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.

Customers / Illustrative scenarios

Engineering scenarios, honestly labeled.

We have no customer deployments to report yet, so there are no case studies here. The entries below are illustrative architecture patterns for automated pull request reviews — useful shapes, not proof.

Eval domains: 11
Score pillars: 4 · 0.75 bar
Location tolerance: ±2 lines
Leaderboard scores: Not published

Platform figures describe the evaluation harness, not customer deployments. See the methodology

01Illustrative scenarios

Patterns, not proof.

Each entry records a context and an outcome shape. Every one is an illustration — no customer, no deployment, no measured result.

01
High-Throughput Payment Gateways
Fintech & payment processors
Go · TypeScript · PostgreSQL · Kafka
Senior review time redirected from manual taint checking to core product architecture.
High-throughput financial systems require automated verification of untrusted inputs across database boundaries. ScanDrix executes deterministic AST queries and source-to-sink dataflow tracking with fast turnaround, without manual checking of every sensitive route.
Illustrative scenario
02
Cloud-Native Infrastructure
Regulated infrastructure & banking
Go · Kubernetes · AWS GovCloud · gRPC
Unified automated review baselines with compliance evidence generated natively from CI runs.
Standardized review policies across multi-repository organizations prevent configuration drift and insecure patterns. Continuous AST and semantic checks generate structured audit trails directly from pull request reviews, aligning with SOC 2 Trust Services Criteria.
Review baseline
Declarative Drixy rules
Compliance readiness
Audit trails from CI
Illustrative scenario
03
Distributed Engineering Teams
High-throughput microservices
Python · Go · RabbitMQ · Redis
Non-blocking automated code reviews for asynchronous engineering teams across time zones.
Line-level review comments with actionable diff suggestions allow pull request authors to immediately apply corrections and merge without waiting on multi-day cross-timezone review cycles.
Illustrative scenario

02Our testing, plainly

Where our testing numbers come from.

These describe the evaluation harness, not customer telemetry. The harness scores findings against ground truth automatically — and publishes no leaderboard.

We do not present a modelled saving as a result. The ROI calculator applies a stated 65% review-time assumption. That is an estimate to test against your own numbers, not a measurement, and it is labelled that way there.

Engine eval gate: 11 domainsAnchoring, dedup, severity, format, investigation, rules, parser, summary, promotion, scorer, secondary — each with behavioral checks.
Composite score: 4 pillars · 0.75 barRecall, precision, format and anchor location weighted into one number per run. Below the bar, the gate blocks.
Ground-truth tolerance: ±2 linesRule violations score against ground-truth file + line sites: recall, precision, F1.
Public scores: Not publishedNo leaderboard and no competitor numbers until independent runs exist. Methodology first.

Engine evals: 11 domains behind a fail-closed gate. No public leaderboard. Read the full evaluation

03Design principles

What the system is built to do.

These are statements about the architecture, from the teams that build it — not customer testimonials. We have none yet, and we won't invent any.

ScanDrix combines tree-sitter AST queries with large language model reasoning, ensuring that every code review suggestion is backed by concrete structural proof rather than speculative hallucinations.

Deterministic AST ArchitectureStatic Analysis Engine, ScanDrix Core

By enforcing version-controlled Drixy rules across every repository, engineering teams establish uniform security baselines and generate verifiable audit trails automatically on every pull request.

Policy & Compliance FrameworkGovernance Protocol, ScanDrix Enterprise

When real customers agree to be named, their words will appear here with verified attribution. Until then, this section stays as architecture statements. If you run ScanDrix and want to be first, tell us.

Next step

Run it on your own pull requests.

Connect a repository and read the first review before believing any claim on this site. Or start with the evaluation behind our claims.

Book a live review See benchmark methodology

Engineering scenarios, honestly labeled.

Patterns, not proof.

High-Throughput Payment Gateways

Cloud-Native Infrastructure

Distributed Engineering Teams

Where our testing numbers come from.

What the system is built to do.

Run it on your own pull requests.