Infrastructure & Operations Solutions

Production-ready patterns for DevOps, API integration, and IT operations. Built for engineers who need reliable automation, not just demos.

Built for real infrastructure

These aren't toy examples. They're patterns we've seen work in production environments with real API limits, failure modes, and operational constraints. The AI enhancement is useful, but the orchestration, retry logic, and observability are what make them production-ready.

DevOps & CI/CD Integration & APIs IT Operations

DevOps & CI/CD

Deployment Pipeline Orchestration

The Real Problem

Your deployment pipeline is a fragile chain of GitHub Actions, shell scripts, and prayers. Build takes 15 minutes, tests fail intermittently, and "deploy to production" means someone runs a script and watches Slack for 30 minutes. When it fails at 2am, the on-call engineer spends an hour figuring out what broke, manually rolls back, and leaves a TODO to fix it later.

What FlowMason Enables

• Orchestrate CI/CD APIs (GitHub, Jenkins, GitLab) with proper retry and timeout handling
• Conditional promotion gates (staging tests must pass before prod)
• Automatic rollback on health check failure
• AI analysis of deployment failures to suggest fixes
• Full audit trail of who deployed what when

Realistic Expectations

Metric	Before	After	Notes
Deploy confidence	Manual checks	Automated gates	Health verified before traffic
Rollback time	15-30 min	2-5 min	Automatic on failure
MTTR	45-60 min	10-20 min	With AI root cause
Deploy frequency	Weekly	Daily+	When you trust the pipeline

Implementation Reality

Timeline 2-4 weeks per service (integration, testing, rollout)

Integrations GitHub Actions, Jenkins, GitLab CI, ArgoCD, Kubernetes

Prerequisites Health endpoints, artifact registry, secrets management

Team Platform engineer + service owners

Pipeline Pattern

trigger_build (GitHub API)
    │
    ├── poll_status (retry: 30x, 10s)
    │
    ├── deploy_staging (K8s API)
    │       │
    │       └── health_check (retry: 12x)
    │
    ├── run_integration_tests
    │       │
    │       ├── [pass] deploy_production
    │       │           │
    │       │           ├── health_check
    │       │           │
    │       │           └── notify_success (Slack)
    │       │
    │       └── [fail] ai_analyze_failure
    │                   │
    │                   └── notify_failure + rollback

Why not just use GitHub Actions directly?

You can. But GitHub Actions can't call your Kubernetes API, roll back based on custom health checks, or use AI to analyze why a deploy failed. FlowMason orchestrates across systems—it calls GitHub, then Kubernetes, then Datadog, then Slack—with proper error handling between each.

Integration & APIs

Multi-Service API Orchestration

The Real Problem

Your customer data lives in Salesforce, Stripe, Intercom, and three internal services. Building a "customer 360" view means writing 500 lines of Python to call 6 APIs, handle rate limits, deal with timeouts, merge the data, and pray nothing changed since last week. When Stripe's API returns a 429, your whole script fails and you start over.

What FlowMason Enables

• Parallel API calls with independent retry logic per source
• Schema validation on inputs and outputs
• Data transformation with JMESPath/Jinja2 templates
• AI enrichment (summarize, categorize, extract insights)
• Composable pipelines you can version and reuse

Realistic Expectations

Metric	Before	After	Notes
Integration dev time	2-3 days	2-4 hours	Visual pipeline builder
Error handling	Ad-hoc	Standardized	Retry, timeout, fallback
Data freshness	Manual runs	Scheduled/webhook	Cron or event-triggered
Maintenance	Tribal knowledge	Visual + versioned	Anyone can understand

Implementation Reality

Timeline 1-2 weeks per integration (auth, mapping, testing)

Integrations Any REST API, OAuth/JWT auth, webhooks

Prerequisites API credentials, rate limit understanding, schema docs

Output JSON/webhook to your systems, or expose as API

Pipeline Pattern

validate_input (schema)
    │
    ├── fetch_salesforce ──┐
    │   (retry: 3x)        │
    │                      │
    ├── fetch_stripe ──────┼── merge_data
    │   (retry: 3x)        │       │
    │                      │       ├── transform (JMESPath)
    └── fetch_intercom ────┘       │
        (retry: 3x)                ├── ai_enrich
                                   │   (summarize, categorize)
                                   │
                                   └── output_result

Common patterns

• ETL: Extract from sources → Transform → Load to warehouse
• Webhooks: Receive event → Route by type → Process → Respond
• Sync: Detect changes → Map fields → Update targets → Log

IT Operations

Incident Response Automation

The Real Problem

PagerDuty wakes you at 3am. You SSH into production, grep through logs, check Datadog, look at recent deploys, try restarting the service, realize it's a memory leak, scale up the pods, and go back to sleep. Next week, same alert, same dance. The runbook exists but nobody reads it because it's faster to just do it manually.

What FlowMason Enables

• Receive alerts via webhook, automatically fetch context
• AI-powered root cause analysis from logs + metrics + deploy history
• Auto-remediation for known issues (restart, scale, rollback)
• Escalation to humans when auto-fix fails or uncertainty is high
• Status page updates and team notifications

Realistic Expectations

Metric	Before	After	Notes
Time to triage	15-30 min	1-2 min	AI analyzes immediately
Auto-resolved %	0%	40-60%	For known issue patterns
On-call pages	All alerts	Only escalations	Sleep more nights
MTTR	30-60 min	5-15 min	When human needed

Implementation Reality

Timeline 4-8 weeks (alert taxonomy, runbook encoding, testing)

Integrations PagerDuty/OpsGenie, Datadog/Prometheus, Kubernetes, Slack

Prerequisites Structured logging, metrics, documented runbooks

Risk Start with safe actions (restart), add destructive later

Pipeline Pattern

receive_alert (webhook)
    │
    ├── fetch_logs (ELK/Loki)
    │
    ├── fetch_metrics (Prometheus)
    │
    └── fetch_recent_deploys
            │
            └── ai_analyze_root_cause
                    │
                    ├── [known issue] auto_remediate
                    │       │
                    │       ├── [success] notify + close
                    │       │
                    │       └── [fail] escalate_human
                    │
                    └── [unknown] escalate_human
                            │
                            └── update_status_page

Critical Limitation

Auto-remediation is powerful but dangerous. Start with safe actions (restart service, scale up) and add destructive actions (rollback, drain node) only after extensive testing. Always have a human in the loop for critical systems.