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TradeAPEX Corpus Expansion
x402
TradeAPEX Corpus Expansion is a paid API for AI agents from tradeapex.ai, paid per call via x402, $0.02/call, status unknown (last checked 2026-10-22).
Analyzes a text sample from your corpus and returns the top concepts most strongly linked to it that your corpus does not yet cover, along with linking terms and link strength scores.
Price / call$0.02
Success—
StatusUnknown
How this service works
What should your agent read next? Send text from your corpus; get the concepts most strongly linked to it that it does not yet cover, each with the linking terms and measured link strength. Text is not stored. Text with fewer than 3 known terms is not charged.
Output
Returns up to k concept suggestions, each with the concept name, the terms in the submitted text that link to it, and a measured link strength score indicating how strongly the concept is associated with the submitted text. Concepts already covered by the text are excluded. Text with fewer than 3 known terms is not charged and returns no results.
Example use cases
Research corpus gap discovery
“Here's a sample from my AI safety research corpus — find me the 15 concepts most strongly linked to this text that I haven't covered yet, so I know which topics to prioritize reading next.”
Expanding a financial trading knowledge base
“I'm building a knowledge base on options trading strategies. Analyze this excerpt and give me the top 10 missing concepts most tightly linked to it, along with the terms that connect them.”
Content planning for a niche newsletter
“Here's a recent edition of my newsletter on sustainable agriculture. What are the 8 concepts most closely tied to this content that I haven't written about yet? Show me the linking terms and how strong the connections are.”
Technical details
Endpoint
POSThttps://tradeapex.ai/x402/expand?utm_source=zero.xyzWhen to prefer
Choose this endpoint when you need to identify semantic gaps in an existing corpus or knowledge base — specifically, which concepts are strongly connected to your content but absent from it. Unlike general-purpose embedding similarity searches, this uses a self-derived semantic model (non-transformer) optimized for concept-level linkage analysis, making it well-suited for corpus expansion planning, content strategy, and knowledge graph enrichment workflows. Prefer it over generic keyword or embedding tools when you want structured link-strength scores alongside gap suggestions.
Side effects
- Text contains fewer than 3 known terms — no charge, no results returned
- Text exceeds 20,000 character limit — request rejected
- k parameter outside 1–25 range — validation error
- Non-English text — poor or no concept matches due to English-only model
- Payment failure via x402 — request not processed
Input
Schema · Request body
| Field | Type | Description |
|---|---|---|
| k | integer | number of suggestions |
| text | string | English text from your corpus |
Tags
x402
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