Annotate text with a verifiable four-layer cascade.
CascadeAnnote turns raw text into labeled, attested intelligence. Retrieval, chain-of-thought, self-consistency voting, and adaptive fallback — wired to decentralized storage so every annotation carries proof.
Four layers. One verifiable answer.
Each layer narrows the search space and produces a structured trace. If any layer is uncertain, the next layer compensates — and every step is recorded for audit.
Dynamic ICL retrieval
L1 · TF-IDF + cosineBuilds a TF-IDF vector space across the corpus and surfaces the top-K most similar labeled examples.
Chain-of-thought prompt
L2 · 5-step CoTComposes a structured five-step reasoning prompt with the retrieved exemplars as evidence.
Self-consistency vote
L3 · 5× samplingSamples five inference runs across diverse temperatures and majority-votes the final label.
Adaptive fallback
L4 · cold re-voteIf confidence < threshold, widens the example window with cooler temperatures and re-votes.
Provider-agnostic. Verifiable. Production-ready.
Pluggable inference
Built-in deterministic ICL classifier, plus opt-in adapters for OpenAI and 0G Compute. Swap providers via env vars.
Verifiable annotations
Every annotation produces a content-addressed receipt with sha-256 root hash and an explorer-linkable transaction.
Bring-your-own corpus
Upload labeled CSV or JSON datasets. The retriever rebuilds in milliseconds and the studio adapts to your label set.
Wired into the 0G modular infrastructure.
Annotation receipts content-address into 0G Storage. Inference can route through 0G Compute. Agent identity and run history live on 0G Chain. Run with or without keys — fallback is local.
Inspect storage receiptsType a sentence, get a verified label.
The studio runs the entire four-layer pipeline against the seed corpus and returns a confidence score, a chain-of-thought trace, the supporting examples, and a storage receipt.