Sourcegraph Cody + Dashboard

Built a dashboard with Sourcegraph Cody?
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.

TypeScriptGoPythonJavaReact

Dashboard challenges in Sourcegraph Cody apps

Building a dashboard with Sourcegraph Cody 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 Sourcegraph Cody dashboard

Query performance — response times under 500ms for all dashboard views
Data accuracy — aggregation logic, timezone handling, null values
Access control — role-based permissions enforced on API and database level
Caching strategy — appropriate refresh intervals for each metric type
Visualization correctness — accurate axes, accessible color schemes
Filter functionality — date ranges, segments, and drill-down interactions
Export capability — CSV, PDF, and scheduled reports
Mobile responsiveness — usable dashboard layout on tablets and phones
Error states — clear messages when data is unavailable or queries fail
Database load — queries don't impact production application performance

Common Sourcegraph Cody issues we fix

Beyond dashboard-specific issues, these are Sourcegraph Cody patterns we commonly fix.

highCode Quality

Suggestions based on deprecated or low-quality code patterns found in the existing codebase

Cody's suggestions are grounded in your actual codebase, which means if the codebase contains outdated patterns, deprecated library usage, or known-bad code, Cody will suggest those same patterns in new code — amplifying technical debt.

highSecurity

Cross-repo context can leak patterns from one team's code into another team's service

In large organizations where Cody indexes multiple repositories, suggestions can carry patterns from one team's codebase into another, introducing unfamiliar dependencies, different error handling conventions, or architectural approaches that do not belong in the target service.

mediumSecurity

Security vulnerabilities in existing code recommended as reference implementations

If the indexed codebase contains known security issues that have not yet been patched — unparameterized queries, missing auth checks, insecure deserialization — Cody may suggest these patterns as examples when generating similar code.

mediumPerformance

Performance anti-patterns replicated from existing high-traffic code paths

When Cody finds existing code as a reference for a new feature, it may replicate performance issues — N+1 queries, missing indexes, blocking synchronous calls — that are accepted in the existing code but will be problematic at the scale of the new feature.

Start with a self-serve audit

Get a professional review of your Sourcegraph Cody dashboard at a fixed price.

External Security Scan

Black-box review of your public-facing app. No code access needed.

$19
  • OWASP Top 10 vulnerability check
  • SSL/TLS configuration analysis
  • Security header assessment
  • Expert review within 24h
Get Started

Code Audit

In-depth review of your source code for security, quality, and best practices.

$19
  • Security vulnerability analysis
  • Code quality review
  • Dependency audit
  • Architecture review
  • Expert + AI code analysis
Get Started
Best Value

Complete Bundle

Both scans in one package with cross-referenced findings.

$29$38
  • Everything in both products
  • Cross-referenced findings
  • Unified action plan
Get Started

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 Sourcegraph Cody?

Sourcegraph Cody 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 Sourcegraph Cody leave in dashboards?

Common issues include: suggestions based on deprecated or low-quality code patterns found in the existing codebase, cross-repo context can leak patterns from one team's code into another team's service, security vulnerabilities in existing code recommended as reference implementations. For a dashboard specifically, these issues are compounded by the need for query performance at scale.

How do I make my Sourcegraph Cody dashboard production-ready?

Start with our code audit ($19) to get a clear picture of what needs fixing. For most Sourcegraph Cody-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 Sourcegraph Cody-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 Sourcegraph Cody dashboard production-ready

Tell us about your project. We'll respond within 24 hours with a clear plan and fixed quote.

Tell Us About Your App