About 9robots
Hardware-encrypted AI for pharma manufacturing — your data stays yours, even mid-inference.
Who we are
9RobotsAI GmbH — Swiss-headquartered, building security-first AI infrastructure and solutions for pharma manufacturing. ColdVault, GlassHood, and TestRobin are live today.
Why we exist
When you send data to an AI model, it is processed in plaintext. Provider commitments and good intentions are routinely defeated by operational complexity. We built encryption that does not depend on either.
Encrypted infrastructure
AMD SEV-SNP Confidential VMs with hardware-enforced encryption during inference itself — not just at rest and in transit. A memory dump returns encrypted gibberish.
Multi-model AI
Multiple AI models on every task — not for more, but because each catches what others miss. Our debate-mode review improves leading models by up to 30% over their standalone performance.
Purpose-built models
Scout (weekly-retrained cybersecurity), GxP (pharma domain), Flash + Heavy (architecturally-distinct general models for ensemble diversity). Fine-tuning a new domain takes weeks.
Pharma manufacturing focus
GxP, Annex 11, 21 CFR Part 11, ALCOA+, EU GMP Annex 22 — not compliance bolt-ons. Regulated pharma is the industry we build for; every architectural decision exists because regulated environments require it.
Who we are
9RobotsAI GmbH, Switzerland. We build security-first AI infrastructure and solutions for pharma manufacturing. Our ColdVault Platform, integration monitoring product GlassHood, and AI test execution product TestRobin are live and available. We use GlassHood internally to monitor our own infrastructure — the demo runs on real data from our systems.
We offer two deployment models. Cloud — hardware-encrypted inference via API, elastic scale, immediate deployment. Or sovereign deployment — we bring our own GPU hardware into your server racks and manage everything end-to-end, fully air-gapped. Both options are Annex 22 ready. You can start with a free API key for evaluation — no budget approval needed.
Why we exist
When you send data to an AI model, it is processed in plaintext. Providers commit not to retain or train on it. But operational complexity defeats even the strongest intentions — in March 2026, an AI company deeply committed to security had the full codebase of a $2.5 billion product exposed through a packaging error. If complexity can defeat their own internal security, plaintext processing of production data is a systemic risk.
We built hardware-encrypted inference where your data is encrypted during AI inference itself — not just in transit or at rest. The architecture, not the policy, is the guarantee.
Encrypted infrastructure
The ColdVault Platform runs on AMD SEV-SNP Confidential VMs with hardware-enforced encryption. Data is encrypted during AI inference — not just in storage and transit. A memory dump of our servers and GPUs returns encrypted gibberish. We monitor system health through logs but cannot see the data itself.
For production data that cannot be sent through a standard AI provider — patient records, proprietary formulations, competitive IP — this is as secure as current technology allows. Bring your auditors; we welcome independent verification.
Multi-model AI
We run multiple AI models on every task — not because more is better, but because each model catches what others miss. Our multi-model debate — where models discuss, disagree, and converge — improves the best models by up to 30% compared to their standalone performance.
The performance gap between open models and leading proprietary models is narrow — and some independent benchmarks show it disappearing entirely. Our own ColdVault Benchmark supports the same conclusion.
Purpose-built models
We can fine-tune purpose-built models on your data for your specific tasks. The first iteration of a custom model for any pharma domain can be delivered within weeks once training data is available.
9robots Scout LLM — cybersecurity model, retrained weekly on the latest threats; knowledge is in the weights, not retrieved at inference time. 9robots GxP LLM — pharma domain model for GMP, Annex 11, 21 CFR Part 11, and ALCOA+ workflows. 9robots Flash LLM and 9robots Heavy LLM — two architecturally different general models trained on ensemble outputs, designed to provide complementary perspectives.
Pharma manufacturing focus
GxP, Annex 11, 21 CFR Part 11, ALCOA+, EU GMP Annex 22. These are not compliance checkboxes we added after building a generic AI tool. Pharma manufacturing is the industry we build for. Every architectural decision — from encrypted inference to pinned model weights to controlled underlying infrastructure — exists because regulated environments require it. Annex 22 demands full control over AI in validated workflows. We provide that control across the full stack.
See: What happens if someone tries to compromise the system?
Attestation. Google Cloud verifies that the code running on our servers matches exactly what is in our GitHub repository. Any modification — even by our own team — breaks attestation. The running code is the audited code.
Traceability. Every line of code has full Git history: who introduced it, when, why. Every change goes through multi-reviewer security checks before production deployment. Accountability is personal and permanent.
The honest answer. Could an employee theoretically introduce malicious code? Yes — this is true for every software company in the world. The difference is that our architecture makes such an act detectable, attributable, and prosecutable. Attestation catches unauthorised modifications. Traceability identifies the author. The encrypted boundary means even a compromised insider cannot extract data from a running machine.
Curious fact — our name
In pharma manufacturing, Six Sigma (6σ) is the gold standard for quality — 3.4 defects per million opportunities. But achieving it is harder than it sounds. Real-world processes experience a well-documented 1.5σ shift over time: a process designed for 6σ actually performs at 4.5σ in practice.
We want to make true Six Sigma a reality — not on paper, but sustained in production. To get there, we believe you have to shoot for the stars: our stated target is Nine Sigma (9σ). We know that sustaining 9σ is extraordinarily difficult, even for machines. But by aiming that high, we give ourselves a real chance of achieving and holding true 6σ.
And who can sustain that level of precision? Not humans alone. We believe true Six Sigma can only be achieved through software robots, AI agents, and physical automation — with humans in the loop, but no longer as the base. The shift from "AI-assisted, human-made" to "human-assisted, AI-made" is what makes this possible.
That's 9robots: targeting nine sigma through robots, to make true Six Sigma a reality.
We arrived at the name twice, independently. Once through the quality argument above. And once through a different path — we wanted to bring together all the most capable AI models. In Chinese culture, the number nine (九) signifies completeness and totality — it is the largest single digit, symbolising "everything." Nine robots bringing together everything AI has to offer. The same name, two reasons.