OutcomeOps
Context Engineering
AI Grounded in Your Standards: ADRs, Patterns, Decisions
Every Failure Becomes a Standard. Every Standard Improves the Next Generation.
Battle-tested at Fortune 10 scale. Zero IP exfiltration. License-compliant code. 10-15x velocity.
Two Tiers: Open Framework + Enterprise Platform
Open Framework (GitHub)
The Context Engineering methodology is open source and freely available:
- ADR templates and best practices
- Self-documenting architecture patterns
- Knowledge base design principles
- Getting started guides
Enterprise Platform (Licensed)
The autonomous agent platform with production-grade features:
- Autonomous pipeline: Jira/GitHub Issues → Code → PR → Test → Self-correction loop
- Surgical cleanup strategy: Only modifies relevant code, preserves existing architecture
- IP protection: Air-gapped deployment, zero data exfiltration, all processing on-prem
- Multi-tenant knowledge base: Isolated ADRs, patterns per team/project with shared libraries
- Multi-LLM integration (Azure OpenAI, Bedrock, on-prem)
- SOC2/HIPAA compliance features and audit trails
Production-Tested Results
Average fully-loaded cost for a compliant backend feature (2024-2025 pilots)
Your AI Writes Code That Breaks Production
Because it has zero context about YOUR architecture, YOUR patterns, YOUR compliance requirements.
The Real Cost of Generic AI
- 3 hours debugging because Copilot didn't know about your custom auth layer
- Failed compliance audit from AI-generated code missing HIPAA logging requirements
- Production incident when ChatGPT used deprecated API patterns instead of your current standards
- $200K+ TCO from engineers rewriting AI code to match organizational standards
The hidden cost: Your team spends 60-80% of their time adapting AI suggestions instead of shipping features.
That's where OutcomeOps steps in—engineering context so AI gets your world from day one.
How Context Engineering Works
Four steps to transform generic AI into your organization's expert system
Ingest Organizational Knowledge
ADRs, code-maps, compliance documents, and architectural standards are ingested into a vector-based knowledge base.
AI Generates Compliant Code
AI queries the knowledge base before generation, ensuring code follows YOUR patterns, not generic examples.
Automated Validation
Generated code is validated against ADRs, tested automatically, and self-corrects based on organizational standards.
Continuous Learning
Each failure becomes an ADR, auto-updating the system for smarter generations next time. The feedback loop compounds: better ADRs → better code → better ADRs.
Enterprise platform details available in briefings only.
See autonomous agents, multi-tenant architecture, and air-gapped deployment in action
Autonomous Support Resolution
Transform your L1/L2 support teams into engineering force multipliers
From Support Ticket to Pull Request Automatically
L1/L2 engineers file bugs or feature requests. The system analyzes your code-maps, generates compliant fixes, and creates PRs for engineering/product teams to approve.
L1/L2 Teams Become Engineering Force Multipliers
Support engineers identify issues, OutcomeOps generates the fix following your patterns, senior engineers review and approve. No more translating support tickets into engineering work.
From Customer Complaint to Deployed Fix in Hours, Not Weeks
Eliminate the backlog bottleneck. Support-driven fixes move at the speed of approval, not the speed of engineering availability.
Enabled by OutcomeOps' code-map architecture—the same context that powers autonomous development can empower your support teams to drive engineering outcomes.
Request This FeatureBuilt 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.