AI Processing, EORA and Automated Suggestions Notice
How EORA and external LLMs are routed, token limits, zero data retention, and age restrictions.
This policy draft is tailored to AI Tathastu's verified architecture and engineering practices. Under India's phased legal framework (Digital Personal Data Protection Act, 2023 and DPDP Rules, 2025), substantive obligations commence in scheduled tranches.
Any item labelled with an [Awaiting Verification] tag designates operational data (such as formal legal entity name, designated grievance officer contacts, or vendor contractual tiers) reserved for verified business confirmation. This platform makes no unverified certification claims.
1. Why AI Tathastu uses AI#
AI Tathastu may use language models for opt-in CV interpretation, learner support through EORA, explanations, suggested lessons/roadmaps, skills gap interpretation or other clearly labelled features. Deterministic parsers, ordinary rules and database lookups are not equivalent to sharing data with an external model. Available models and providers may vary by feature, availability and configuration.
2. What might be sent#
Only the minimum authorized context needed for the particular operation: a prompt, part of CV text, reviewed academic/skills context or questions asked to the tutor. Do not include credentials, raw secret keys or unrelated private records in model requests. Never submit someone else's CV or confidential material without the necessary authority.
3. Provider eligibility and routing#
Possible configured vendors include Groq, Cerebras, Google Gemini, OpenRouter, Mistral and SambaNova. These names are not a representation that every provider currently processes data. Multi-provider failover must only use recipients covered by the user's notice/permission and approved age, location, privacy and security criteria. A provider becomes active only after passing the current provider register and contract review.
OpenRouter can forward the prompt to a selected upstream model provider with different retention/training practices. An approved router name alone is not a complete vendor disclosure. Groq's data settings likewise need verification for the actual account and feature. Avoid claims of universal zero retention or zero training.
4. Decision support, not proof#
Model outputs can be incorrect, biased, incomplete, outdated or unsupported. A suggested skill does not count as a verified skill. Recommendations do not guarantee jobs, research outcomes, grades or placements. Consequential academic, eligibility or employment decisions require suitable human review and an opportunity to correct underlying data.
5. Optional AI consent#
When externally hosted AI analysis is optional, it requires a separate affirmative authorization describing scope and recipients. Refusing it should not disable the core deterministic CV parser or unrelated product features. Permission may be withdrawn for future use. Where a model/provider's terms prohibit a use, consent cannot override that prohibition.
6. Learning and coding safety#
Generated lesson text, citations, simulations, code, source summaries and advice should be checked before reliance. Executable code features must use the approved sandbox and safety protections; a chat answer is not professional advice.
7. Data retention and requests#
Our storage, provider logging and model data-use handling differ. The exact retained items and periods must be listed after a vendor and infrastructure review. For questions or opt-out requests contact Awaiting Verification: privacy contact.