Two problems, one cause: your apps don't share context, and your AI isn't yours to govern. Aurora answers both. Cortex carries context across your apps; Nexus keeps every model under your own roof.
Today's AI has created two critical, interconnected problems that threaten both productivity and security
AI tools are trapped in silos, blind to your broader workflow, forcing manual copy-paste and destroying productivity
Your AI is scattered across volatile single vendors you can't switch off, and you have no view of what data employees send to which model. Shadow AI is the symptom.
These aren't two separate problems. They're symptoms of one foundational flaw: the lack of a unified, secure context layer — and no control plane to route, see, and govern every AI request.
A unified ecosystem that serves both users and enterprises without compromise
The Application
The user-facing co-pilot designed for extreme ease of use. An intelligent, context-aware interface that manages your entire workflow, built on top of Aurora Nexus.
The Engine
The secure, model-agnostic engine that routes every request — local models first, any cloud LLM when needed. Because all AI traffic flows through it, Nexus is also where you see and govern it. Deployable 100% on-premise.
This separation allows each component to be optimized for its primary function without compromise. Enterprises get total security and control, while users get extreme ease-of-use.
Chat, documents, meetings, and AI assistants in a single app, organized the way teams actually work.
Your private AI workspace: Drive, calendar, and mail, with an assistant that learns across everything you do.
Shared workspaces for a team or project: members, shared knowledge, and cross-room visibility.
Focused activity hubs: chat, meetings, and shared docs, with a Room Agent scoped to just that work.
Aurora reads context from the tools your team already uses and packs the right context into every answer, automatically.
"Why did we lose the Henderson deal?"
The innovative technologies that power the Aurora ecosystem
The engine behind Aurora's zero-effort experience: a local, client-side service that understands your entire workflow without manual input.
Create powerful AI agents without writing a single line of code using simple "Trigger → Action" logic.
The orchestration layer at Aurora's core: it routes every request to the right model — local-first, any cloud LLM when needed — eliminating vendor lock-in.
How Aurora handles: "Summarize my Notion notes from the 10am meeting and draft an email to the team"
Security is not a feature. It's the foundation. Every AI request flows through Aurora — so you can route it, encrypt it, and watch it.
Every layer of Aurora is built on battle-tested security standards that enterprises demand.
User-Controlled Intelligent Scrubbing
The AI Data Scrubber provides intelligent scrubbing suggestions with full transparency and user control. Before sending prompts to external LLMs, it analyzes content using pattern matching (for structured data like SSNs and credit cards), Named Entity Recognition (for people and organizations), and custom enterprise dictionaries. You choose your scrubbing level: Light (structured data only), Moderate (balanced), or Aggressive (maximum anonymization). Then review detected entities with confidence scores before sending. Manually add entities the system missed or remove false positives. The pre-send review interface shows exactly what will be scrubbed and the expected quality impact, letting you make informed security-quality trade-offs for each prompt. An encrypted session map enables re-hydration of responses while maintaining your chosen level of anonymization.
You control what gets scrubbed. Best-effort detection with human verification for maximum security.
Application-Level Ingestion Control
The Context Fence provides application-level control over what Aurora Cortex can index. Toggle specific desktop applications on or off with simple allowlist/blocklist rules. Block sensitive apps like Telegram or Signal to prevent those communications from being indexed. For enterprises, IT administrators can define baseline application policies. Important limitations: web applications running in browsers (like Gmail or Google Sheets) are treated as part of the browser process: you can block all of Chrome or none of it. For maximum control when working with sensitive information, use the manual "Pause Indexing" button, which immediately stops all context capture until you resume. This simple approach is the most reliable way to ensure sensitive moments aren't captured.
Simple application blocking plus manual pause button for sensitive moments. On-premise deployment provides complete control.
Client-Side Content Encryption
Aurora uses a hybrid encryption model that balances strong security with practical functionality. All raw content (your documents, emails, and sensitive text) is encrypted client-side using AES-256 with a unique key generated in your device's Secure Enclave (iOS/macOS) or TPM (Windows/Linux). This key never leaves your device. The encrypted content is stored on Aurora Nexus, which cannot decrypt or read it. For semantic search to function, vector embeddings (mathematical representations of content meaning) are stored with limited encryption that enables similarity matching. These embeddings reveal topic categories but not actual content. For organizations requiring maximum security, the on-premise deployment option keeps all data (content, embeddings, and encryption keys) inside your firewall, providing architectural data sovereignty.
Your raw content remains encrypted. For complete data sovereignty, deploy Aurora Nexus on-premise.
Observability & Governance
Because every AI request — local or cloud — passes through Aurora Nexus, you get one place to govern it all. Aurora classifies each request, logs it to an immutable audit trail, and checks it against your policies in real time: detect improper usage and risky data uploads, audit who sent what to which model, and alert your security team the moment a policy is crossed. This is how funneling AI traffic through one layer turns Shadow AI from an invisible risk into a managed one.
One chokepoint for all AI traffic means full visibility and control — without banning the tools people need.
For maximum security, deploy the entire Aurora Platform 100% on-premise in your private cloud. No data ever leaves your firewall.
Your data is never used to train AI models. When you use public LLMs through Aurora, your anonymized queries are sent with opt-out flags to prevent model training.
We don't sell your data. We don't share it with advertisers. We don't give it to partners. Your data stays with you, under your control.
Request complete deletion of your data at any time. We'll purge it from all systems within 30 days and provide cryptographic proof of deletion.
Access complete audit logs of every API call, every data access, and every action taken on your behalf. No hidden processes.
We don't access any client data without your explicit permission. This commitment is legally binding in our Terms of Service. It's not just a promise, it's a contract.
Regular third-party penetration testing and security audits by leading cybersecurity firms. We publish audit summaries and maintain continuous security validation.
Aurora creates a flywheel where each part drives value for the others
Aurora Cortex
Freelancers and consultants live in digital chaos, juggling dozens of apps without a unified way to manage context.
Freemium model builds massive user base beachhead, Pro subscription unlocks premium features
Aurora Nexus
Your AI spend is locked to volatile single vendors, and you can't see what employees send to which model. Shadow AI is the symptom of having no control plane.
High-margin annual revenue per-seat licensing for on-prem platform and enterprise support
The Developer Ecosystem
SaaS companies need to extend AI functionality but building secure, context-aware features takes months.
Transaction fees/revenue share on marketplace + API licensing creates defensible network effect
Each component strengthens the others, creating a self-reinforcing network effect
Build custom agents
Agents shared & sold
Build integrations
Deploy platform
Join the waitlist for Aurora: route every AI request through one layer you control — local-first, any model, fully observable.