Privacy Policy
Effective Date & Last Updated: August 29, 2026
Information We Collect
To deliver multi-agent academic synthesis, gap identification, and citation grounding, we collect the following categories of information:
A. Academic Queries & Research Inputs
Research queries, problem statements, domain keywords, methodological constraints, and academic intents submitted to the multi-agent search engine.
B. Account & Authentication Data
Your full name, academic email address, password hashes, institutional role (e.g., Researcher, PhD Candidate, Professor, Reviewer), and assigned administrative privileges.
C. Usage Telemetry & Operational Logs
Credit consumption history, search session timestamps, agent pipeline execution records (Agents 1 through 7), latency metrics, error reports, and export activities (e.g., BibTeX, APA, MLA citations, PDF/Markdown reports).
D. Payment & Subscription Details
Transaction identifiers, plan tier status, and purchased credit balances. All direct card details are tokenized and processed by certified, PCI-DSS compliant third-party payment gateways; we never store raw credit card numbers on our servers.
How We Use Your Information
Your research inputs and personal data are strictly utilized for legitimate academic and technical operational purposes:
- Executing multi-agent gap detection and synthesizing scientific papers across open scholarly repositories.
- Generating structured 13-section comprehensive research blueprints and methodological strategies (Agent 7).
- Persisting session history and enabling auto-resume capabilities for complex academic inquiries.
- Managing research credit balances, subscription renewals, and administrative access controls.
Third-Party Academic Providers & AI Models
To aggregate evidence and verify citations, Space Academic interacts via secure, server-side connections with reputable academic databases and foundational models:
Integrated Open-Access Academic Sources:
Semantic Scholar (Allen Institute for AI), OpenAlex, CORE (Open University), arXiv (Cornell University), CrossRef, PubMed (NIH/NCBI), and Europe PMC. Queries dispatched to these sources contain purely scientific keywords and contain no identifying personal information.
AI Inference & Intellectual Protection:
AI operations are executed strictly server-side using the calibrated Gemini 3.7 Flash engine. Your private, proprietary research queries are not used to train public models. We enforce zero-retention policies on ephemeral inference context.
Data Security & Storage Practices
We employ enterprise-grade technical and organizational safeguards:
- Encryption in Transit: All web traffic and API communications are encrypted using modern Transport Layer Security (TLS 1.3/HTTPS).
- Server-Side Secret Isolation: API keys and AI credentials never reach the browser client, preventing credential leaks or unauthorized client-side inspection.
- Role-Based Access Control (RBAC): Granular permissions strictly limit administrative and inspection access according to user roles.
- Local Persistence Security: Session tokens and cached reports stored locally in browser storage can be cleared by the user at any time.
User Rights & Data Control
Regardless of your geographical jurisdiction (including GDPR in the European Union and CCPA in California), you possess the following rights regarding your data:
Contact Our Data Protection Officer
If you have any questions, concerns, or requests regarding this Privacy Policy or your academic data, please reach out to our privacy and support team: