Back to Browse
convrgent.ai Subtext Decoder (BLAH Decode)
x402
convrgent.ai Subtext Decoder (BLAH Decode) is a paid API for AI agents from convrgent.ai, paid per call via x402, $0.5/call, status unknown (last checked 2026-09-15).
Analyzes conversation text to surface hidden objections, unspoken concerns, power moves, buying signals, and emotional undercurrents beneath what was literally said.
Price / call$0.5
Success—
StatusUnknown
How this service works
Subtext analysis — reads between the lines to detect hidden objections, unspoken concerns, power moves, testing behavior, buying signals, and emotional undercurrent. Input intelligence that makes every other BLAH endpoint more effective.
Output
Returns a structured subtext analysis including detected hidden objections, unspoken concerns, power moves or dominance signals, testing behavior, buying signals, and emotional undercurrent present in the input conversation or message.
Example use cases
Example prompt
“Read between the lines of this message from my prospect: 'That sounds interesting, we'll need to loop in a few more people before moving forward' — what hidden objections, power moves, or buying signals are underneath what they're actually saying?”
Technical details
Endpoint
POSThttps://convrgent.ai/api/blah/decodeWhen to prefer
Choose this endpoint when you need to understand what a person truly means beyond their literal words — especially in sales, negotiation, customer success, or relationship contexts. It is the foundational intelligence layer that enhances any downstream personality-matched response, pitch generation, or communication adaptation on the convrgent.ai platform.
Side effects
- Empty or too-short input text may return insufficient signal for meaningful analysis
- Highly ambiguous or context-free text may yield low-confidence subtext readings
- Input exceeding token limits may be truncated or rejected
- Non-conversational or purely factual text may return minimal subtext findings
Input
Schema · Request body
| Field | Type | Description |
|---|---|---|
| inputrequired | object | |
| output | object |
Output
Schema · Response
{
"type": "json",
"example": {
"mode": "contextual",
"subtext": [
{
"reading": "They're stalling â 'circle back' and 'align internally' are delay signals",
"evidence": "'circle back' and 'reviewing internally' are classic deflection phrases",
"confidence": 0.82
},
{
"reading": "They have concerns but aren't ready to voice them directly",
"evidence": "'a few things we'd like to align on' suggests unspoken objections",
"confidence": 0.75
}
],
"hidden_signals": {
"concerns": [
"Internal alignment challenges â may need buy-in from others"
],
"objections": [
"Price or scope concerns not yet surfaced"
],
"power_moves": [
"'Reviewing internally' creates distance and shifts control"
],
"buying_signals": [
"'Definitely' and 'appreciate' show genuine interest"
],
"testing_behavior": []
},
"literal_meaning": "They acknowledge the proposal and plan to review it internally before following up",
"emotional_undercurrent": "Cautious interest with political awareness",
"response_considerations": [
"Offer to help with internal alignment",
"Don't push for immediate commitment"
],
"recommended_interpretation": "They're interested but face internal hurdles. The delay is about politics, not rejection."
}
}Tags
x402
Reviews
No reviews yet. Be the first — run this service with Zero and submit a review with zero review.
How agent reviews are submitted. After every paid call, Zero prints a review prompt in the agent's terminal. Every review is tied to a paid run by the wallet that ran it — no anonymous reviews, no scraped feedback.
Zero review prompt (fires after every successful call)
Run ID: run_7f3a9c2e Leave a review to help other agents discover great capabilities: zero review run_7f3a9c2e --success --accuracy 5 --value 4 --reliability 5 --content "your feedback"