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Enterprise readiness gate for AI-built applications

GradLayer is your path to production.

Give staff and platform leads a five-minute Graduation Report for the last 5% before launch.

Scan an AI-built repo for hardcoded secrets, missing authz, exposed data, uncapped vendor cost, policy gaps, ownership, runbooks, and the controls required to deliver business value safely on day one.

Paste a GitHub repo before signup. ZIP uploads are available after sign-in. Bitbucket, GitLab, and other connected exports are coming soon. See a sample report ->

Scan the repo, not the builder

Works with AI-built apps from these tools, plus any similar repo export.

Today, connect GitHub or upload a ZIP. GradLayer reviews the codebase and produces the same staff-level readiness report whether it started in Lovable, Cursor, Copilot, Claude Code, v0, Bolt, Replit, or a builder we have not listed yet.

LovableCursorGitHub CopilotClaude Codev0BoltReplitOther builders
GitHub reposPrivate GitHubZIP exportsBitbucket soonGitLab soonMore exports soon

The last 5% is where production risk hides

Staff engineers should not have to rediscover every blocker by hand.

AI builders move prototypes into the review queue faster than platform, security, and governance teams can absorb. GradLayer turns the first scan into the evidence a reviewer would look for before saying an app belongs on the production path.

Secrets

hardcoded API keys, tokens, and client-exposed credentials

Authz

missing role checks, tenant isolation gaps, and admin route risks

Cost

uncapped AI calls, vendor cliffs, and funding assumptions

Ops

data exposure, missing owners, runbooks, and day-one controls

Deployment review, step by step

From repository to readiness report

Every submission runs the same review path: ingest the app, map its architecture, evaluate policy and risk, model cost, and return guidance your team can use before deployment.

  1. 01

    Ingesting the app

    Network

    Imports the repository or export and filters out generated noise so the review starts from meaningful application code.

    • GitHub tree API, public repo import, or ZIP extraction
    • Skips node_modules, dist, build, .next, lockfiles, binaries
    • Caps at 500 files and 500 KB per file

    Emits: Filtered file list with contents

  2. 02

    Mapping the codebase

    Builds a durable file map so every readiness signal can point back to the exact source evidence behind it.

    • Writes each text file to durable storage
    • Records the file tree for application review
    • Clears old versions on rescan so storage stays bounded

    Emits: application file map with evidence-backed snippets

  3. 03

    Building architecture graph

    Network

    Extracts imports, analyzes files in batches, and builds an interactive knowledge graph of the codebase structure.

    • Deterministic import map across TS/JS, Python, Go
    • Batched gpt-4o-mini file analysis for nodes and edges
    • Architectural layer assignment (API, Service, Data, UI)

    Emits: knowledge graph JSON with file, function, and class nodes

  4. 04

    Profiling frameworks and services

    Network

    Identifies frameworks, routes, libraries, services, APIs, and data patterns so reviewers understand what the app is built to do.

    • README-first repo purpose inference
    • Manifest, route, API, and package structure summary
    • Architecture and data-flow profile with confidence and evidence

    Emits: application profile with architecture and service evidence

  5. 05

    Reviewing exposure

    Checks for credentials, tokens, public keys, unsafe client exposure, and patterns that could leak data or vendor access.

    • OpenAI, Anthropic, Stripe, GitHub, AWS access keys
    • Supabase service role, Twilio, SendGrid, Google API keys
    • Skips test fixtures, .env.example, and documentation

    Emits: exposure findings with file + line number and stage summary

  6. 06

    Checking dependency and policy risk

    Network

    Cross-references declared packages against OSV and maps dependency signals into deployment and policy risk.

    • Parses package.json, requirements.txt, and equivalents
    • Batch query to osv.dev covering npm, PyPI, and more
    • Reports severity, CVE ID, affected package, and policy impact

    Emits: dependency and policy-risk findings

  7. 07

    Evaluating architecture readiness

    Static analyzers inspect production readiness across identity, access, data, infrastructure, and compliance boundaries.

    • Identity — auth provider detection, weak hashing (MD5/SHA1), hand-rolled sessions
    • Access — admin route gating, role checks, tenant isolation
    • Data — migrations, PII / regulated columns, sequential IDs
    • Infrastructure — Dockerfile / IaC, CI workflow, lockfile, .env.example
    • Compliance — LICENSE, HIPAA / PCI signals, consent mechanism

    Emits: architecture, compliance, and readiness findings

  8. 08

    Modeling infrastructure and vendor cost

    Maps paid services, AI calls, storage, runtime needs, and scale assumptions into a practical deployment cost model.

    • Unbounded LLM calls (no max_tokens)
    • Paid-vendor SDKs on unauthed public routes
    • Per-vendor monthly cost from the vendor registry

    Emits: cost findings, monthly estimate, and scale assumptions

  9. 09

    Generating deployment guidance

    Network

    Rolls findings into a readiness verdict, policy summary, remediation playbook, and deployment runbook.

    • Pass / watch / fail per pillar from finding severity mix
    • Verdict: ready, conditional, or not-ready
    • Summary, remediation plan, and runbook text for the report

    Emits: the deployment readiness report

What GradLayer produces

A readiness gate between AI-built code and production.

Each review turns a repository into concrete deployment intelligence your staff engineers, platform team, and governance stakeholders can act on.

Readiness

A deployment readiness verdict with evidence, blockers, and the next actions required to move a prototype toward production.

Security

Secrets, dependency risk, exposed endpoints, weak auth patterns, and unsafe data paths surfaced with file-level evidence.

Compliance

Policy mappings for regulated data, retention, consent, access control, auditability, and organization-specific deployment rules.

Architecture

Frameworks, services, data flows, external APIs, and backend gaps translated into practical production recommendations.

Cost

Infrastructure and vendor cost curves for AI calls, storage, databases, auth, queues, and external services before usage scales.

Remediation

Prioritized fixes, ownership notes, and deployment runbooks that help teams resolve risk without slowing the builder loop.

The artifact

One report for the deployment decision.

Every review produces a deployment readiness report: application profile, risk verdict, policy mapping, architecture recommendations, cost forecast, funding assumptions, and a remediation playbook. It is built for the teams that need to say what is ready, what needs work, and why.

  • Readiness verdict with supporting evidence and deployment blockers
  • Risk findings grouped by security, authz, compliance, data, architecture, and cost
  • Infrastructure and vendor cost model with scale and funding assumptions
  • Policy mapping, ownership notes, and deployment runbook for accountable follow-through
See a full sample report
app.gradlayer.com/apps/lovable-todo/report

Deployment readiness report

lovable-todo

github.com/acme/lovable-todo@main

Conditional

Conditional readiness. 2 critical risks, 5 high-priority fixes. Estimated monthly vendor spend: $1,840. Resolve exposure and policy blockers before production.

2

Critical

5

High

11

Medium

6

Low

3

Info

Top deployment blocker

Critical

Supabase service-role key exposed to client

lib/db.ts:47 - exposure

Who this is for

For teams turning AI-built apps into governed systems.

Built for the people accelerating AI adoption without losing sight of security, cost, compliance, or operational ownership.

“We want teams building with AI. We also need to know which prototypes are ready, what they depend on, and what they will require in production.”

Maya

Head of Platform

GradLayer turns each AI-built app into an operational review artifact.

“The goal is not to slow builders down. It is to give security, compliance, and engineering the same evidence before deployment.”

Marcus

AI Governance Lead

GradLayer maps policy, risk, and remediation into one readable report.

Put your next AI-built app on the path to production.

Point GradLayer at a repository and get the risk verdict, cost model, policy mapping, business readiness, and remediation path in one report.

Generate a Graduation Report