Stop babysitting AI output

AI Engineers
Who Ship

Autonomous agents that clear your backlog—following YOUR standards, not generic patterns.

Zero IP exfiltrationMerge-ready PRs10-15x velocity
OutcomeOps Sprint Board
BACKLOG
Setup CI/CD Pipeline
Integrate with Payment Processor
Add error handling middleware
IN PROGRESS
Integrate Auth into the Frontend
Build notification service
Create API documentation
IN REVIEW
Build an authentication layer
Setup database migrations
Implement rate limiting
0STORIES COMPLETED TODAY

Engineers focus on Outcomes

AI Engineers focus on translating those outcomes to code

From Real Enterprise Deployments

Production-Tested Results

0hrs~0min
Task Completion
.$0
Cost per Feature
100-0x
ROI Multiplier
0%
First-Time Approval

"The model matters less than the context you give it."

Your AI Writes Code That Breaks Production

Zero context about YOUR architecture, patterns, or compliance requirements.

3 hours debugging — Copilot missed your custom auth layer
Failed audit — AI code missing HIPAA logging
Production incident — deprecated API patterns
$200K+ TCO — rewriting AI code to match standards

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

MCP Server Support

Extend your platform with any MCP-compatible tool

How Context Engineering Works

Three steps to transform generic AI into your organization's expert system

01

Ingest Your Knowledge

ADRs, code-maps, and compliance docs become a searchable knowledge base.

02

Generate Compliant Code

AI queries your standards before generating—YOUR patterns, not generic examples.

03

Validate & Learn

Self-corrects against ADRs. Each failure becomes a new standard. The loop compounds.

Integrates With Your Stack

Jira
GitHub
GitLab
AWS
Bedrock
Azure
Python
Java
TypeScript
MCP
$2.24
Average cost per feature at Fortune 500 scale

Built by a former AWS ProServe Principal who led $18M enterprise transformations. Meet the team →

Ready to see your repos light up? Free 2-week PoC available.

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