Built a fintech app with Pythagora?
We'll make it production-ready.
Financial apps handle the most sensitive data there is — bank accounts, transactions, and personal financial information. A single security flaw can mean stolen funds, regulatory penalties, or permanent loss of user trust. AI tools can prototype a budgeting dashboard or invoicing system quickly, but the compliance, encryption, and audit requirements for financial software are far beyond what any AI tool generates out of the box.
Fintech App challenges in Pythagora apps
Building a fintech app with Pythagora is a great start — but these challenges need attention before launch.
Data encryption and storage
Financial data must be encrypted at rest and in transit. Account numbers, transaction histories, and personally identifiable financial information need field-level encryption, not just HTTPS. AI tools store financial data in plain text in the database, which is a compliance violation waiting to happen.
Transaction integrity
Financial transactions must be atomic — money debited from one account must be credited to another, with no in-between state where it disappears. AI-generated code doesn't use database transactions, meaning a server crash mid-operation can leave accounts in an inconsistent state.
Audit trail and compliance
Every financial action needs an immutable log — who initiated it, when, what changed, and what the balances were before and after. Regulators require this. AI tools don't generate audit logging, and retrofitting it into an existing codebase is tedious but essential.
Third-party financial API integration
Connecting to Plaid, Stripe, or banking APIs requires handling OAuth flows, webhook verification, idempotency keys, and retry logic for failed calls. AI tools generate the initial API call but miss the error handling and reliability patterns these services require.
Authentication and fraud prevention
Financial apps need multi-factor authentication, session timeout policies, device fingerprinting, and suspicious activity detection. Basic email/password auth from AI tools is nowhere near sufficient for an app that touches people's money.
Number precision and currency handling
JavaScript floating-point math causes rounding errors with money — $0.1 + $0.2 !== $0.3. AI tools use standard floats for currency calculations, which leads to penny discrepancies that compound over time and break reconciliation.
Regulatory awareness
Depending on what your app does, you may need to comply with PCI DSS, SOC 2, KYC/AML requirements, or state money transmitter regulations. AI tools have no awareness of these requirements, and non-compliance can result in fines or forced shutdowns.
What we check in your Pythagora fintech app
Common Pythagora issues we fix
Beyond fintech app-specific issues, these are Pythagora patterns we commonly fix.
Weak input validation
Pythagora validates inputs at the UI level but often skips server-side validation, allowing malicious requests to bypass frontend checks.
Insecure session management
Sessions and tokens are sometimes stored insecurely or don't expire properly, allowing unauthorized access to persist.
Step-by-step accumulation of bugs
Each development step is reviewed individually, but the interaction between steps can introduce bugs that aren't visible in isolation.
Redundant code from iterations
Pythagora's iterative approach sometimes leaves behind old code paths, unused functions, and deprecated approaches from earlier steps.
Start with a self-serve audit
Get a professional review of your Pythagora fintech app at a fixed price.
External Security Scan
Black-box review of your public-facing app. No code access needed.
- OWASP Top 10 vulnerability check
- SSL/TLS configuration analysis
- Security header assessment
- Expert review within 24h
Code Audit
In-depth review of your source code for security, quality, and best practices.
- Security vulnerability analysis
- Code quality review
- Dependency audit
- Architecture review
- Expert + AI code analysis
Complete Bundle
Both scans in one package with cross-referenced findings.
- Everything in both products
- Cross-referenced findings
- Unified action plan
100% credited toward any paid service. Start with an audit, then let us fix what we find.
Frequently asked questions
Can I build a fintech app with Pythagora?
Pythagora is a great starting point for a fintech app. It handles the initial scaffolding well, but fintech apps have specific requirements — data encryption and storage and transaction integrity — that need professional attention before launch.
What issues does Pythagora leave in fintech apps?
Common issues include: weak input validation, insecure session management, step-by-step accumulation of bugs. For a fintech app specifically, these issues are compounded by the need for data encryption and storage.
How do I make my Pythagora fintech app production-ready?
Start with our code audit ($19) to get a clear picture of what needs fixing. For most Pythagora-built fintech apps, the critical path is: security review, then fixing core flow reliability, then deployment. We provide a fixed quote after the audit.
How much does it cost to fix a Pythagora-built fintech app?
Our code audit is $19 and gives you a complete report of issues. Fixes start at $199 with our Fix & Ship plan. For larger fintech app projects, we provide a custom fixed quote after the audit — no hourly billing.
Get your Pythagora fintech app production-ready
Tell us about your project. We'll respond within 24 hours with a clear plan and fixed quote.