A vertically-integrated fleet of agents that pentests your apps, mines every finding into training data, and finetunes its own next-generation model — continuously.
Six autonomous stages. Every product is a tool and a data source. Frontier models fill skill gaps. Multiple judges validate every release. Zero humans in the loop.
Every product is both a tool and a data source. Research findings become training data. Training data becomes better agents. Better agents find harder bugs. The system never stops — and the moat compounds with every device-hour.
Every component — orchestrator, agents, training stack, eval harness — deployed entirely inside your environment. Air-gap supported. Zero outbound traffic.
Jailbroken iPhones, rooted Androids, real selectable handsets — racked in your DC. Unlimited autonomous test-hours, never throttled, no per-call billing.
Finetune Studio produces a model that lives only inside your walls — improved continuously from your own research, never sent upstream. Sovereign weights.
Nothing leaves the boundary. Zero outbound. Zero telemetry. The loop runs as fast as your hardware lets it. ∞ runs · ∞ data.
Watch the platform handle "Build a working exploit for iOS WebKit on iOS 26.2.1" — from question, through agent collaboration, to working PoC.
iOS · Android · Backend API · Web portal — coordinated by PO, executed by Djini specialists, validated by QA, delivered to Compliance with fix-tickets in your repo.
Point-in-time engagements. 4–8 week turnarounds. Findings reset every release.
Talent for WebKit-class research is rare — and costly to retain once hired.
One platform. One contract. The capability of a senior research org — without the recruitment cycle.
You're not replacing your security team. You're replacing the external firm you keep re-hiring — and the research infrastructure you'd otherwise spend two years building. Djini delivers both, continuously.
Illustrative · varies by scopeMid-tier UK specialist firm, full-scope. 4 – 8 week turnaround. One snapshot, then silence.
Platform runs on your hardware. Scale is bounded by your device-lab capacity, not your budget.
The pentest isn't just a line item — it's a release blocker. External engagements stretch across weeks of procurement, testing, reporting, fixing, and retesting. Djini moves that loop inside your CI.
Five questions every CISO asks before choosing a security platform: what does it cost, where does my data go, will it do the work, can it touch real devices, and does it learn over time.
The question we hear in every meeting: "why not just use a frontier model?"
— Three reasons it doesn't work
Even casual Opus usage is expensive. Continuous scanning across a serious codebase, with dynamic instrumentation, runs into hundreds of thousands per app, per year — before refusals trigger retries.
Runs on your hardware. Flat platform fee, unlimited scans, budget you can hand to the CFO with three decimal places.
Every regulated customer asks the same thing: can it run locally? Frontier APIs send your code, findings, and PoCs to a third party — a non-starter under most banking, healthcare, and defence frameworks.
Sovereign by design. Air-gappable. Audit-grade logs stay with you. Nothing — not a token, not a finding — leaves your perimeter.
Frontier models refuse offensive security work — by policy, by classifier, by silent reroute. The closer you get to a real attack workflow, the more often the model walks away from the keyboard.
Built on frontier-grade foundations, distilled from the same families of models. Then trained on something the frontier labs cannot have: thousands of real dynamic pentests run by Djini — validated exploits, fail traces, PoCs, and the telemetry behind them.
Same foundations. Yours, unrestricted.
— Confidential