Full-System Context
Every review pulls in relevant code and past review history from your indexed codebase — the AI sees your full system, not just the diff.
DeployIQ
Deployment risk intelligence
DeployIQ generates full deployment risk reports on every pull request — stored on your account and used to learn from your history. Every review builds smarter context, surfacing blast radius, missing checks, and safer rollout guidance automatically.
9
touchpoints mapped
15
code sections analyzed
High
7 / 10
Capabilities
DeployIQ goes beyond line-by-line commentary. It understands your full codebase and historical context to analyze the true blast radius of every change.
Every review pulls in relevant code and past review history from your indexed codebase — the AI sees your full system, not just the diff.
Ties risk calls to test coverage, past incidents, deploy history, and known weak spots. Scores 1–10 with clear risk levels.
Maps every change against affected services, shared contracts, downstream consumers, and data stores automatically.
Surfaces missing migration rehearsals, untested rollback paths, partial integration coverage, and skipped preflight checks.
Stores every PR review on your account with full risk reports you can revisit anytime — track patterns across deployments.
GitHub Actions trigger analysis on every pull request — risk reports post as PR comments within seconds, no manual steps.
Architecture
A modern stack from frontend to infrastructure — containerized, automated, and running on AWS Fargate.
Server-rendered UI for risk reports and dashboards
Tailwind CSS v4 · shadcn/ui · Radix UI
Multi-step agent pipeline for contextual risk analysis
LangChain/LangGraph · OpenAI Embeddings · Vector Database
API routes, database access, and structured storage
Amazon RDS · Connection Pooling · JSONB Storage
Automated build, test, and deploy pipeline on AWS
CodeBuild · ECR · ECS Fargate
ECS on Fargate
Serverless containers
Amazon ECR
Docker image registry
Amazon RDS
PostgreSQL database
CodePipeline
Automated CI/CD
Region: ca-central-1 · Multi-stage Docker builds · Standalone Next.js output · Automated deploys on merge to main
How It Works
Open a pull request and DeployIQ handles the rest — no config, no manual triggers.
Step 1
GitHub Actions detects a new pull request, extracts the diff, changed files, and PR metadata.
Step 2
AI searches your indexed codebase for relevant code sections and past reviews related to the change.
Step 3
Multi-step AI agent parses the diff, injects system context, and generates a structured risk report.
Step 4
Structured risk report posts directly as a PR comment, and with further context in your dashboard.
Report Preview
Every PR gets a structured risk report grounded in your codebase context, scored by evidence, and delivered as actionable guidance.
Deployment Risk Report
Backend change touching retry orchestration, webhook delivery, and ledger reconciliation across 4 services.
Risk
7 / 10
High
Codebase Match
0.91 relevancesrc/services/retry-queue.ts:45-89
Existing retry logic uses exponential backoff with no circuit breaker — the refactor changes this path.
Past Review Match
PR #47Prior retry queue regression caused duplicate webhook dispatch under load — flagged as high risk.
Tests cover the happy path only
Integration coverage exists for retry creation, but not delayed reprocessing or rollback.
No circuit breaker tests
New circuit breaker logic has zero test coverage in the current suite.
Step 1
Flag the new retry path
Ship behind a feature flag for staged rollout
Step 2
Canary background workers
Roll out worker path first, monitor queue depth
Step 3
Monitor and promote
Watch webhook retries for 24h before full rollout
Why DeployIQ
Every PR gets a structured risk report — grounded in your codebase context, scored with evidence, and delivered with rollout guidance.