MOAI Labs
AI.CleanCode

Built into the Workflow

We add security expertise directly to R&D — findings, explanations, and fixes appear right in the editor, ensuring secure code at the development stage.

AI.CleanCode workflow architectureThe employee perimeter contains the development environment, plugin and AI assistant. One secure channel connects the plugin with the enterprise perimeter: intake, seven scanning engines, project memory, and local models requiring no internet.EMPLOYEE PERIMETERENTERPRISE PERIMETERyour server, cloud, or hybridDevelopment EnvironmentVisual Studio Code, Visual StudioPlugininstalls in a couple of minutes, flags the issueright as you typeAI Assistantwrites code alongside you — its output ischecked by the same pluginone securechannelINTAKESEVENENGINESPROJECTMEMORYEditor serviceWeb interface & reportsCI/CD integrationCode analysisDependenciesSecretsLive applicationPentestCode fuzzingAPI fuzzingFindings & settingsScan artifactsSecurity knowledge baseMetrics & analyticsMODELSCode parsing & explanationSemantic searchdeployed inside the perimeter — no internet required

1. Scans as you code

The plugin installs in minutes and embeds seamlessly into MS Visual Studio and Visual Studio Code. It flags vulnerabilities in your code while it's being written — not after the fact.

  • AI-assisted triage: each finding is validated in context before the developer sees it
  • Vulnerable lines are highlighted inline, with a suggested fix next to them
A 7-Step Method for Reducing False PositivesApproximately 100 incoming findings from seven engines pass through layers 0 to 6 to yield 5–7 confirmed findings with proof. Bar heights illustrate the successive filtering stages, not measured intermediate counts.▸ SEVEN ENGINES≈100incoming findingsfrom seven enginesLAYER 0SeveralindependentscannersLAYER 1Dedup &engineagreementLAYER 2Reachablevia callgraphLAYER 3Multi-signalcode/standcheckLAYER 4Auto-triage:rules, model,memoryLAYER 5Proof viaexploit &fuzzingLAYER 6Feedbackloopanalytics5–7confirmed,with proof

2. Server-side code validation

A 7-Step Method for Reducing False Positives

Every finding is re-checked by a security-tuned model in the context of the surrounding code, deduplicated by fingerprint across scans, and ranked by severity and by whether the vulnerable code is actually reachable.

  • Automated tracking in vulnerability-tracking systems
  • Mark false positives once to filter them out going forward
Unified project memorySeven analysis sources feed unified project memory: findings registry, call graph, attack surface, finding relationships, architecture overview, and decision memory. It produces one findings list, deduplicated across engines, with proof and priority.Source code analysisOpen-source componentsSecrets in codeLive applicationPentestCode fuzzingAPI fuzzingUnified project memoryFindings registrydeduplicated, withhistory per findingCall graphthe path data takes to adangerous locationAttack surfacewhat the applicationexposes externallyFinding relationshipsissues that share asingle root causeArchitecture overviewhow the system is builtand what matters in itDecision memoryhow your analyst judgedsimilar cases beforeOne findingslistdeduplicated across engines,with proof and priority

3. Unified project context

Flagged issues are logged automatically; fixed code drops off the list on its own. Teams get secure code without slowing releases down, plus the evidence trail auditors ask for: every finding, suppression, and policy change is logged and maps to NIST SSDF practices.

  • Security champions get the data to lead by example
  • No surprises during testing or after deployment