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AI Big Data Spec

B2BComing soon

From raw signals to AI-ready pipelines. Spec covers ingestion, taxonomy, validator gates, decay.

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spec-ai-big-data · demoDemo

Task

From raw signals to AI-ready pipelines. Spec covers ingestion, taxonomy, validator gates, decay.

Result

  1. step 1Hot/Warm/Cold fact decay — never delete, supersede
  2. step 2PARA-style knowledge graph (Projects/Areas/Resources/Archives)
  3. step 3Nightly extraction cron + semantic search backend
Autocheck passed

A sample run on demo data — this is what the result looks like in the simulator.

Who it's for

Data leads whose RAG pipeline is silently stale and nobody's measuringCTOs sitting on years of logs but no taxonomyFounders building a vertical AI on proprietary data they own outrightHeads of platform asked to make 'company knowledge' searchable by Friday

When you'll reach for this

01

Your RAG returns confidently wrong answers from 2022 docs

Hot/Warm/Cold decay supersedes old facts instead of deleting them. Recency wins by design, audit trail intact.

02

You have a data lake nobody can query without a Slack thread

PARA-style taxonomy gives every fact a home — Projects, Areas, Resources, Archives — and search starts behaving.

03

Your validator catches schema drift after it's already in prod

Validator gates run at ingestion. Bad rows bounce before they reach the index.

04

You're hiring a data engineer and want the work scoped honestly

The deliverable is a buildable spec — cron schedules, schemas, gates — not a slide deck of intent.

Powers you walk away with

  • Да: Design a RAG that ages gracefully instead of rotting in silence
  • Да: Impose a taxonomy on years of unstructured data without a year-long project
  • Да: Set validator gates that pay for themselves the first time they bounce a row
  • Да: Spec a data pipeline a senior engineer can build in a sprint

What's inside

  1. 01Hot/Warm/Cold fact decay — never delete, supersede
  2. 02PARA-style knowledge graph (Projects/Areas/Resources/Archives)
  3. 03Nightly extraction cron + semantic search backend

Same decay + taxonomy patterns FolderAI uses for its own knowledge graph.

Pairs well with

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AI Work Spec

Adaptive quiz → tailored AI-Work specification, with five live test runs proving it works.

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