Define a complex schema once. An agent learns each website at design time and writes typed extraction stages; A3 then replays them at scale with no LLM in the loop — so cost stays close to raw scraping, not agentic re-parsing on every row.
You need highly structured rows from dozens or hundreds of websites — nested fields, normalisation, provenance. Agentic extraction that re-reasons over every page works for a demo; at daily refreshes across a whole source list, model cost and latency take over.
Per-page LLM calls add up fast. Complex schemas make each invocation heavier — and you still need a separate integration per site, with no guarantee rows match your warehouse shape.
Intelligence stays in the learning phase. The agent visits each source once, writes stages against your schema, and hands off to a deterministic runner — locally or in the cloud — that extracts without a model in the loop.
LLM on every row — agentic platforms re-parse each page at collection time; cost scales linearly with volume.
LLM at design time — the agent learns each source once; production runs replay compiled stages with zero model spend.
Flat, inconsistent output — free-form extraction returns a different shape per site; normalisation is a second project.
Complex typed schemas — define nested records once; every site maps into the same validated output, automatically.
Manual per-site work — engineers hand-write scrapers or prompt-tune each source; onboarding a new site is slow.
Agent learns each site — point A3 at a URL, describe the schema, and it writes stages and a playbook for that source.
Hard to fan out — running thousands of extractions daily across many domains is ops-heavy and expensive.
Scale without token cost — schedule and fan out collection with configurable concurrency; extraction cost stays at runner rates, not token rates.
Sites drift — a redesign breaks extraction; agentic re-prompting on every failure is slow and still costly.
Self-healing stages — when a page stops matching, the learning agent auto-fixes the stage in real time; the runner keeps collecting.
Data teams need structure and scale, not another platform that bills per page. A3 separates design-time learning from production extraction — so you get agent-quality dataset builds at a cost profile closer to traditional scraping.
Real estate, retail, travel, and firmographics look different on the surface — but you define one schema and onboard source after source. A3 learns each site's collection path once, then replays it on a schedule at scrape-scale cost.
Define your schema and learn each source in the Product ADE, then schedule collection at scale — structured rows from hundreds of sites, without paying model tokens on every refresh.