PRISM Engine.
40% of bank-FinTech partnerships fail, according to EY-Parthenon's 2025 report. The #1 cause: wrong fit at the outset. PRISM is TECH's proprietary three-layer AI matching system designed to cut that number. It ranks 30,000+ global FinTechs against every FDIC-insured US institution using vector similarity, LLM fit scoring, and continuous feedback reinforcement — all with PII stripped before anything touches a third-party model.
What it does
PRISM turns the question "who should we partner with?" into a ranked, explainable short-list. Every night at 06:00 UTC, and on-demand any time a user clicks Regenerate, PRISM evaluates your organization's profile, strategy, and historic feedback against every other member and returns the top 10 matches — each one scored, each one explained in plain English.
Matches are symmetric: when the engine surfaces a bank for your FinTech, that same bank independently sees your FinTech on their own feed. A mutual match — where both sides click Accept — opens a FINRA-compliant messaging channel between the two organizations.
Output: ranked top-10 partners with a 1-10 fit score, AI rationale, and shared objectives.
Latency: ~60 seconds for a full regeneration (8 candidates × Claude scoring). Runs in a Celery background task so the UI stays responsive.
How it works — three layers
PRISM is deliberately architected as three sequential layers, each narrowing the candidate set and adding more expensive intelligence. The split means we can afford to run layer 2 (the LLM) only on the strongest candidates from layer 1 — keeping cost linear in membership size, not quadratic.
Every organization has a 1,536-dimensional embedding generated from
its name, sector, stage, description, partnership objectives, and
target criteria. We use PostgreSQL 16 with the
pgvector extension (version 0.8.2) to run a cosine-distance
nearest-neighbour query against the entire membership in a single SQL
round-trip. The top 50 candidates graduate to layer 2.
Each of the top 8 candidates is scored by Anthropic's
claude-sonnet-4-5 model against a structured rubric
covering problem alignment, technical compatibility, regulatory
posture, cultural fit, and strategic value. The model returns a 1-10
fit score plus a short narrative explanation that becomes the
match's Why This Match card in the UI. All PII is
stripped before any prompt reaches the Claude API.
When you accept, decline, rate, or message a match, PRISM records the signal and adjusts future rankings. Decline a bank three times in a row and the ranker learns to down-weight banks of that type for you. Accept a partnership and successfully close a deal in CORTEX and the engine amplifies the signal that brought you there. Each organization ends up with a personalised ranker that improves with every interaction.
When it acts
PRISM is always-on. There are four triggers that produce a new ranking:
- Scheduled daily refresh — every day at 06:00 UTC, PRISM regenerates the top 10 for every KYB-verified member with active users. This is driven by a Celery Beat task and ensures your feed is fresh every morning.
- On-demand — clicking Regenerate matches in the app queues a Celery task and returns an HTTP 202 immediately. Your feed updates as rows are written; the UI polls every 5 seconds for up to 90 seconds.
- Profile change — editing your org profile, partnership objectives, or target criteria invalidates your embedding and triggers a regeneration on your next login.
- Feedback loop — accepting or declining a match updates the feedback signals used by layer 3 on the next run.
Privacy and compliance
PRISM is designed to be compatible with the full compliance stack TECH operates under: FINRA Rule 4511, SEC Marketing Rule 206(4)-1, CCPA/CPRA, GLBA, BSA/AML, and DORA. Three design choices make this possible:
Every match is AI-labelled. The API response carries an
is_ai_generated: true flag, surfaced on the UI with an AI badge per SEC Marketing Rule 206(4)-1.Immutable audit trail. Every match generation, acceptance, decline, and feedback event writes to the SHIELD compliance log with user, IP, timestamp, and (if applicable) model version.
Specifications
API surface
PRISM is exposed through the authenticated REST API under
/api/v1/matching/. The endpoints your app uses on every
login:
Open the Matches page in the app.
Sign in, open Matches, and watch PRISM's ranked feed. Click the Why This Match card on any result to see the exact AI rationale — the output of layer 2 described above.
Open Matches →