#1139 — Processor matching
Repo: Twill-AI/facade State: open | Status: open Assignee: meliascosta
Created: 2026-03-19 · Updated: 2026-03-19
Description
Summary
Implement processor matching / suitability for merchants whose onboarding application has enough data: evaluate which payment processors (PayEngine, Luqra, EMS, etc.) are suitable to file the application, persist results, recompute when merchant context changes, and notify the partner UI (e.g. via existing activity → WebSocket path).
This work is a concrete slice of the NBA / AI-assisted onboarding epic (#977); backend design notes align with signal ingestion (#983), persistence (#979), and WS/action delivery (#981).
Decision engine: suitability and processor ranking/matching MUST be produced using an LLM integrated from the twill_llm_engine repo (not hard-coded rules-only long term). Facade orchestrates: build a structured merchant snapshot + processor constraints/prompts, call the llm_engine, validate/persist structured output, then emit realtime updates.
Scope (high level)
- Trigger — When application data crosses an explicit completeness gate (and again when fingerprinted inputs change): schedule background evaluation (e.g. after partner/master merchant
PUT, after doc-driven application updates). - LLM — Add/adapt a twill_llm_engine capability (prompt + response schema) that takes normalized merchant context and returns per-processor suitability (eligible / not, rationale, optional ordering or confidence).
- Persistence — Store results +
input_fingerprint+ ti
Notes
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Related
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