Every other autonomous AI-pentesting platform either tests the model and hands you a risk score, or sells straight to your client and cuts you out. MILLENNIUMS.AI does neither — it finds and proves real exploits in your clients' AI apps, PoC-backed and mapped to the OWASP LLM Top 10, delivered white-labeled under your firm's name. Take on 2–3× the AI-app engagements with the analyst team you already have.
No guardrail dashboards. No risk scores without proof. No vendor selling around you to your own clients — just the recon-to-PoC layer of a real pentest, done in minutes and handed back to you to finish, sign, and bill.
If you're testing AI-native applications — agents, LLM apps, RAG pipelines, anything with a model in the loop — you've felt this. Infra and network pentesting matured over two decades of scanners, frameworks, and playbooks. AI-app testing hasn't. Every engagement starts closer to research than to a repeatable process, which means it eats far more analyst time than your client's invoice assumes.
That's not a knock on your team. It's a tooling gap — the same gap that's currently capping how many AI-app engagements you can take on.
You don't need us to explain what a professional-tier AI-app pentest costs — you're the one billing it. Laid out plainly, the economics make the case themselves.
A mid-size SaaS app — 50–100 pages, a REST API, the closest shape to most AI-app clients — with 5–10 business days of hands-on testing, usually stretched into 2–4 calendar weeks by scheduling and reporting.
Plugins, RAG pipelines, agentic tools. Starts at $9,500+ for a single chatbot; enterprise multi-agent platforms and fine-tuned models run $35K–75K. The surface is newer and more specialized, so it bills higher.
At boutique day rates of $1,300–$2,600, a typical 10–20 day professional-tier engagement is that much in labor before markup — most of it methodical recon and mapping, not the fraction that needs senior judgment.
Every AI-app engagement carries real analyst-hours of embedded cost — most of it recon, probing, and mapping that follows a pattern, not work that needs a human making a judgment call every step.
Every hour of analyst time spent on repeatable recon and probing is an hour not spent on the work that actually justifies your rate.
Chaining findings into a real attack path, assessing business impact, advising the client on remediation — the work a tool can't do for you.
Analyst-hours are the ceiling on how many clients you can serve. Compress the repeatable work and the ceiling moves.
You still bill the client $15K–35K. If the automatable portion drops from days to minutes, the margin on every engagement improves — not just throughput.
The same shift already happened in appsec scanning and SOC analysis: the repeatable work got automated, and the humans moved up the value chain instead of getting replaced. AI-app pentesting is at that inflection point now — firms that build it in early get the throughput and margin edge before it's table stakes.
The "autonomous AI pentesting" category is crowded, but almost nothing in it was built to sit inside a pentest firm's workflow. Some test the model, not the app. Some sell straight to your client. Some are priced to punish exactly the volume a real practice runs.
Your whole book of business in one account — not capped per-application the way a single-company tool is.
Findings and reports go out under your firm's name, because we're not trying to win your client.
Metered the way you already bill — we're trying to help you win more clients, not disintermediate them.
Unlimited client apps in one account, white-labeled output, priced per engagement the way you already bill — because we're not trying to win your client. We're trying to help you win more of them. See the full comparison →
Finds and proves real exploits, not a list of maybes — prompt injection, tool/agent abuse, data leakage, permission-boundary failures — each backed by a working PoC, the way a human tester documents it. Maps to the OWASP LLM Top 10, so findings drop straight into the compliance evidence your clients' auditors ask for (SOC 2 CC4/CC7 and similar). Runs in minutes on the recon-and-probing layer — the methodical part, not the creative one. Augments your team, never competes with it.
The same way you scope any engagement — models in play, integrations, agentic tools, access level.
It handles recon, injection probing, and tool-abuse mapping autonomously, surfacing proven findings with PoCs attached.
Validate context, chain findings into real attack paths, and write the report in your firm's voice, under your firm's name.
White-label findings and PoC export mean the client sees your firm's report, not ours.
The engine tests every category on the industry-standard AI-security checklist. The full matrix is published live on the Trust Center. See it →
Your whole book of business lives in one workspace, not capped per-application the way a single-company tool would be.
Because that's the unit you already bill and think in.
Your whole analyst team works from the same workspace.
Findings and reports that go out under your name.
Priced per engagement, not per seat — scale from solo consulting to an MSSP practice. Start with a free pilot on one of your live engagements.
Every tier’s per-engagement rate is lower than the tier below it — $399, then $299, then from $249. The more you run, the less each one costs, and a solo plan passes the Firm price at about 20 engagements a month. An engagement is one assessment against one client target. The pentester tool runs the scan and proves each finding; your analysts direct it, validate it, and sign the white-label report — auto-fix pull requests are a developer feature and aren't part of these plans.
Founding partners. The first firms to run Redthread on a live engagement get pilot pricing for their first three months, in exchange for a case study. Ask about the founding-partner track →
Book a 30-minute walkthrough and we'll show you actual findings against a representative AI-app target — no commitment, no card required.