Mindbreeze InSpire

Mindbreeze integrates Conversational AI chat with LLM-powered assistants that operate directly over indexed search results. It provides:

• Trending searches and contextual query suggestions to guide exploration.
• Personalized ranking by automatically learning user behavior and applying a dynamic relevance model.
• Indirect querying via graph traversal (e.g., expert identification through transitive graph closure).
• Uniform and Hybrid search (Integrates all kinds – lexical, dense retrieval, in-memory filtering into uniform model)
• Summarized results for rapid insight extraction without scanning entire documents.
• High performant and expressive authorization via graph structures (referenced ACLs and ahead of time computation)

Mindbreeze is designed for rich, multi-dimensional data navigation with:

• Faceted filtering and dynamic relevance tuning for fine-grained control.
• Semantic clustering to group related results.
• Visual diffing to detect and highlight changes across similar content.
• Timeline-based result presentation and carousel-based navigation for temporal or visual browsing.
• Geocoding with map overlays for location-aware datasets.
• Infinite scrolling or traditional pagination for large result sets.
• Full content preview rendering to maintain original document formatting.

Mindbreeze supports enterprise-grade interoperability:

• Document collection, sharing, and query-based export for large-scale result distribution.
• RESTful OpenAPI access for system-to-system integration and automation.
• Accessibility compliance (ARIA, screen reader support, keyboard navigation) for inclusive usage.
• Speech I/O support, enabling voice-driven queries and audio responses.

Our telemetry integration provides over 1,000 real-time metrics out of the box, covering:

• User interaction metrics (query logs, refinements, click-through rates, time spent per step) for feedback learning and auto-tuning.
• RAG (Retrieval-Augmented Generation) metrics such as correctness, faithfulness, context recall, and context precision to evaluate LLM-driven workflows.
• Custom RAG metrics via sub-agents or tool calling for domain-specific performance assessment.
  All metrics can be queried, diagnosed, and analyzed through our evaluation system for precise tuning and optimization.

We leverage usage analytics, sentiment detection, and intent recognition to optimize search relevance and ranking models. Analytics can:

• Detect content gaps, track conversion triggers (e.g., case deflection rates), and measure user engagement trends over time.
• Provide actionable business impact metrics to support cost-reduction initiatives.
• Feed directly into AI-based ranking models for improved personalization.
  Administrators have access to dashboards and APIs for reporting, system tuning, and integration with external BI platforms.