Live in production · cellix.ai

Payment intelligence that
learns from every outcome.

AI-powered dispute defense, real-time monitoring, and fraud prevention — across every processor you use. Built on nights and weekends by someone who works on these problems at Adyen by day and can't stop thinking about them after hours.

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27+
Total integrations — processors, carriers, CRMs, support tools
50ms
Average dispute decision response time
99.9%
Historical platform uptime
<48h
Time for teams to go live
Platform

Three pillars of
payment intelligence.

Cellix connects every processor, carrier, CRM, and support tool into one source of truth — then applies AI to automate the work that payments teams do manually.

⚡

Dispute Automation

AI agents investigate every chargeback, assemble evidence to Visa CE3.0 and Mastercard network specifications, and submit directly via processor APIs.

  • Auto-generates evidence packages per card network spec
  • Direct API submission to all supported processors
  • Configurable auto-submit thresholds or manual review
  • Win probability scoring across 40+ signals
  • First dispute recovered within 7 days of onboarding
📈

Payment Monitoring

Real-time authorization rates, issuer declines, and anomaly detection — with cross-processor visibility and second-level alert response times.

  • Cross-processor auth rate dashboards
  • Issuer decline pattern analysis
  • Anomaly detection with instant alerting
  • Historical trend tracking and forecasting
  • Processor-level performance comparison
🛡

Fraud Prevention

Pre-checkout ML scoring, card testing detection, velocity controls, and BIN intelligence — blocking fraudulent orders before they ship.

  • Pre-authorization ML fraud scoring
  • Card testing and enumeration detection
  • Velocity controls with custom thresholds
  • BIN intelligence and issuer risk signals
  • Outcome learning loop that improves with every decision
Intelligence engine

Machine learning that
gets smarter with every dispute.

The intelligence engine scores every dispute across 40+ signals, executes the optimal decision, and feeds outcomes back into the model.

Signal layer
Win Probability Scoring
Confidence scores based on evidence quality, reason codes, processor history, and merchant patterns — computed in real time across 40+ weighted signals.
Learning layer
Outcome Learning Loop
Won disputes reinforce winning signals. Losses trigger automatic recalibration. The model improves with every outcome — not just every deployment.
Visibility layer
Calibration Tracking
Dashboards show prediction accuracy mapping — how well the model's confidence scores align with actual outcomes, surfacing drift before it impacts win rates.
The build story

From domain expertise
to production platform.

Cellix.ai exists because I kept seeing the same problems at every company — and decided to try building something about it.

After pitching fraud tools at PayPal/Braintree, building a risk engine from scratch at Paze, scaling a fraud team at Chime, and working on authorization optimization and ML fraud prevention at Adyen, the pattern was clear: every payments team solves the same problems with different tools, no shared intelligence, and massive manual overhead.

Cellix.ai encodes that domain knowledge into a production platform. It connects to 7 processors (Stripe, Adyen, Checkout.com, Braintree, Square, PayPal, Shopify Payments), 5 shipping carriers, 5 OMS platforms, 5 CRM tools, and 5 support platforms — creating a single source of truth for dispute defense, fraud prevention, and payment monitoring.

1

Domain research and specification

Wrote a 14-page technical specification covering every processor's dispute API surface, card network evidence requirements (Visa CE3.0, Mastercard), and ML signal architecture.

2

Architecture and infrastructure

Designed the platform architecture on AWS and Vercel. Built using Claude Code and Anthropic APIs for the AI investigation engine. SOC 2 Type II compliant, PCI DSS Level 1 certified.

3

Processor integrations

Integrated 7 payment processors with full dispute lifecycle support — ingestion, evidence assembly, API submission, and outcome tracking. 27+ total integrations across the commerce stack.

4

Intelligence engine

Built the ML scoring engine with 40+ signal inputs, outcome learning loop, and calibration tracking. 50ms average response time. 99.9% uptime.

5

Production launch

Live at cellix.ai — teams go live in under 48 hours, first dispute recovered within 7 days. Serving e-commerce, SaaS, travel, and financial services verticals.

Integrations

Connected to the tools
payments teams already use.

7 processors, 5 shipping carriers, 5 OMS platforms, 5 CRM tools, and 5 support platforms — one unified view.

Payment Processors

Stripe Adyen Checkout.com Braintree Square PayPal Shopify Payments

Commerce Stack

UPS FedEx DHL Shopify OMS BigCommerce Salesforce HubSpot Zendesk

Security & Compliance

SOC 2 Type II PCI DSS Level 1 TLS 1.3 AES-256 Visa CE3.0 Mastercard
Why I built this

A weekend project that
turned into something real.

The problem I couldn't stop thinking about
After years of watching merchants lose disputes they should win, managing compliance programs manually, and seeing the same operational gaps at every company — I wanted to see if I could build something better. Cellix is early-stage, but every decision in it was informed by almost 8 years of working in this space.
The product thinking, not just the code
The part I'm proudest of isn't the engineering — it's the product decisions: writing the 14-page processor API spec, deciding which integrations to prioritize, designing the ML signal architecture, choosing FIGHT/ACCEPT/PREVENT as the merchant-facing decision framework, and defining the outcome learning loop. Those are the decisions I want to make at scale as a PM.
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