{"passport":{"unfragile":{"@version":"1.0","version":"2026-05","artifact":{"id":"tool_gosearch","slug":"gosearch","name":"GoSearch","type":"product","url":"https://www.gosearch.ai","page_url":"https://unfragile.ai/gosearch","categories":["research-search"],"tags":[],"pricing":{"model":"paid","free":false,"starting_price":null},"status":"active","verified":false},"capabilities":[{"id":"tool_gosearch__cap_0","uri":"capability://search.retrieval.semantic.search.across.enterprise.data.sources","name":"semantic-search-across-enterprise-data-sources","description":"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.","intents":["I need to find documents related to a concept without knowing the exact keywords used","I want to search across multiple disconnected enterprise systems in a single query","I need to locate relevant Slack conversations, Jira tickets, and Confluence pages simultaneously"],"best_for":["Enterprise teams with fragmented knowledge across multiple platforms","Organizations where employees spend significant time searching for information across silos","Mid to large enterprises wanting semantic search without building custom solutions"],"limitations":["Embedding quality depends on underlying model choice — no details on whether using proprietary or third-party embeddings","Latency increases with number of indexed data sources and total document volume","Requires initial indexing pass which may be time-consuming for large enterprises with millions of documents","No information on real-time indexing capabilities — may have indexing lag for newly created content"],"requires":["Active connectors to at least one enterprise data source (Slack, Jira, Confluence, SharePoint, etc.)","API credentials for connected systems with appropriate read permissions","Network connectivity to GoSearch cloud infrastructure"],"input_types":["natural language text query","conversational questions"],"output_types":["ranked list of documents with relevance scores","snippets with highlighted matching content","metadata about source system and document type"],"categories":["search-retrieval","memory-knowledge"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"tool_gosearch__cap_1","uri":"capability://tool.use.integration.custom.gpt.integration.for.domain.specific.agents","name":"custom-gpt-integration-for-domain-specific-agents","description":"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.","intents":["I want to create a domain-specific AI assistant for HR policies without hiring ML engineers","I need to build a customer support bot that answers questions using our internal knowledge base","I want to configure a Jira-aware assistant that helps teams find and create tickets based on natural language requests"],"best_for":["Non-technical domain experts (HR, legal, support) who want to build AI agents","Enterprises wanting to avoid custom LLM application development","Organizations needing multiple specialized agents for different departments"],"limitations":["Likely limited to OpenAI GPT models — no information on support for other LLM providers (Anthropic, open-source models)","Custom agent behavior constrained by GPT's capabilities and safety guidelines","No details on fine-tuning capabilities — agents may not adapt to domain-specific terminology without extensive prompt engineering","Unclear how agents handle multi-turn conversations and state management across sessions"],"requires":["OpenAI API key with GPT access","At least one indexed data source connected to GoSearch","Domain knowledge to define agent instructions and scope"],"input_types":["natural language user queries","conversational multi-turn interactions"],"output_types":["natural language responses","structured actions (create Jira ticket, retrieve document, etc.)","citations to source documents"],"categories":["tool-use-integration","planning-reasoning"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"tool_gosearch__cap_2","uri":"capability://tool.use.integration.multi.system.connector.framework.with.pre.built.integrations","name":"multi-system-connector-framework-with-pre-built-integrations","description":"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.","intents":["I want to index Slack messages, Jira tickets, and Confluence pages without writing custom API integration code","I need to set up data synchronization from multiple enterprise systems with minimal configuration","I want to add a new data source to search without engineering effort"],"best_for":["Enterprises with diverse tech stacks (Slack, Jira, Confluence, SharePoint, etc.)","Teams without dedicated integration engineering resources","Organizations wanting to avoid custom ETL pipeline development"],"limitations":["Limited to pre-built connectors — adding custom data sources likely requires engineering support","Connector coverage unclear — no public list of supported systems beyond Slack, Jira, Confluence, SharePoint","Sync frequency and latency not documented — may have significant lag for real-time data sources","No information on handling of access control — unclear if GoSearch respects per-user permissions from source systems"],"requires":["API credentials for each connected system (OAuth tokens or API keys)","Appropriate permissions in source systems to read data","Network connectivity from GoSearch infrastructure to source systems"],"input_types":["system credentials and authentication tokens","connector configuration parameters"],"output_types":["normalized document schema","indexed content in vector database","sync status and error logs"],"categories":["tool-use-integration","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"tool_gosearch__cap_3","uri":"capability://text.generation.language.natural.language.query.interface.for.enterprise.search","name":"natural-language-query-interface-for-enterprise-search","description":"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.","intents":["I want to ask questions about company policies in plain English instead of using keywords","I need to search for information using conversational language like 'What was discussed about the Q4 roadmap?'","I want the search system to understand what I'm looking for even if I phrase it differently than the original documents"],"best_for":["Non-technical employees unfamiliar with keyword search syntax","Organizations wanting to improve search adoption and user satisfaction","Enterprises with diverse user bases (executives, support staff, engineers) with varying search expertise"],"limitations":["Ambiguous queries may return irrelevant results — no information on disambiguation or clarification mechanisms","Performance degrades with very long or complex multi-part questions","No details on handling of domain-specific jargon or acronyms — may require training or configuration","Unclear how system handles negation and complex boolean logic in natural language"],"requires":["Indexed enterprise data from at least one source","NLP/intent classification model (likely cloud-based)"],"input_types":["natural language text queries","conversational questions","follow-up clarifications"],"output_types":["ranked search results","clarification questions for ambiguous queries","conversational responses with citations"],"categories":["text-generation-language","search-retrieval"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"tool_gosearch__cap_4","uri":"capability://text.generation.language.context.aware.response.generation.with.source.attribution","name":"context-aware-response-generation-with-source-attribution","description":"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.","intents":["I want the AI to answer my question using our internal documents and tell me where the information came from","I need responses that cite specific Confluence pages or Slack conversations so I can verify the information","I want to ensure the AI isn't making up information — it should only use what's in our knowledge base"],"best_for":["Enterprises with compliance requirements (legal, healthcare, finance) needing audit trails","Organizations wanting to reduce hallucination in AI-generated responses","Teams needing to verify AI responses against source documents"],"limitations":["Response quality depends on search result relevance — poor search results lead to poor responses","LLM may still hallucinate or misinterpret source documents despite grounding","No information on handling of conflicting information across multiple sources","Citation accuracy not guaranteed — LLM may cite wrong sources or misquote documents","Latency increases with number of source documents injected into context window"],"requires":["Indexed enterprise data with document metadata","LLM API access (likely OpenAI GPT)","Semantic search capability to retrieve relevant context"],"input_types":["natural language user query","search results with document content"],"output_types":["natural language response","source citations with document links","confidence scores or uncertainty indicators"],"categories":["text-generation-language","memory-knowledge"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"tool_gosearch__cap_5","uri":"capability://data.processing.analysis.incremental.data.indexing.and.sync.management","name":"incremental-data-indexing-and-sync-management","description":"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.","intents":["I want newly created Slack messages and Jira tickets to be searchable within minutes without re-indexing everything","I need to know if data synchronization is working properly and when the last sync occurred","I want to handle deleted documents so they don't appear in search results"],"best_for":["Enterprises with high-velocity data sources (Slack, Jira) requiring near-real-time search","Organizations wanting to minimize indexing costs and latency","Teams needing visibility into data synchronization health"],"limitations":["Sync latency not documented — unclear if new content is indexed within seconds, minutes, or hours","No information on handling of bulk deletes or data purges","Webhook-based sync may miss events if GoSearch infrastructure is temporarily unavailable","Unclear how system handles permission changes — if user loses access to document, is it immediately removed from their search results?","No details on cost implications of frequent syncs versus batch indexing"],"requires":["Connected data sources with change notification capabilities (webhooks or API polling)","Sufficient storage for vector indices","Network connectivity for webhook delivery or polling"],"input_types":["change notifications from source systems","sync configuration parameters","deletion events"],"output_types":["updated vector indices","sync status logs","error reports and retry queues"],"categories":["data-processing-analysis","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"tool_gosearch__cap_6","uri":"capability://safety.moderation.access.control.and.permission.enforcement.in.search","name":"access-control-and-permission-enforcement-in-search","description":"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.","intents":["I want to ensure employees only see search results from Slack channels and Jira projects they have access to","I need to prevent confidential documents from appearing in search results for unauthorized users","I want permission changes in Slack or Jira to immediately affect search visibility"],"best_for":["Enterprises with strict access control requirements (finance, legal, healthcare)","Organizations with sensitive information across multiple systems","Teams needing to comply with data governance and privacy regulations"],"limitations":["Permission enforcement depends on accurate permission caching — stale permissions may allow unauthorized access","No information on permission sync frequency — may have lag between permission changes and search result filtering","Complex permission models (conditional access, time-based permissions) may not be supported","Unclear how system handles cross-system permissions (e.g., user has Slack access but not Jira access)","Performance impact of permission filtering at query time not documented"],"requires":["Connected systems with permission/access control APIs","User identity mapping across systems","Regular permission sync from source systems"],"input_types":["user identity","permission data from source systems","search query"],"output_types":["filtered search results respecting user permissions","permission metadata for audit logging"],"categories":["safety-moderation","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"tool_gosearch__cap_7","uri":"capability://text.generation.language.multi.turn.conversation.management.with.context.retention","name":"multi-turn-conversation-management-with-context-retention","description":"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.","intents":["I want to ask a follow-up question that builds on my previous search without repeating context","I need the AI to remember what I asked before and use that context to answer my next question","I want to have a natural conversation where I can say 'Tell me more about that' and the system understands what 'that' refers to"],"best_for":["Users wanting natural conversational search experiences","Support teams using AI assistants for multi-turn customer interactions","Analysts conducting iterative research requiring progressive refinement"],"limitations":["Conversation context window limited by LLM token limits — very long conversations may lose early context","No information on conversation persistence — unclear if conversations are saved for later retrieval","Context injection may introduce irrelevant information from previous turns, degrading response quality","No details on handling of context conflicts (user asks contradictory questions in same conversation)","Unclear how system handles context across different data sources or time periods"],"requires":["Session management infrastructure to track conversation state","LLM with sufficient context window (likely GPT-4 or similar)","Conversation history storage"],"input_types":["natural language query","conversation history from previous turns"],"output_types":["contextual response","updated conversation state","search results with context from previous turns"],"categories":["text-generation-language","planning-reasoning"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"tool_gosearch__cap_8","uri":"capability://data.processing.analysis.analytics.and.search.insights.dashboard","name":"analytics-and-search-insights-dashboard","description":"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.","intents":["I want to understand which topics employees are searching for most frequently","I need to identify gaps in our knowledge base where users can't find answers","I want to measure the impact of our search implementation on employee productivity"],"best_for":["Knowledge management teams optimizing content and search","Enterprises measuring ROI of search implementations","Organizations wanting data-driven insights into information needs"],"limitations":["Analytics granularity not documented — unclear if tracking individual queries or aggregated patterns","No information on privacy implications of query logging and analytics","Unclear if analytics respect user permissions — may expose sensitive search patterns","No details on historical data retention — may not support long-term trend analysis","Custom analytics or export capabilities not documented"],"requires":["Search usage data collection infrastructure","Analytics database or data warehouse","Dashboard visualization tools"],"input_types":["search queries and interactions","user engagement events","result click-through data"],"output_types":["usage dashboards and reports","trend analysis","content gap identification","search quality metrics"],"categories":["data-processing-analysis","planning-reasoning"],"confidence":0.5,"matches":0,"success_rate":0}],"trust":{"score":42,"verified":false,"data_access_risk":"high","permissions":["Active connectors to at least one enterprise data source (Slack, Jira, Confluence, SharePoint, etc.)","API credentials for connected systems with appropriate read permissions","Network connectivity to GoSearch cloud infrastructure","OpenAI API key with GPT access","At least one indexed data source connected to GoSearch","Domain knowledge to define agent instructions and scope","API credentials for each connected system (OAuth tokens or API keys)","Appropriate permissions in source systems to read data","Network connectivity from GoSearch infrastructure to source systems","Indexed enterprise data from at least one source"],"failure_modes":["Embedding quality depends on underlying model choice — no details on whether using proprietary or third-party embeddings","Latency increases with number of indexed data sources and total document volume","Requires initial indexing pass which may be time-consuming for large enterprises with millions of documents","No information on real-time indexing capabilities — may have indexing lag for newly created content","Likely limited to OpenAI GPT models — no information on support for other LLM providers (Anthropic, open-source models)","Custom agent behavior constrained by GPT's capabilities and safety guidelines","No details on fine-tuning capabilities — agents may not adapt to domain-specific terminology without extensive prompt engineering","Unclear how agents handle multi-turn conversations and state management across sessions","Limited to pre-built connectors — adding custom data sources likely requires engineering support","Connector coverage unclear — no public list of supported systems beyond Slack, Jira, Confluence, SharePoint","builder identity is not verified yet","no observed match outcomes yet"],"rank_breakdown":{"adoption":0.36666666666666664,"quality":0.7300000000000001,"ecosystem":0.2,"match_graph":0.25,"freshness":0.75,"weights":{"adoption":0.25,"quality":0.25,"ecosystem":0.1,"match_graph":0.35,"freshness":0.05}},"observed_outcomes":{"matches":0,"success_rate":0,"avg_confidence":0,"top_intents":[],"last_matched_at":null},"maintenance":{"status":"active","updated_at":"2026-05-24T12:16:30.893Z","last_scraped_at":"2026-04-05T13:23:42.552Z","last_commit":null},"community":{"stars":null,"forks":null,"weekly_downloads":null,"model_downloads":null,"model_likes":null}},"distribution":{"claim_url":"https://unfragile.ai/submit?claim=gosearch","compare_url":"https://unfragile.ai/compare?artifact=gosearch"}},"signature":"1YCcOxyBuUREV5p1uSTYlpuLWfqsWu3IH3q+FobqIR3DmiMp7Qoh0nXZqJ1ofIFoj2wPPoQJV0heGK3QR4I/BA==","signedAt":"2026-06-22T04:07:12.836Z","signedBy":"unfragile.ai","version":1},"_links":{"self":"https://unfragile.ai/api/v1/passport/gosearch","artifact":"https://unfragile.ai/gosearch","verify":"https://unfragile.ai/api/v1/verify?slug=gosearch","publicKey":"https://unfragile.ai/api/v1/trust-passport-public-key","spec":"https://unfragile.ai/trust","schema":"https://unfragile.ai/schema.json","docs":"https://unfragile.ai/docs"}}