Komo

Processes natural language queries through an LLM-powered search pipeline that interprets user intent, retrieves relevant web results, and synthesizes answers in conversational format. Unlike traditional keyword-based search, it understands semantic meaning and context, returning synthesized answers rather than ranked links. The system likely uses query understanding, web crawling/indexing, and LLM-based result synthesis to generate coherent responses.

Maintains a searchable index of web content that can be queried in real-time to retrieve relevant documents and passages. The system crawls and indexes web pages, likely using distributed crawling and inverted indexing techniques, enabling fast retrieval of relevant content for query processing. This differs from static indexes by supporting fresh content discovery and dynamic ranking based on query relevance.

Analyzes natural language queries to extract semantic intent, entities, and relationships, then matches them against indexed content using vector embeddings or semantic similarity rather than keyword matching. This capability enables the system to understand that 'best restaurants near me' and 'where should I eat tonight' are semantically equivalent queries. The implementation likely uses transformer-based NLP models for intent classification and embedding-based retrieval.

Aggregates information from multiple web sources, identifies consistent facts and conflicting claims, and synthesizes a coherent answer while maintaining source attribution. The system likely uses cross-reference validation, source credibility scoring, and LLM-based synthesis to produce answers that acknowledge different perspectives or conflicting information. This differs from simple aggregation by performing semantic deduplication and conflict resolution.

Maintains conversation history and context across multiple turns, enabling follow-up questions that reference previous answers without requiring full re-specification. The system tracks entities, topics, and implicit context from prior exchanges, allowing queries like 'tell me more about that' or 'what about the second option' to be resolved without ambiguity. Implementation likely uses session-based state management and context injection into subsequent queries.

Explicitly links synthesized answer content back to original sources with inline citations, allowing users to verify claims and explore source material. The system tracks which source contributed which fact or claim, maintaining attribution through the synthesis process. This differs from opaque synthesis by making the source-to-answer mapping transparent and verifiable.

Adjusts search result ranking and filtering based on user preferences, location, search history, and implicit signals (time of day, device type, etc.). The system likely maintains user profiles or session-based preference models that influence which results are surfaced and in what order. This enables location-aware results, time-sensitive filtering, and preference-based ranking without explicit user configuration.