Built a dashboard with GPT Engineer?
We'll make it production-ready.
Dashboards aggregate and display your most important business data — revenue metrics, user analytics, operational KPIs. They need to load fast, display accurate numbers, and restrict access to authorized users. AI tools build visually impressive charts quickly, but the underlying data queries are often slow, insecure, or return incorrect aggregations.
Dashboard challenges in GPT Engineer apps
Building a dashboard with GPT Engineer is a great start — but these challenges need attention before launch.
Query performance at scale
AI-generated dashboards run raw database queries on every page load. With thousands of rows, these queries take seconds or minutes instead of milliseconds. You need materialized views, pre-aggregated data, proper indexing, and caching to keep dashboards responsive.
Data accuracy
A dashboard that shows wrong numbers is worse than no dashboard at all. AI tools generate SQL aggregations that look correct but miss edge cases — timezone handling, duplicate records, null values, and off-by-one errors in date ranges produce silently incorrect metrics.
Access control and data exposure
Dashboards display sensitive business data. AI tools often skip role-based access, meaning anyone with the URL can see revenue figures, customer data, or operational metrics. Different team members need different data visibility levels.
Real-time vs. cached data
Some metrics need to be live (active users, system status), while others can be cached (monthly revenue, historical trends). AI tools either make everything real-time (slow, expensive) or everything static (stale). You need a thoughtful caching strategy.
Chart and visualization bugs
AI-generated charts often have subtle issues — wrong axis scales, misleading truncated Y-axes, color schemes that are indistinguishable for colorblind users, and tooltips that show raw data instead of formatted values. These issues erode trust in the data.
Filter and drill-down interactions
Users need to filter by date range, segment, region, or other dimensions and drill into the details behind any number. AI tools build static charts but not the interactive filtering and drill-down that makes dashboards actually useful for decision-making.
Export and reporting
Stakeholders need to export data to CSV, generate PDF reports, or schedule automated email summaries. AI tools rarely implement data export, and when they do, the output format often breaks in Excel or includes raw technical field names.
What we check in your GPT Engineer dashboard
Common GPT Engineer issues we fix
Beyond dashboard-specific issues, these are GPT Engineer patterns we commonly fix.
No authentication system
GPT Engineer generates functional UIs but typically skips authentication entirely. All routes and data are publicly accessible.
Direct database access from client
Some generated apps query databases directly from the frontend without an API layer, exposing database credentials and structure.
Incomplete feature implementations
Features that look complete in the UI but don't actually work end-to-end. Buttons that don't submit, forms that don't save, and links that go nowhere.
Missing error boundaries
A single component error crashes the entire application. No error boundaries or fallback UIs to gracefully handle failures.
Start with a self-serve audit
Get a professional review of your GPT Engineer dashboard 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 dashboard with GPT Engineer?
GPT Engineer is a great starting point for a dashboard. It handles the initial scaffolding well, but dashboards have specific requirements — query performance at scale and data accuracy — that need professional attention before launch.
What issues does GPT Engineer leave in dashboards?
Common issues include: no authentication system, direct database access from client, incomplete feature implementations. For a dashboard specifically, these issues are compounded by the need for query performance at scale.
How do I make my GPT Engineer dashboard production-ready?
Start with our code audit ($19) to get a clear picture of what needs fixing. For most GPT Engineer-built dashboards, 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 GPT Engineer-built dashboard?
Our code audit is $19 and gives you a complete report of issues. Fixes start at $199 with our Fix & Ship plan. For larger dashboard projects, we provide a custom fixed quote after the audit — no hourly billing.
Get your GPT Engineer dashboard production-ready
Tell us about your project. We'll respond within 24 hours with a clear plan and fixed quote.