Every Tool Scans.
We Attack.

2027 is Breach Year

In 2025, AI wrote roughly 60% of all new code. By the end of 2026, it will be closer to 90%. AI code generators are fast, fluent, and confidently wrong. They produce plausible output that passes a glance review, compiles cleanly, and hides security flaws in patterns that look correct. Worse, they produce code en masse -- faster than any human team can review it. The volume alone guarantees that vulnerabilities ship.

1. Multi-Agent Adversarial Architecture

Traditional scanners run one pass with one set of rules. AI code review tools typically use a single LLM prompt. ShipItClean deploys up to 108 independent agents of your choice -- 28 of them strictly security -- each with a different attack surface specialization, that analyze your code in parallel.

Each agent operates independently. An SQL injection specialist does not know what the auth bypass agent found. A concurrency analyst does not share context with the cryptographic weakness detector. This independence is the point. When three agents independently flag the same function from different angles, that finding is real. When only one agent flags something and the others pass, the consensus engine weighs that differently.

2. Unlimited Context. No Forgetting.

Every AI tool runs on a foundation model with a hard context limit. The biggest frontier models cap out around 2 million tokens. A serious engineering codebase, once you include dependencies, tests, and infrastructure, routinely exceeds that. Mid-size monorepos hit 10 million tokens. Mature products hit 50 million or more. We scanned Mozilla Firefox -- 44 million tokens -- with 36 agents. That is 1.6 billion tokens processed in a single audit.

When a scanner hits its limit, it starts forgetting. A vulnerability flagged early gets lost by the time the reviewer reaches the file that exploits it. Cross-file attack chains fragment. Every sliding-window scanner has this failure mode.

3. Agents Argue. False Positives Die.

The worst thing about most security scanners is the noise. Hundreds of findings, most of them false positives, burying the three critical issues that actually matter. Development teams learn to ignore scanner output entirely, which is worse than not scanning at all.

ShipItClean runs a consensus engine after agents complete their analysis. Findings are cross-validated, deduplicated, and severity-ranked. A finding that multiple independent agents confirm from different attack angles gets elevated. A finding that only one agent flags with low confidence gets downgraded or filtered. The result is a report with real issues, not a wall of noise.

4. Fix Workflow Included

Most scanners tell you what is wrong and leave you to figure out what to do about it. ShipItClean generates a copy-paste fix workflow for every scan. Hand it to Claude, Cursor, Copilot, or any AI coding assistant. It walks through every confirmed finding, one at a time, with file references, severity context, and suggested fixes, until they are resolved.

The scanner finds the problems. Your AI assistant fixes them. Then you scan again to verify. That is the loop. Finding vulnerabilities is half the job. Fixing them is the other half. We handle both.

5. We Are Building Our Own Models

Every competitor in this space pays API fees to third-party model providers -- and marks them up before passing the cost to you. We are actively raising capital to train and run our own security-focused models on our own hardware. When that is live, the API middleman disappears. Our costs drop. Your costs drop further.

Most scanners charge $20-$50 per month for pattern-based detection. ShipItClean gives you all 360,000+ detection patterns for free already. AI adversarial scans are pay-per-use based on codebase size -- no subscriptions, no minimums.

Who This Is For