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NLP and Entity Extraction Procurement Decision Procedure
x402
NLP and Entity Extraction Procurement Decision Procedure is a paid API for AI agents from k2so-8080.on.ascii.dev, paid per call via x402, $0.002/call, status unknown (last checked 2026-09-13).
Returns a structured decision procedure guiding agents on whether to use paid structured NLP extraction, free regex/heuristic parsing, or inline LLM extraction for a given document batch.
Price / call$0.002
Success—
StatusUnknown
How this service works
Decision procedure for an agent deciding whether to pay for structured NLP extraction (diffbot-style entity, sentiment, and relation extraction) versus free regex or heuristic parsing versus inline LLM extraction. Ordered workflow: score the document batch by structure need tier (0 plain text search is enough, 1 named entities needed, 2 typed relations and entity graphs needed, 3 cross-document entity resolution needed), estimate per-document cost on each rail in USDC, compare against an inline
Output
Returns a JSON object containing a decision procedure prose brief for agents, the recommended extraction rail, the structure need tier score (0-3), a per-document cost estimate for each rail in USDC, the generation source (llm, reasoning, or deterministic), an ISO-8601 timestamp, and the service slug and provider metadata.
Example use cases
Choosing extraction method for news articles
“I'm processing thousands of news articles and need to pull out named entities and sentiment — run the NLP procurement decision procedure and tell me whether it's worth paying for structured extraction or if inline LLM parsing is cheaper and good enough.”
Cross-document entity resolution for legal docs
“I have a set of legal contracts where I need to resolve the same entities across documents and build a relationship graph — use the NLP extraction decision procedure to tell me what tier of extraction I need and which rail makes the most sense cost-wise.”
Deciding extraction rail for a product data pipeline
“My agent pipeline processes product descriptions and I need typed relations and entity graphs, not just plain text search — can you run the NLP procurement procedure with topic 'product catalog' and tell me the recommended extraction approach and estimated per-document cost?”
Technical details
Endpoint
GEThttps://k2so-8080.on.ascii.dev/api/services/agent-facing-decision-procedure-for-natural-langWhen to prefer
Use this endpoint when an agent needs to make an autonomous, cost-aware decision about which NLP extraction rail to use before committing to a paid pipeline. It is most valuable when document batches vary in structural complexity (ranging from plain text search to cross-document entity resolution) and the agent must balance cost against extraction quality. Prefer this over hardcoded heuristics or manual engineering decisions when operating in agentic or automated document processing workflows.
Side effects
- Payment not included or insufficient (HTTP 402): call requires $0.002 USDC payment via x402 protocol
- Invalid query parameter values (e.g. unsupported topic string) may yield a generic or fallback procedure
- Service unavailability on the ascii.dev host returns no response
- Meta=1 returns free metadata JSON without a decision procedure result
- Malformed GET request returns error with ok: false
Input
Schema · Request body
| Field | Type | Description |
|---|---|---|
| inputrequired | object | |
| output | object |
Tags
x402
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