Thint AI & Experience Intelligence Policy

1. Purpose and Scope

Thint Research Private Limited ("THINT", "we", "us", or "our") operates Thint Sense, a unified Experience Intelligence Platform. Our mission is to empower organizations with advanced capabilities such as predictive analytics, automated thematic extraction, and governance intelligence while strictly adhering to ethical AI principles.

This AI & Experience Intelligence Policy defines our responsible approach to how we use, train, and deploy Artificial Intelligence (AI) and Machine Learning (ML) models. It ensures our commitment to human oversight and continuous innovation never compromises enterprise customer confidentiality or data ownership.

2. Our Intelligence Framework

The Thint Sense Platform categorizes its AI-driven capabilities into three core intelligence pillars:

1. Experience Intelligence: Using natural language processing to extract themes, sentiment, and actionable insights from raw customer feedback across multiple channels.
2. Governance Intelligence: Utilizing predictive routing and historical metadata to forecast case escalation probabilities and potential SLA breaches.
3. Benchmark Intelligence: Generating cross-industry baseline scoring models that allow customers to evaluate their performance against anonymous aggregate standards.

3. Responsible & Ethical AI Principles

THINT is committed to deploying AI that builds trust rather than acting as a "black box." Our systems operate under the following ethical principles:

• Human-in-the-Loop (HITL) Decision Making: AI is designed to augment human intelligence, not replace it. Thint Sense drafts insights and response recommendations, but empowers the Customer with full control and oversight over final actions and communications.
• Explainability: We strive to make our Platform-Generated Intelligence transparent. When the Platform highlights a pattern or routes a case, users can trace the insight back to its originating thematic drivers.
• AI Limitations: AI models are probabilistic by nature. We acknowledge that natural language processing is not infallible and continually calibrate our models to minimize bias and misinterpretation.

4. The "Privacy-Preserving AI" Guarantee

We strictly adhere to a Privacy-Preserving Intelligence Architecture, guaranteeing that:

1. No Raw PII Training: Raw, identifiable Customer Data (including personally identifiable information and raw survey text containing identifiers) is never used to train, fine-tune, or develop the foundational weights of shared Large Language Models (LLMs).
2. Strict Tenant Isolation: No Customer's proprietary raw data or specific configuration logic will ever be exposed to, or used to generate outputs for, another THINT customer.
3. Zero-Retention Subprocessing: When utilizing external LLM providers (e.g., OpenAI, Google Gemini) for real-time processing tasks, we operate strictly under Zero-Data-Retention agreements. These providers cannot store our API payloads or use them to train their external models.

5. Continuous Platform Improvement

To refine the Platform—including our semantic embeddings, classification heuristics, and predictive models—we rely exclusively on Irreversibly Anonymized and Aggregated Derivatives.

Data is used for internal model improvement only when it has been mathematically decoupled from its source, stripped of all identifiers, scrubbed of Customer branding, and combined with a multitude of aggregated datasets. THINT retains exclusive ownership of these anonymized statistical derivatives and uses them to continuously improve topic extraction, predictive accuracy, and baseline intelligence models without infringing upon customer privacy.