Built a internal tool with Databutton?
We'll make it production-ready.
Internal tools don't face the public internet, but they often have access to sensitive business data — customer records, financial data, operational metrics. AI tools build internal dashboards quickly, but the security bar is still high because a compromised internal tool can expose your entire business.
Internal Tool challenges in Databutton apps
Building a internal tool with Databutton is a great start — but these challenges need attention before launch.
Access control
Who can see what? Internal tools need role-based access — finance sees revenue data, support sees customer data, engineering sees system metrics. AI tools build the dashboard but rarely implement granular permissions.
Data sensitivity
Internal tools often connect directly to production databases. A bug that deletes records or a missing auth check that exposes customer PII can have serious legal and business consequences.
Network security
Internal tools should be behind a VPN or protected network, not on the public internet. AI tools deploy to public URLs by default. Proper network configuration prevents external access.
Audit logging
When someone modifies data through an internal tool, you need to know who did what and when. This is essential for debugging, compliance, and accountability.
Data mutations
Internal tools often write to production databases — updating orders, modifying user accounts, issuing refunds. These operations need confirmation dialogs, validation, and audit trails to prevent costly mistakes.
What we check in your Databutton internal tool
Common Databutton issues we fix
Beyond internal tool-specific issues, these are Databutton patterns we commonly fix.
SQL injection in AI-generated query strings
Databutton's generated FastAPI endpoints sometimes build SQL queries using f-strings or string concatenation with user-supplied parameters, bypassing parameterized query protections entirely.
No authentication on data API endpoints
Data pipeline endpoints are frequently generated without authentication middleware, exposing raw database access and sensitive business metrics to anyone who discovers the API URL.
Unhandled data type mismatches crashing pipelines
Generated data processing code assumes clean input schemas. When upstream data contains nulls, type changes, or unexpected formats, pipelines throw unhandled exceptions and fail silently.
Missing pagination on large dataset queries
Data queries load entire tables into memory without limit or offset clauses. With more than a few thousand rows, responses time out and memory usage spikes.
Start with a self-serve audit
Get a professional review of your Databutton internal tool 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 internal tool with Databutton?
Databutton is a great starting point for a internal tool. It handles the initial scaffolding well, but internal tools have specific requirements — access control and data sensitivity — that need professional attention before launch.
What issues does Databutton leave in internal tools?
Common issues include: sql injection in ai-generated query strings, no authentication on data api endpoints, unhandled data type mismatches crashing pipelines. For a internal tool specifically, these issues are compounded by the need for access control.
How do I make my Databutton internal tool production-ready?
Start with our code audit ($19) to get a clear picture of what needs fixing. For most Databutton-built internal tools, 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 Databutton-built internal tool?
Our code audit is $19 and gives you a complete report of issues. Fixes start at $199 with our Fix & Ship plan. For larger internal tool projects, we provide a custom fixed quote after the audit — no hourly billing.
Get your Databutton internal tool production-ready
Tell us about your project. We'll respond within 24 hours with a clear plan and fixed quote.