What Is ScanDrix And How Does It Work?

ScanDrix is an autonomous AI-powered code review and AST security scanning platform built for modern engineering teams. It integrates into your Git providers (GitHub, GitLab, Bitbucket, Azure Repos) to automatically review pull requests, detect vulnerabilities, suggest code fixes, and enforce custom Drixy rules.

How Does ScanDrix Differ From Traditional Linters Like SonarQube?

Traditional linters analyze files in isolation using rigid regex or pattern rules, resulting in high false-positive rates and zero understanding of code intent. ScanDrix performs deep Abstract Syntax Tree (AST) analysis combined with multi-model AI reasoning, understanding whole-codebase context, taint flows, and architectural boundaries.

What Are Drixy Rules And How Do I Configure Them?

Drixy rules are customizable code review and security policies. You can define rules globally across your organization, per repository, or choose from a library of community best practices. You can also run 'Dry Run' previews to test rule modifications against real historical PRs before enforcing them.

Which Git Providers And CI/CD Tools Does ScanDrix Support?

ScanDrix supports GitHub, GitLab, Bitbucket, Azure Repos, and Forgejo out of the box. It also provides a CLI and team API keys for local developer workflows, pre-commit hooks, and custom CI/CD runners.

Does ScanDrix Train On Our Private Source Code?

No. ScanDrix never uses customer source code to train AI models. All code diffs are processed in ephemeral, isolated environments with strict zero-data-retention guarantees. For enterprise deployments, ScanDrix supports Bring Your Own Key (BYOK) and self-hosted on-premise installations.

How Fast Are ScanDrix Code Reviews On New Pull Requests?

ScanDrix is built for sub-minute reviews: an async worker architecture picks up webhook events without blocking CI pipelines. That is a design goal, not a measured guarantee — live service state is on the status page.

Can ScanDrix Suggest Inline Code Fixes?

Yes. When ScanDrix detects bugs, security vulnerabilities, or rule violations, it generates precise inline diff suggestions directly in the pull request review comments that developers can apply with a single click.

Can We Deploy ScanDrix On-Premise Or In Our Own VPC?

Yes. Enterprise customers can deploy ScanDrix in their own AWS, GCP, or Azure VPC, or in fully air-gapped on-premise environments with support for internal LLM endpoints (vLLM, Ollama) and enterprise SSO.

Kubernetes Deployment (Helm)

Production-grade Kubernetes deployment guide with Helm chart, Horizontal Pod Autoscaling (HPA), and secrets management.

For high-throughput enterprise environments, ScanDrix provides official Helm charts and Kubernetes manifests for deployment on AWS EKS, Google Cloud GKE, Azure AKS, or bare-metal Kubernetes clusters.

Architecture topology

A complete Kubernetes deployment separates API ingestion from asynchronous review workers:

code

[Ingress (TLS)]
       │
       ▼
[scandrix-api] (Go REST Service, 8080)
       │
       ├──► [PostgreSQL] (Row-Level Security, pgxpool)
       │
       └──► [RabbitMQ] (Quorum Queues & Delayed Exchange)
                  │
                  ▼
          [scandrix-worker] (Auto-scaled by queue depth via KEDA / HPA)
                  │
                  └──► [Ephemeral Tree-sitter & Model Runners]

Quick installation with Helm

bash

# Add official ScanDrix Helm repository
helm repo add scandrix https://charts.scandrix.dev
helm repo update

# Install ScanDrix into dedicated namespace
helm install scandrix scandrix/scandrix \
  --namespace scandrix-system \
  --create-namespace \
  --values values.production.yaml

Production `values.yaml` configuration

yaml

global:
  domain: "scandrix.internal.yourcompany.com"

api:
  replicaCount: 3
  resources:
    limits:
      cpu: 2000m
      memory: 4Gi
    requests:
      cpu: 500m
      memory: 1Gi
  env:
    PORT: "8080"
    DB_MAX_CONNS: "50"
    DB_MIN_CONNS: "5"

worker:
  replicaCount: 5
  autoscaling:
    enabled: true
    minReplicas: 3
    maxReplicas: 50
    targetRabbitMQQueueLength: 5

database:
  host: "postgres-cluster.internal.vpc"
  port: 5432
  name: "scandrix_prod"
  sslmode: "require"
  existingSecret: "scandrix-db-credentials"

rabbitmq:
  clusterHost: "rabbitmq.internal.vpc"
  port: 5672
  existingSecret: "scandrix-mq-credentials"

Horizontal Pod Autoscaler (HPA)

The review worker pool scales dynamically based on the number of unacknowledged pull request diffs in RabbitMQ:

yaml

apiVersion: keda.sh/v1alpha1
kind: ScaledObject
metadata:
  name: scandrix-worker-scaler
  namespace: scandrix-system
spec:
  scaleTargetRef:
    name: scandrix-worker
  minReplicaCount: 3
  maxReplicaCount: 40
  triggers:
    - type: rabbitmq
      metadata:
        queueName: "scandrix.review.tasks"
        targetQueueValue: "2"

Setting targetQueueValue: "2" guarantees that whenever a PR spike occurs (e.g. at the end of a sprint), workers immediately scale up to review pull requests in parallel.

PreviousSelf-Hosted Deployment NextReverse Proxy & TLS

Kubernetes Deployment (Helm)

Architecture topology

Quick installation with Helm

Production values.yaml configuration

Horizontal Pod Autoscaler (HPA)

Production `values.yaml` configuration