GoSearch

Performs AI-powered semantic search by converting natural language queries into vector embeddings and matching them against indexed content from multiple enterprise systems (Slack, Jira, Confluence, SharePoint, etc.). Uses embedding models to understand query intent beyond keyword matching, enabling users to find relevant information even when exact terminology doesn't match indexed documents. The system maintains separate vector indices per data source while providing unified search across all connected systems.

Enables enterprises to create custom GPT-based agents that operate on top of indexed enterprise data without requiring extensive backend engineering. Integrates with OpenAI's GPT models and likely provides a configuration layer to bind custom instructions, system prompts, and knowledge bases to specific GPT instances. The system likely handles prompt engineering, context injection from search results, and response formatting automatically, allowing non-technical domain experts to define agent behavior through UI configuration.

Provides a connector architecture that abstracts authentication, data fetching, and indexing for enterprise systems like Slack, Jira, Confluence, SharePoint, and others. Each connector handles system-specific API pagination, rate limiting, and data normalization to a common schema, allowing GoSearch to treat heterogeneous data sources uniformly. The framework likely includes OAuth/API key management, incremental sync capabilities, and error handling for failed connections.

Replaces traditional keyword-based search with a conversational natural language interface that understands user intent and context. Likely uses intent classification and entity extraction to parse queries, then translates them into semantic search operations and structured database queries. The interface may support follow-up questions and clarifications, maintaining conversation context across multiple turns to refine search results progressively.

Generates natural language responses to user queries by combining search results with LLM-based synthesis, automatically attributing information to source documents. The system likely retrieves relevant documents via semantic search, injects them into an LLM prompt as context, and generates a coherent response that cites specific sources. This prevents hallucination by grounding responses in indexed enterprise data and provides audit trails for compliance.

Maintains synchronized indices across connected enterprise systems by tracking changes and indexing only new or modified content rather than re-indexing everything. Likely uses change detection mechanisms (webhooks, polling, or API timestamps) to identify new documents, deleted content, and updates, then applies incremental updates to vector indices. The system manages sync schedules, handles failures gracefully, and provides visibility into sync status and latency.

Enforces source system permissions so users only see search results they have access to in the original system. Likely caches user permissions from connected systems (Slack channels, Jira project access, Confluence space permissions) and filters search results based on these permissions at query time. The system may use role-based access control (RBAC) or attribute-based access control (ABAC) to determine visibility.

Maintains conversation state across multiple turns, allowing users to ask follow-up questions that reference previous context without re-stating their full intent. The system likely stores conversation history, extracts relevant context from previous turns, and injects it into subsequent queries to maintain coherence. This enables natural dialogue patterns where users can refine searches or ask clarifying questions progressively.

Provides visibility into search usage patterns, popular queries, and content discovery gaps through dashboards and analytics. Likely tracks metrics like query volume, click-through rates, search result relevance, and user engagement patterns. The system may identify frequently searched topics, unused content, and areas where users struggle to find information, enabling organizations to improve knowledge base organization and content creation.