Stop babysitting AI output
AI Engineers Who Ship
Autonomous agents that clear your backlog—following YOUR standards, not generic patterns.
From Real Enterprise Deployments
Production-Tested Results
Your AI Writes Code That Breaks Production
Zero context about YOUR architecture, patterns, or compliance requirements.
Your team spends 60-80% of their time adapting AI suggestions instead of shipping.
Enterprise Platform
Production-grade features for regulated industries
Autonomous Pipeline
Jira/GitHub Issues → Code → PR → Test → Self-correction
Air-Gapped Deployment
Zero data exfiltration, all processing on your infrastructure
GovCloud & FedRAMP Ready
Deploys to AWS GovCloud with Bedrock for federal workloads
ADR Traceability
Every line of code traceable to your architectural decisions
Automatic PR Analysis
Catches architectural drift before merge
License Compliance
Detects GPL/copyleft code before legal issues
Multi-Tenant Knowledge Base
Isolated ADRs per team with shared libraries
Model Flexibility
Claude, Llama, Mistral, Titan via AWS Bedrock
SOC2/HIPAA Compliance
Audit trails and compliance features
Self-Correction Loop
AI validates and fixes its own output automatically
Language & Framework Support
Python, Java, TypeScript, Terraform, Serverless Framework
Custom Configuration
.outcomeops.yaml adapts to non-standard structures
How Context Engineering Works
Three steps to transform generic AI into your organization's expert system
Ingest Your Knowledge
ADRs, code-maps, and compliance docs become a searchable knowledge base.
Generate Compliant Code
AI queries your standards before generating—YOUR patterns, not generic examples.
Validate & Learn
Self-corrects against ADRs. Each failure becomes a new standard. The loop compounds.
Integrates With Your Stack
Built by Someone Who's Done This Before
Brian Carpio - Former AWS ProServe Principal who delivered $18M Fortune 10 pharmaceutical transformation (featured at re:Invent 2023)
Proven at Scale
Currently powering 90+ serverless functions in production - built with OutcomeOps in 120 days
20 Years Building the Infrastructure Patterns That Became Industry Standard
- Largest HCLS engagement in AWS ProServe history - $18M cloud transformation
- CIO keynoted at AWS re:Invent 2023 on generative AI strategy
- Created the cloud operating model now adopted as the standard for AWS HCLS
- Generated $20M+ in downstream pipeline across other pharmaceutical companies
MongoDB at Production Scale
Before it was mainstream
Platform Engineering
Before it had a name - "Nibiru" platform
Golden Pipelines
Before Spotify popularized the term - "Utopia" platform
The same playbook that worked for infrastructure automation now applied to AI-assisted development.
Make the right path the easy path.