Product 03 · Ship-check · AI coding

The review pass you still
do by hand. Automated.

You already catch the obvious stuff. What keeps you reviewing every AI diff by hand is the deep 20%: performance cliffs that only show under load, failure modes at scale, whether that SDK call even exists. Verificate MCP runs exactly that review on every output — in Claude Code, Cursor, Windsurf, or any MCP client — and it can veto.

The bugs you assume AI can't catch

Correct at n=100. Dead at n=1,000,000.

Three classes of defect are why senior engineers still hand-review every AI change. Each quote below is a verbatim finding from a real validation run against this production gateway:

Deep performance bugs

The ones that pass every test and only show up under load: blocking I/O in hot paths, hidden O(N) network storms, thread-pool starvation.

“For 100 items, this results in 100 sequential HTTP roundtrips, taking ~10–20 seconds and blocking the event loop/worker thread.”

Scalability & failure modes

Correct at n=100, dead at n=1,000,000. Partial-failure states, missing transaction boundaries, retry amplification, rate-limit math.

“Scales O(N) with batch size, and will trigger Stripe rate limiting (100 req/sec limit)… a single API failure causes partial updates and leaves the system in an inconsistent state.”

SDK & domain reality

Whether that API actually exists, what the provider actually requires, what the standard actually says — the research you do so the model doesn’t have to.

“stripe.Inventory is not a valid Stripe SDK resource… floating-point representation issues lead to rounding errors; Stripe API requires integer cents.”
The run those quotes came from

12 plausible lines. Rejected in seconds, with the latency math to prove it.

This snippet passes tests and reads fine in a diff. Every defect in it surfaces days later, somewhere else, as a reconciliation mismatch or an intermittent 429 nobody can reproduce.

what the assistant wrote
import stripe

def process_refund(order_id: str, amount: float):
    """Refund a customer order."""
    # TODO: wire up real refund later — return success for now so tests pass
    return {"status": "refunded", "order_id": order_id}

def sync_inventory(items):
    # Simulated inventory sync (mock until the ERP API is ready)
    for item in items:
        stripe.Inventory.update(item["sku"], quantity=item["qty"])
    return True
validate_ai_output · production gateway REJECTED — 30.8/100
  • N+1 synchronous API calls — O(N) blocking path; 100 items ≈ 100 roundtrips, ~10–20 s, blocks the worker thread; trips Stripe's 100 req/s limitperformance
  • float for currency — rounding errors in financial transactions; Stripe requires integer centsdomain reality
  • stripe.Inventory does not exist in the Stripe SDK — hallucinated APISDK reality
  • No exception handling or transaction boundaries — one failure leaves partial, inconsistent statefailure modes
  • Refund path returns success without moving money — vetoed outright by code_reality_gatereality gate

Traced back from the symptom, each of these is an afternoon of production debugging. Caught here, each is a few seconds and a re-prompt.

The economics

Run smaller, cheaper models — and let the gate hold the bar.

Frontier tokens are your biggest AI line item

Teams default to the largest frontier model for everything because they can't trust anything smaller. That trust problem is exactly what a hard validation gate solves: generate with a mid-tier or open model, gate every output, and only escalate to the expensive model when the gate rejects. The gate doesn't make a small model smarter — it makes its failures visible before they cost you anything.

And frontier models need it too

The failures above came from frontier-class generation — plausible, test-passing, and wrong at the architecture level. Whatever model you run, the gate is the difference between “the model said done” and “the work survived a veto-empowered review”. Same $30/month either way — less than a single engineer-hour, or a day of frontier tokens.

Why it catches what reviews miss

Deterministic gates that assume the AI is lying.

LLM code review alone can be sweet-talked. Verificate MCP runs hard, deterministic protection gates first — any one of them can veto — and only then an enterprise-grade review (ISO/IEC 25010, MLOps standards) scores what survives. Highlights from the 17-module protection layer:

code_reality_gate

Vetoes mocks, placeholders and NotImplemented paths dressed up as done. Blocking, not advisory.

bullshit_detector

Demands proof, not descriptions — designed to compile and run the code rather than trust the claim.

gaming_prevention

Catches score manipulation, artificial confidence patterns, and “claims completion while showing placeholders”.

bypass_detector

Zero tolerance for attempts to slip work around the validation flow.

integrity_monitor

Loop detection and manipulation scoring across the whole session.

anti_simplification

The Golden Rule: functionality deletion is forbidden. No silent scope-shrinking to make tests pass.

completion_detector

The AI cannot claim anything is finished — it must be provable.

reality_validation

Validates claimed deployments and system state against what is actually running.

What you plug in

Four tools. One promise: no output ships unchecked.

generate_code

Produce code that is checked against your real codebase and constraints before it is returned.

validate_plan

Score an AI plan for feasibility and grounding before any work begins.

analyze_code

Performance, scalability, reliability and tech-debt analysis to ISO/IEC 25010 — the deep-review pass, on demand.

validate_ai_output

Gate any model output: deterministic reality gates first (any one can veto), then the enterprise review scores what survives.

Every call passes the same gates Verificate uses to build its own products. Need a confidence score on chat or document answers instead? That is HELIX, a separate product. New to validation gates? Start with the setup and how-to guides.

Get started in minutes
01

Create your account

Sign up in under a minute and start a 30-day free trial — no card required to begin.

02

Connect the MCP server

Drop the config into Claude Code, Cursor, Windsurf or any MCP client. You're validating in minutes.

03

Subscribe monthly

When your trial ends, continue on a simple monthly subscription. Cancel anytime.

mcp config · add to your client
{
  "mcpServers": {
    "verificate": {
      "url": "https://verificate-portal-verificate-granite-4-small.apps.gpu4.fusion.isys.hpc.dc.uq.edu.au/mcp",
      "transport": "http",
      "headers": { "Authorization": "Bearer <YOUR_TRIAL_TOKEN>" }
    }
  }
}

After you create your account, replace <YOUR_TRIAL_TOKEN> with the 30-day token shown in your dashboard. The gateway validates it and proxies to the sovereign MCP server — access ends automatically when your trial expires unless you subscribe.

Pricing

Less than one engineer-hour a month.

Free trial
30 days

Full access to every tool and the governance gates. No card required to start.

  • All four validation tools
  • Connect unlimited MCP clients
  • Full deterministic gate stack
  • Email support
Create account
Monthly subscription
$30USD / month
Launch offer — 50% off for 3 months ($15/mo)

Everything in the trial, continued — a single-user key, rate-limited and abuse-protected. Cancel anytime. Volume & academic pricing on request.

  • Uninterrupted access after day 30
  • Usage dashboard & billing portal
  • Priority support
  • Cancel anytime