Zerethon FlowExecution Intelligence Platform
Understand your application.
Not just your code.
Flow traces real backend execution — request, controller, service, database, external calls — and pairs it with website-level security, SEO, and AI-readiness analysis, so engineering teams see what an application actually does instead of inferring it from source code alone.
Built for engineering teams
Understanding a codebase takes too long
Every non-trivial application accumulates knowledge that lives nowhere but in the heads of the people who built it — how requests actually flow through the system, which services really talk to which, where the bottlenecks are, and which code paths are quietly dead. New team members, auditors, and even the original authors end up reading thousands of files by hand to rebuild that picture, one incident or onboarding at a time.
That manual process doesn't scale, and it's especially costly for the people who most need a fast, accurate picture: CTOs and engineering leaders evaluating a codebase, tech leads onboarding new engineers, and architects planning a refactor or migration.
Product Overview
How Flow understands software
Runtime Trace Intelligence
Real request → controller → service → database → external-call timing, deterministically scored to identify the actual bottleneck — with confidence and evidence, not a guess.
Website & Security Analysis
Missing security headers, broken SEO fundamentals, and structured-data gaps, surfaced with the exact evidence found on the page — not a generic checklist.
API Intelligence
Point Flow at your OpenAPI spec and see undocumented endpoints, missing auth, and insecure server URLs before your API consumers find them first.
AI Readiness
Whether AI agents and LLMs can actually discover and use your site — llms.txt coverage and AI-crawler access, checked automatically.
How Flow works
- 1
Add a project
Point Flow at a URL — your own site, staging, or anything public.
- 2
Flow crawls and correlates
The Engine crawls the site and, if the Flow Adapter is installed, correlates each request to its real backend execution trace.
- 3
Analyzers run
SEO, security, structured data, API, and AI-readiness checks run against what was found — plus deterministic bottleneck detection for any correlated trace.
- 4
Review a scored report
Every finding ships with real evidence — the exact header, tag, or timing measurement behind it — not just a claim.
Coverage
Supported languages & frameworks
Laravel instrumentation is live today. Every other stack below is on the roadmap — nothing here is overstated as working before it actually does.
Languages
Frameworks
Platform features
Deterministic Bottleneck Detection
Scores every eligible node in a real execution trace and explains why it picked the one it did — not a black box.
Execution Trace Rendering
The real request → controller → service → database/external breakdown, rendered as a readable tree.
Technical SEO Analysis
Title/meta description length, canonical tags, heading structure, missing alt text — checked against the actual rendered page.
Security Header Analysis
CSP, HSTS, clickjacking protection, and MIME-sniffing headers — checked against the real HTTP response.
Structured Data Validation
Whether valid JSON-LD is present, and whether it's missing required @context/@type fields.
API Intelligence
Discovers an OpenAPI/Swagger spec and checks it for missing auth, undocumented endpoints, and insecure server URLs.
AI Readiness Checks
llms.txt presence and whether robots.txt blocks known AI crawlers (GPTBot, ClaudeBot, and others).
Run Comparison
See exactly what got fixed, what's new, and what's still open between any two scans of the same project.
Roadmap
Integrations
Not live yet — this is where Flow is headed next.
GitHub
PlannedRun Flow automatically on every pull request and see results inline.
Slack
PlannedGet notified the moment a scan finds a critical issue.
CI/CD
PlannedFail a build when a deploy introduces an architectural regression.
Who Flow is built for
Trust
Why teams trust Flow
Never asked to believe — every point below is something you can verify yourself.
- Your source code is never read or uploaded — Flow observes execution, not source.
- Runtime analysis is deterministic — scored and explained, not a black-box model.
- Every finding ships with real evidence, not just a claim.
- Sensitive data (tokens, secrets) is masked by default when captured.
- The Flow Adapter never initiates outbound network calls — it only responds when asked.
Runtime analysis, not just static analysis
Static analysis tools read source code without running it, so they can miss dynamic behavior and runtime-only relationships. Flow observes the application actually executing — producing a picture of real request flow, real dependencies, and real bottlenecks, not just what the code implies.
How is Flow different from SonarQube or Semgrep?
SonarQube and Semgrep read source code without running it. Flow's core differentiator is the opposite: it observes an application actually executing — real request → controller → service → database → external-call timing — and deterministically identifies which node is the real bottleneck, with confidence and evidence attached, not a static rule match.
How is Flow different from Datadog or New Relic?
Datadog and New Relic are always-on production observability platforms with a data pipeline you operate continuously. Flow today is on-demand: you run a scan, get a scored report, and can compare it against a previous run. It isn't a continuous monitoring replacement yet.
Does Flow upload or store my source code?
No. The Flow Adapter installed in your application never reads, uploads, or transmits source files — it observes execution through your framework's own event system (query events, HTTP client events, a lightweight method-timing wrapper), not by inspecting code.
Can I self-host Flow?
Not yet as a supported, packaged product — there's no Docker image or install guide today. It's on the roadmap; until then, Flow runs as a hosted service at flow.zerethon.com.
How much runtime overhead does the Adapter add?
We haven't published a specific number, because we haven't run a real benchmark yet — and we'd rather say that than guess. What we can say precisely: collection is synchronous (adds to request latency, doesn't hide behind the response), and it's disabled by default outside local/staging environments.
Does Flow use AI?
Not yet, and we're deliberate about that: the deterministic bottleneck-detection engine (scoring, confidence, explainability) is the actual product today, with no AI in the decision path. An optional AI layer for summarizing and explaining findings in natural language is planned, but it's designed to sit strictly downstream of the deterministic engine — it will never be allowed to decide whether a finding exists or change its severity.
Do I need to give Flow production access?
No. Flow analyzes whatever URL you point it at — most teams start with staging or a public-facing page. The Adapter itself is a passive component: it never initiates outbound calls and only responds when the Engine asks it a question over HTTP.
Can Flow work without installing anything in my application?
Yes, partially — the SEO, security, structured-data, API, and AI-readiness analyzers run against any public URL with no installation required. The Runtime Trace bottleneck detection specifically needs the Flow Adapter installed to correlate a request to its real execution timing.
Does Flow build a dependency or architecture graph?
Not today. This is a real gap between where Flow is now and where it's headed — Runtime Trace Intelligence (the bottleneck engine) is shipped and works; a broader dependency/architecture graph is a future pillar, not a current feature. We'd rather tell you that clearly than show you a diagram that isn't real.
Why does runtime analysis matter more than reading the source?
Source code tells you what a system is capable of doing. Runtime execution tells you what it actually did — which query really ran, which external call really dominated the request, which code path was actually hit. Flow is built around that distinction: evidence over inference.
Ready to understand your application?
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