Organizational Intelligence Layer

The Intelligence Layer

Underneath every AI tool you ship. PR review, chat, and code generation — all grounded in the same queryable substrate of your ADRs and standards.

12 source integrations6 languages incl. Go6 grounded PR checksCross-repo resolution
OutcomeOps Intelligence Layer
FEEDS IN
Jira, GitHub, GitLab
Confluence, Salesforce
ServiceNow, SQL, M365
ONE SUBSTRATE
Your ADRs & standards
Ground-truth code wins
Audit log, your AWS acct
GROUNDS
PR review (6 checks)
Chat & code generation
Your own coding agent
1,842RETRIEVALS GROUNDED TODAY

Swap the model. Swap the agent.

The intelligence layer stays yours

From Real Enterprise Deployments

Production-Tested Results

16hrs~20min
Task Completion
$2.24
Cost per Feature
100-200x
ROI Multiplier
90%
First-Time Approval

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

Why OutcomeOps Exists

"I've watched this pattern repeat for 14 years across five platform shifts. The teams that win encode organizational intelligence into the tooling layer."

— Brian Carpio, Founder

Enterprise Platform

Production-grade features for regulated industries

Autonomous Pipeline

Jira/GitHub Issues → Code → PR → Test → Self-correction

Air-Gapped Deployment

Fully private and single-tenant in your own VPC. No public ingress or egress, zero data exfiltration, all processing on your infrastructure

GovCloud & FedRAMP Ready

Deploys to AWS GovCloud with Bedrock for federal workloads

PII Redaction by Default

Sensitive data redacted before AI invocation. Per-workspace toggle, audit-logged.

ADR Traceability

Every line of code traceable to your architectural decisions

Automatic PR Analysis

Catches architectural drift before merge — on GitHub, GitLab, and Azure DevOps

Cross-Repo Impact Analysis

Resolves every caller, importer, and implementer of a changed symbol — across repos, not just the one in the diff

License Compliance

Detects GPL/copyleft code before legal issues

Multi-Tenant Knowledge Base

Isolated ADRs per team with shared libraries

Model Flexibility

Claude and OpenAI (GPT-5.5, Codex) via AWS Bedrock — swap tiers via config, no code change

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, Go, C#, ABAP, 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

Review and Generate Against Your Standards

Every PR is judged against YOUR retrieved ADRs. Code generation runs on the same knowledge base and is entirely optional.

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
C#
Go
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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