🚀 Implementation Plan

Email Finder Service
Testing & Monitoring Suite

A high-level plan to ensure reliability, performance, and quality for LeadMagic's email finding and validation API.

10
Implementation Phases
1-2
Weeks Timeline
99.9%
Target Uptime
10
Integrated Tools

🎯 What We're Solving

Current challenges that this plan addresses to improve service reliability and developer experience.

⚠️

Shared Test & Production Data

Staging and production environments share the same databases and caches, risking data corruption and unreliable testing.

🔴

No Automated Deployments

Manual deployments are error-prone and slow. There's no CI/CD pipeline to catch issues before they reach production.

👁️

Limited Visibility

When things go wrong, there's no easy way to see what happened. No error tracking, tracing, or dashboards.

📊

No Performance Insights

No load testing or synthetic monitoring means we discover performance issues only when customers complain.

📝

Inconsistent Code Quality

Without automated code reviews and testing standards, code quality varies and bugs slip through.

📋

No Documented Workflow

Team members follow different processes. No clear rules for branching, reviews, or deployments.

✨ What You'll Get

Benefits after implementing this plan

🛡️

Reliable Deployments

Every code change is tested automatically before it reaches production.

⚡

Instant Error Alerts

Know about issues in minutes, not hours. Get notified before customers do.

📈

Performance Visibility

See response times, error rates, and usage patterns in real-time dashboards.

🔄

Safe Testing

Test confidently in staging without affecting production data.

🤖

AI Code Reviews

Every pull request gets automated feedback to catch issues early.

📚

Clear Standards

Documented workflows and rules everyone can follow consistently.

🛠️ Our Tool Stack

Industry-standard tools chosen for reliability and developer experience

GitHub Actions

GitHub Actions

Automated testing & deployment

Sentry

Sentry

Error tracking & monitoring

Axiom

Axiom

Logs, dashboards & alerts

OpenTelemetry

OpenTelemetry

Distributed tracing

Checkly

Checkly

Synthetic monitoring

K6

K6

Load & performance testing

CodeRabbit

CodeRabbit

AI code reviews

Vitest

Vitest

Unit & E2E testing

Slack

Slack

Team notifications & alerts

Biome

Biome

Linting & code formatting

📋 Prerequisites

What needs to be in place before we start

🔐 Required Access

  • GitHub repository (Admin)
  • Cloudflare account (Workers access)
  • Sentry organization (Admin)
  • Axiom organization (Admin)
  • Checkly account
  • Slack workspace (webhooks)

💻 Local Tools

  • Node.js 20+
  • pnpm 9+
  • Wrangler CLI 4+
  • Git 2.40+
  • K6 (optional, for load testing)

✅ Before Starting

  • All team members have repo access
  • Cloudflare API token created
  • Slack channel for alerts ready
  • Budget approved for services
  • Production deployment is stable

🗺️ Implementation Roadmap

10 phases over 1-2 weeks to build a complete testing and monitoring suite

Phase 0 · Day 1 AM

📝 Git Flow & Best Practices

Establish clear rules for how code moves from development to production. Create documentation everyone can follow.

What
Create staging branch, branch protection rules, CONTRIBUTING.md
Why
Prevent accidental deployments and ensure code review
Where
GitHub repository settings
Outcome
No one can push directly to main or staging
Phase 1 · Day 1 PM (Critical)

🔀 Infrastructure Separation

Create separate databases and caches for staging and production so testing never affects real customer data.

What
Create 8 new KV namespaces, 1 new Hyperdrive config
Why
Staging tests currently pollute production cache
Where
Cloudflare Dashboard, wrangler.toml
Outcome
100% isolated staging and production environments
Phase 2 · Day 2

🚀 CI/CD Pipeline

Set up automated testing and deployment. Every code change gets tested automatically before deployment.

What
3 GitHub Actions workflows (CI, staging deploy, production deploy)
Why
Catch bugs before they reach production
Where
.github/workflows/ directory
Outcome
Automated deploy on every merge, with Slack notifications
Phase 3 · Day 3

🐛 Sentry Error Tracking

Know about errors the moment they happen. See exactly what went wrong and where in the code.

What
Install Sentry SDK, wrap error handlers
Why
Currently no way to know when errors occur
Where
src/index.ts, error handler middleware
Outcome
Instant error alerts with full stack traces
Phase 4 · Day 4

🔍 Distributed Tracing

See the full journey of every request through the system. Identify slow operations and bottlenecks.

What
Enable Cloudflare observability, export to Axiom
Why
Understand where time is spent in each request
Where
wrangler.toml, Cloudflare Dashboard
Outcome
Trace every request from start to finish
Phase 5 · Day 4

📊 Axiom Dashboards

Build real-time dashboards showing system health, performance, and costs. Set up alerts for anomalies.

What
3 dashboards (Operations, Cost, Health), 4 monitors
Why
Proactive monitoring instead of reactive firefighting
Where
Axiom Dashboard
Outcome
Slack alerts for high errors, slow responses
Phase 6 · Day 5

✅ Synthetic Monitoring

Automated checks that continuously test the API from multiple locations. Know if the service is down globally.

What
5 Checkly checks (health, find, validate endpoints)
Why
Detect outages before customers report them
Where
Checkly Dashboard
Outcome
24/7 monitoring from 4 global regions
Phase 7 · Day 5

🏋️ Load Testing

Test how the service performs under heavy traffic. Find the breaking point before customers do.

What
3 K6 scripts (smoke, load, stress tests)
Why
Ensure service handles expected traffic
Where
tests/load/ directory
Outcome
Smoke tests run automatically on every deploy
Phase 8 · Day 6

🐰 AI Code Review

Every pull request gets reviewed by an AI that catches security issues, bugs, and suggests improvements.

What
Install CodeRabbit GitHub App, configure review rules
Why
Extra pair of eyes on every code change
Where
.coderabbit.yaml configuration
Outcome
Automated feedback on security, performance, style
Phase 9 · Day 6

🧪 Enhanced Test Suite

Add coverage requirements and test factories. Ensure critical code paths are always tested.

What
70% coverage threshold, test data factories
Why
Maintain test quality as codebase grows
Where
vitest.config.ts, tests/factories/
Outcome
PRs fail if coverage drops below threshold

🎯 Implementation Flow

The critical path that must be followed in order

0 Git Flow
→
1 Infra Split
→
2 CI/CD
→
3-9 Monitoring & Testing

⚠️ Phases 0-2 must be completed in order. Phases 3-9 can be parallelized.

🤖 AI-Accelerated Timeline

This estimate assumes using Claude AI with tools like Cursor for code generation, configuration, and documentation.

Manual coding: ~5 weeks  →  AI-assisted: ~1-2 weeks   ✨ 3-4x faster

🔮 What's Coming Next

Planned improvements after the initial implementation is stable

🔐

Doppler Secrets

Centralized secrets management with version history and audit logs.

When team grows 5+
🗄️

Separate Staging DB

Fully isolated staging database for safer testing and schema changes.

High Priority
🔄

Canary Deployments

Gradual rollouts to catch issues before they affect all users.

When incidents increase
🚩

Feature Flags

Toggle features on/off without deployments. Perfect for A/B testing.

When needed
📋

Public Status Page

Show customers real-time service status and incident history.

SLA requirements
📖

OpenAPI Documentation

Auto-generated API docs with interactive explorer.

Developer experience