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AI app production review + hardening

Shipping an AI-built app?
Find the risks before launch.

We find and fix the production risks in apps built with Lovable, Bolt, v0, Cursor, Claude Code, and other AI tools—before customers, attackers, or procurement teams find them.

Auth

Sessions, roles, protected routes

Isolation

Cross-tenant reads and writes

Billing

Webhooks, tiers, entitlements

Launch

Logs, tests, deploys, rollback

Fixed-scope first step

One application. Human-reviewed findings. Three-business-day turnaround. The entire $495 fee is credited toward a Hardening Sprint.

Build + Govern

Start with the structure AI needs to stay useful.

The scaffold preview is the control layer in miniature: describe the system, then get architecture, threat analysis, decisions, tests, and deployable files before an agent starts improvising in production.

Production hardening for AI-generated software

Turn AI-generated apps into
production-ready software.

Vibe coding gets you a demo. Stackbilder generates governed scaffolds, threat models, ADRs, and test plans — before your prototype becomes production debt.

stackbilder >
Threat model includedADRs includedTest plan includedCloudflare Workers scaffoldDeterministic output
~20msscaffold generation
0inference cost (Phase 1)
100%deterministic output

Verify before code lands

Every scaffold includes governance output.

.ai/ governance output
threat-model.md
## STRIDE Analysis
T-001: Spoofing — JWT validation bypass via alg:none
T-002: Tampering — D1 SQL injection in query builder
Severity: HIGH | Mitigation: parameterized queries...
adr-001-auth-strategy.md
## Decision
Use cookie-based sessions over stateless JWTs.
Context: Edge runtime + D1 session store available...
test-plan.md
## Integration Tests
test_auth_flow: login → session → protected route
test_billing: checkout → webhook → tier upgrade
Coverage target: 85% | Framework: vitest...

Build on explicit architecture

Requirements, components, data boundaries, and deployment shape are recorded before implementation so generated code has a frame to obey.

Govern decisions and risk

STRIDE threats, ADRs, and constraints give agents permission rails. The output says what is allowed, what is risky, and what has to be tested.

Execute with bounded tools

Durable jobs, tier gates, quotas, and MCP tools keep autonomous actions observable and interruptible instead of turning them into opaque API calls.

Verify with evidence

Receipts, test plans, and trust surfaces make the work reviewable after the model call. Read the field guide →

Execute proof workload

img-forge is the live workload, not the whole company story.

The Tarot Detective is a live detective game whose entire art pipeline runs through the governed img-forge surface — 16 portraits, 23 locations, 28 props. Every character keeps the same face across every scene: once a look is approved, every subsequent call reuses the same seed, so asset 43 still matches asset 2.

Vera Saint-Claire character portrait, TAROT_NOIR style, locked seed Dr. Helena Price character portrait, TAROT_NOIR style, locked seed Vivian Delacroix character portrait, TAROT_NOIR style, locked seed
Assets in production 67
Total generation budget $15–25
Same list, human-illustration rates $3,350–13,400
Style systems, zero drift 2

See the full pipeline breakdown →

Execute with agents

One governed agent flow. One URL back.

Connect MCP, discover allowed tools, call generate_image, receive a job_id and asset_url, and keep billing inside explicit credit controls. REST is there when raw HTTP fits better.

MCP imgforge-mcp.stackbilder.com
# Codex
[mcp_servers.img-forge]
url = "https://imgforge-mcp.stackbilder.com"
auth = "oauth"
oauth_resource = "https://imgforge-mcp.stackbilder.com"

codex mcp login img-forge

# First successful flow
list_models
generate_image(prompt: "a red fox in a snowy forest")
check_job(job_id: "60b3ba6a-...")

11 models, one auth

SDXL, FLUX, Leonardo, and Gemini behind one token and one billing surface. Switch tiers per request — no per-provider accounts, no integration rewrite.

Draft 1 cr Standard 2 cr Premium 5 cr Ultra 10 cr Ultra+ 20 cr

Generate, edit, correct

img2img restyling and mask-based inpainting through the same endpoint. Asset 43 of 67 has a bad hand? Fix the region, keep everything else — no re-roll, no drift from the approved look.

Your agent can respect its budget

billing_status and billing_purchase_credits are MCP tools — an agent checks its balance mid-run, tops up, and keeps generating. Replicate, fal.ai, and Leonardo expose nothing like this to agents.

imgforge-mcp.stackbilder.com →

Agent trust markers

OAuth 2.1 / PKCE, MCP 2026-07-28 compatibility, legacy 2025-11-25 compatibility, read-only discovery through list_models, credit controls, and no surprise overages.

Buyer paths

Self-serve the workload, or bring the control layer to yours.

Start with the platform, use img-forge today, or bring Stackbilt in for a governed customer workflow. No competing primary CTA, just the right next step.

Free

Get started, no strings

$0
  • 5 free image generations / month
  • Draft and standard tiers
  • 3 governed scaffolds / month
  • REST API + MCP access
Get started

Pro

The whole platform, one price

$29/mo
  • 1,000 img-forge credits / month
  • All tiers, Ultra and Ultra+ included
  • Unlimited scaffolds + LLM polish
  • Credit packs stack on top
  • Priority support
Go Pro

Agency

For production pipelines at volume

$75/mo
  • 4,000 credits / month
  • Credits roll over while active
  • ~8 full production runs / month
  • Priority queue + dedicated support
See Agency

Need one-time volume? Builder and Studio credit packs never expire · full plan comparison

Higher-touch path

Need the control layer around your own workflow?

Services cover audits, Cloudflare-native buildouts, governed agent workflows, and trust bundles without forcing the self-serve img-forge buyer path.

Work with us

Start with the control layer.

Free tier includes 3 governed scaffolds and 5 image generations. No credit card. No tokens to buy.

Or get launch updates — no spam, just milestones.