LLM Training Data →Scraping API →Managed Web Scraping →
FAQ

Before the first call.

Consumption and preparation. Training data is ingested offline in bulk, so volume, deduplication and provenance dominate. RAG data is retrieved at answer time, so freshness, chunking and filterable metadata dominate. Same web sources, opposite last mile — buying one prepared as the other is the most common mistake we rescue.

It's an SLA, not an adjective: standard feeds refresh hourly; price-critical entities can refresh in minutes during defined windows. We report achieved freshness, and one production shopping assistant runs at under 15 minutes median between platform change and retrievable chunk.

We deliver chunks with stable IDs and metadata over webhooks or API; your pipeline embeds and upserts into any store — Pinecone, Weaviate, pgvector, Elasticsearch and peers. We stay embedding-model-agnostic deliberately so your retrieval stack remains yours.

Yes — feeds support a managed entity list your product can append to via API. New products or locations requested by users enter monitoring automatically, with first data typically available within the next refresh cycle.

Get a free sample dataset in 48 hours.