For Developers, Architects, DevOps & SRE Teams

Your Camunda 7 Platform is a Black Box.
Let`s Make It Crystal Clear

Champa Intelligence is the observability platform you'd build if you had 12-18 months and a dedicated team. We did the heavy lifting so you can focus on building reliable, performant workflows.

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Optimized SQL Queries
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Faster RCA
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Cluster Observability
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Validation Rules

The Real Problems Nobody Talks About

If you've run Camunda in production, these will sound familiar

Incidents? Let Me Check 10 Screens in 5 Apps

Cockpit shows the incident. Admin shows the job log. Your APM shows the JVM crash. Your log aggregator has the stack trace. Your business owner asks "why?" You team spends 2 hours correlating data instead of fixing the issue.

REST API Doesn't Scale to Analytics

Try fetching performance data for 10 process versions over 90 days via REST. Now try it for 50 processes. Your monitoring dashboard just timed out. You need direct database access with optimized queries—but who has time to write 80+ hand-tuned SQLs?

Version Migrations Are Guesswork

"Did we change the service task timeout?" "Was this gateway parallel before?" Detailed enough visual diff tools don't exist. XML diffs are unreadable. You're deploying to production with fingers crossed, hoping nothing breaks.

Quality Gates? What Quality Gates?

Your junior developer just deployed a BPMN with no documentation, hardcoded passwords in scripts, and missing error boundaries. You found out in production. There's no automated validation preventing this.

Architecture That Makes Sense

Built by engineers who know the pain points

Performant Backend

  • Flask with psycopg2 connection pooling
  • 80+ hand-optimized PostgreSQL queries
  • JWT authentication with granular RBAC
  • Native Prometheus metrics endpoints

Lazy-Loading SPA

  • Alpine.js + Intersection Observer API
  • Tailwind CSS with sexy dark mode support
  • bpmn-js + dmn-js for visualization
  • Chart.js + AG Grid for analytics

Observability-First

  • Structured logging (conservative + JSON)
  • Separate logs: access, security, DB, AI
  • Comprehensive audit trail
  • Datadog, Grafana, ELK stacks ready

See It In Action

Parallel multi-node health data collection

with concurrent.futures.ThreadPoolExecutor(max_workers=10) as executor:
    # Parallel JMX metrics collection
    jmx_future = executor.submit(collect_jmx_metrics)

    # Parallel database queries
    db_futures = {
        'process_analytics': executor.submit(collect_process_analytics),
        'system_health': executor.submit(collect_system_health),
        'sla_metrics': executor.submit(collect_sla_metrics)
    }

    # Wait for JMX, then query each node
    jmx_data = jmx_future.result()
    node_futures = [
        executor.submit(fetch_node_data, name, url, jmx_data)
        for name, url in CAMUNDA_NODES.items()
    ]

    # Aggregate results
    cluster_metrics = [f.result() for f in node_futures]

Full cluster health data in <2 seconds for 5-node clusters

Features That Solve Real Problems

Not buzzwords. Just tools that work.

Unique to Champa

BPMN Diff Tool

Compare any two BPMN versions visually. See exactly what changed—structure, attributes, layout—before deploying. Generate diff reports for PRs and audits.

  • Visual highlighting on BPMN diagram (Added/Removed/Changed)
  • Side-by-side XML diff for changed elements
  • HTML report generation for documentation
// Use Case

"Before pushing v23 to production, I need to verify the timeout change in the API call didn't affect anything else."

+ Added: serviceTask_api_v2
~ Changed: serviceTask_api_v1
Attribute: camunda:asyncBefore false → true
// Validation Results
Security Risk: Hardcoded Credentials
Script task 'CallExternalAPI' contains potential hardcoded password
Best Practice: Missing Documentation
12 activities have no documentation
Shift-Left Quality

Automated Model Validator

50+ rules that catch issues before deployment: missing names, hardcoded secrets, inefficient patterns, syntax errors in scripts, and Camunda-specific misconfigurations.

  • JavaScript/Groovy syntax validation
  • Security scanning (credentials, SQL injection patterns)
  • Custom ruleset configuration
AI-Powered

Intelligent Root Cause Analysis

Ask complex questions in natural language. Gemini AI analyzes 40+ database queries across incidents, performance, variables, and patterns to deliver actionable insights in minutes.

  • Multi-process analysis (up to 5 processes)
  • Focus modes: incidents, performance, long-running, variables, patterns
  • Contextual recommendations based on actual data trends
// AI Analysis Output

Root Cause Identified:

The spike in incidents correlates with deployment v47. Analysis shows the new 'ValidatePayment' service task has a 23% failure rate due to timeout configuration (5s → should be 15s based on P95 latency of 12.3s).

Recommendation:

1. Increase timeout to 18s (P99 + buffer)
2. Add async continuation before task
3. Implement circuit breaker pattern

Built for Enterprise Reality

Production-ready, not prototype-ready

Enterprise Security

  • JWT Authentication: Salted password hashing (pbkdf2_hmac), brute-force protection, account lockout
  • Granular RBAC: Feature-based permissions , system and custom roles, smart API access control
  • Audit Trail: Comprehensive logging of all security events, user actions, admin operations
  • API Token Management: Configurable TTL, automatic expiration checks, regeneration

Observability Stack

  • Prometheus Native: Built-in /health/full/metrics and /portfolio/overview/metrics endpoints with 50+ metrics
  • Log Aggregation: Structured JSON logs ready for ELK/Splunk, separated by concern
  • JVM Metrics: Heap usage, GC statistics, thread counts via JMX/Micrometer(e.g. Tomcat or Quarkus based)
  • Database Health: Connection pool monitoring, slow query detection, tables size tracking

Performance & Scale

  • Optimized Queries: Hand-tuned SQL with proper indexing, aggregation pushdown, minimal data transfer
  • Parallel Execution: ThreadPoolExecutor for multi-node queries, concurrent DB access
  • Lazy Loading: Intersection Observer API ensures data fetched only when needed
  • Connection Pooling: Efficient PostgreSQL connection management under load

Deployment Flexibility

  • WSGI-Compliant: Deploy with Gunicorn, uWSGI, or any WSGI server
  • Docker-Ready: Containerized deployment with environment-based configuration
  • Configuration: python-dotenv for flexible environment variables, no hardcoded secrets
  • Reverse Proxy: ProxyFix middleware for seamless nginx/Apache integration

Modern Robust Extendable Tech Stack

Familiar technologies, no vendor lock-in

Python
Flask, psycopg2
PostgreSQL
Direct DB integration
Alpine.js
Lightweight reactivity
Tailwind CSS
Utility-first styling
bpmn.io
Diagram rendering
Gemini AI
Google GenAI
Prometheus
Native metrics
Webpack
Asset bundling

Ready to Stop Firefighting?

Join forward-thinking engineering teams who've already made the switch.

Schedule Technical Demo
Free technical consultation
Architecture review included
POC support available

Element XML Difference

Diagram A:

Diagram B: