{"passport":{"unfragile":{"@version":"1.0","version":"2026-05","artifact":{"id":"tool_collato","slug":"collato","name":"Collato","type":"product","url":"https://collato.com","page_url":"https://unfragile.ai/collato","categories":["research-search"],"tags":[],"pricing":{"model":"freemium","free":true,"starting_price":null},"status":"active","verified":false},"capabilities":[{"id":"tool_collato__cap_0","uri":"capability://search.retrieval.multi.source.semantic.search.with.unified.indexing","name":"multi-source semantic search with unified indexing","description":"Collato indexes content from disparate sources (Slack, Google Docs, Jira, Linear) into a unified vector embedding space, enabling semantic search that understands intent and context rather than relying on keyword matching. The system maintains separate connectors for each source platform, normalizes heterogeneous data schemas into a common internal representation, and performs similarity-based retrieval across the aggregated index. This approach allows users to query across fragmented information silos with a single natural-language search without migrating data.","intents":["Find relevant product context scattered across Slack conversations, design docs, and Jira tickets with a single semantic query","Discover related discussions and decisions across multiple platforms without manually switching between tools","Retrieve context for a feature request by searching across historical conversations, requirements docs, and implementation notes simultaneously"],"best_for":["Product teams managing information across 3+ platforms (Slack, Docs, Jira, Linear)","Engineering teams needing cross-platform context without consolidating to a single tool","Organizations with distributed decision-making where context lives in multiple sources"],"limitations":["Semantic search quality depends on embedding model capacity and training data; may struggle with highly domain-specific jargon without fine-tuning","Indexing latency for large Slack workspaces or document repositories may introduce 5-30 minute delays in search freshness","No built-in deduplication across sources—same information indexed from multiple platforms may appear as separate results","Context window limitations may truncate long documents or threads before embedding, losing semantic information"],"requires":["Active accounts on at least one supported platform (Slack, Google Workspace, Jira Cloud, Linear)","OAuth/API token permissions for read access to workspace content","Network connectivity to Collato's indexing infrastructure"],"input_types":["natural language queries (text)","structured metadata from source platforms (timestamps, user IDs, channel/project names)"],"output_types":["ranked search results with relevance scores","snippets with source attribution and direct links back to original content","metadata (author, date, platform origin)"],"categories":["search-retrieval","memory-knowledge"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"tool_collato__cap_1","uri":"capability://tool.use.integration.platform.specific.connector.framework.with.oauth.integration","name":"platform-specific connector framework with oauth integration","description":"Collato implements a modular connector architecture where each supported platform (Slack, Google Docs, Jira, Linear) has a dedicated integration module that handles OAuth authentication, API polling/webhooks for content discovery, schema mapping, and incremental sync. Connectors normalize disparate API responses into a common internal data model, manage rate limits and pagination, and handle platform-specific authentication flows. This design allows new source platforms to be added without modifying core search logic.","intents":["Connect Collato to existing Slack workspace without manual data export or complex setup","Automatically sync new Slack messages, Docs updates, and Jira tickets into the searchable index without user intervention","Maintain secure, token-based authentication across multiple platforms without storing plaintext credentials"],"best_for":["Teams using standard SaaS platforms (Slack, Google Workspace, Jira Cloud, Linear) with OAuth support","Organizations with security policies requiring OAuth-based integrations rather than API keys","Product teams wanting plug-and-play integration without custom development"],"limitations":["Connector coverage limited to officially supported platforms; custom or self-hosted tools (Mattermost, Confluence Server) not supported","OAuth token refresh logic may fail silently if user revokes permissions, causing indexing to stall without clear error messaging","Rate limiting from source APIs (e.g., Slack's 1 request/second limit) may cause indexing delays during high-volume syncs","Webhook-based sync depends on platform support; some sources may require polling, introducing latency"],"requires":["OAuth application credentials registered with each source platform","User account with workspace admin or sufficient permissions on source platforms","Network access from Collato infrastructure to source platform APIs"],"input_types":["OAuth authorization codes","workspace/organization identifiers","user credentials for initial authentication"],"output_types":["authenticated API tokens (stored securely)","normalized content objects (messages, documents, issues)","sync status and error logs"],"categories":["tool-use-integration","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"tool_collato__cap_10","uri":"capability://data.processing.analysis.cross.platform.content.deduplication","name":"cross-platform content deduplication","description":"Collato detects and handles duplicate or near-duplicate content that may be indexed from multiple sources (e.g., a Slack message that was also forwarded to a Doc, or a Jira ticket description that was discussed in Slack). The system uses content hashing and similarity detection to identify duplicates and either merges them or marks them as duplicates in search results. This approach prevents users from seeing the same information multiple times in search results.","intents":["Avoid seeing the same information multiple times when it appears in multiple source platforms","Reduce result clutter by consolidating duplicate content from different sources","Understand where information originated and how it propagated across platforms"],"best_for":["Teams with high content overlap across platforms (e.g., Slack discussions that are documented in Docs)","Organizations wanting cleaner search results without duplicate noise","Workflows where understanding information provenance is important"],"limitations":["Deduplication logic may be too aggressive, merging related but distinct content","Detecting near-duplicates requires similarity thresholds that may need tuning per workspace","Deduplication adds indexing overhead; may increase latency by 10-20%","Merged duplicates may lose important context from one of the sources"],"requires":["Content hashing or similarity detection algorithm (e.g., MinHash, Jaccard similarity)","Deduplication rules and thresholds (configurable per workspace)","Duplicate tracking and merging logic"],"input_types":["indexed content items from multiple sources","content hashes or embeddings"],"output_types":["deduplicated content items","duplicate relationship mappings","source attribution for merged items"],"categories":["data-processing-analysis","search-retrieval"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"tool_collato__cap_11","uri":"capability://data.processing.analysis.workspace.analytics.and.search.insights","name":"workspace analytics and search insights","description":"Collato provides analytics on search patterns, popular queries, and information discovery trends within a workspace. The system tracks metrics like most-searched topics, common search intents, result click-through rates, and which source platforms are most frequently accessed through search. These insights help teams understand information gaps, identify frequently-needed context, and optimize their documentation and communication practices.","intents":["Understand which topics or information needs are most common in the team","Identify gaps in documentation by seeing what users frequently search for but don't find","Discover which source platforms contain the most valuable information"],"best_for":["Product and engineering leaders wanting to understand team information needs","Documentation teams optimizing content based on search patterns","Organizations wanting to improve knowledge management practices"],"limitations":["Analytics depend on sufficient search volume; small teams may not have meaningful data","Search patterns may reflect poor search UX rather than actual information needs","Privacy concerns with tracking user search queries; requires clear data handling policies","Analytics are retrospective; don't predict future information needs"],"requires":["Search query logging with user and timestamp information","Click-through and engagement tracking","Analytics aggregation and visualization infrastructure","Privacy compliance for search data retention"],"input_types":["search queries","click-through events","result engagement metrics"],"output_types":["aggregated search metrics (volume, trends)","popular queries and topics","source platform usage statistics","result click-through rates"],"categories":["data-processing-analysis","planning-reasoning"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"tool_collato__cap_2","uri":"capability://automation.workflow.incremental.content.synchronization.with.change.detection","name":"incremental content synchronization with change detection","description":"Collato implements incremental sync logic that detects changes in source platforms (new Slack messages, updated Docs, modified Jira tickets) and updates the search index without re-indexing entire workspaces. The system uses platform-specific change detection mechanisms (Slack's cursor-based pagination, Google Docs' revision history, Jira's updated timestamp filtering) to identify new or modified content, then re-embeds only changed items. This approach reduces indexing overhead and keeps search results fresh without requiring full re-crawls.","intents":["Ensure search results reflect recent Slack discussions and document updates without manual refresh","Reduce indexing costs by only processing changed content rather than re-indexing entire workspaces","Maintain search freshness for time-sensitive information (recent decisions, latest requirements)"],"best_for":["Teams with high-volume Slack workspaces or frequently-updated documentation","Organizations concerned with indexing costs and wanting efficient resource utilization","Product teams needing near-real-time search results for recent discussions"],"limitations":["Change detection latency varies by platform; Slack may have 5-15 minute delays before new messages are indexed","Deleted content may remain in the index if deletion detection is not implemented; requires explicit purge logic","Concurrent edits in Google Docs or Jira may cause race conditions if sync intervals overlap","Incremental sync assumes monotonic timestamps or cursors; clock skew or retroactive edits may be missed"],"requires":["Source platforms supporting change detection APIs (cursor pagination, revision history, timestamp filtering)","Persistent state storage to track last sync timestamp or cursor position per source","Scheduled sync jobs or webhook handlers to trigger incremental updates"],"input_types":["last sync timestamp or cursor position","change detection queries (e.g., 'messages since timestamp')"],"output_types":["list of new or modified content items","updated embeddings for changed items","sync status and next cursor/timestamp"],"categories":["automation-workflow","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"tool_collato__cap_3","uri":"capability://search.retrieval.context.aware.result.ranking.with.relevance.scoring","name":"context-aware result ranking with relevance scoring","description":"Collato ranks search results using a multi-factor relevance model that combines semantic similarity scores (from embedding-based retrieval), metadata signals (recency, author authority, source platform), and user interaction patterns (click-through rates, dwell time). The ranking system weights factors differently based on query type (e.g., recent decisions prioritize recency; technical questions prioritize source authority) and learns from implicit feedback (which results users click on). This approach surfaces the most contextually relevant results rather than purely similarity-based matches.","intents":["Get the most relevant search result first without scrolling through dozens of tangentially-related items","Find recent decisions or discussions that are more relevant than older, similar content","Discover authoritative sources (e.g., results from senior engineers or product leads) when searching for technical context"],"best_for":["Teams with large result sets where ranking quality significantly impacts productivity","Organizations where information authority matters (e.g., official requirements vs. brainstorm discussions)","Product teams wanting search results to improve over time as the system learns usage patterns"],"limitations":["Ranking model requires sufficient user interaction data to learn; cold-start behavior may be suboptimal for new workspaces","Metadata signals (author, date) may introduce bias; e.g., results from senior engineers ranked higher even if less relevant","No transparency into ranking factors; users cannot understand why a particular result ranked first","Learning from click-through rates may reinforce filter bubbles if users consistently click on certain result types"],"requires":["User interaction tracking (clicks, dwell time) with privacy compliance","Metadata enrichment from source platforms (author IDs, timestamps, platform origin)","Machine learning infrastructure to train and serve ranking models"],"input_types":["semantic similarity scores from embeddings","metadata (author, timestamp, source platform)","user interaction events (clicks, dwell time)"],"output_types":["ranked list of results with relevance scores","ranking factor breakdown (optional, for transparency)"],"categories":["search-retrieval","planning-reasoning"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"tool_collato__cap_4","uri":"capability://text.generation.language.natural.language.query.understanding.with.intent.classification","name":"natural language query understanding with intent classification","description":"Collato processes natural language queries through an intent classification layer that identifies the user's underlying goal (find recent decisions, locate technical documentation, discover related discussions, etc.) and adjusts search parameters accordingly. The system may expand queries with synonyms, filter by source platform or date range based on inferred intent, and select appropriate ranking strategies. This approach allows users to search in natural language without learning query syntax or manually specifying filters.","intents":["Search using conversational language ('What did we decide about authentication last week?') without learning query syntax","Automatically filter results by inferred intent (e.g., 'recent decisions' query prioritizes recent content)","Expand queries with domain-specific synonyms (e.g., 'auth' → 'authentication, OAuth, SSO')"],"best_for":["Non-technical users or teams wanting search without learning query languages","Organizations with domain-specific terminology where query expansion is valuable","Teams wanting conversational search experience similar to ChatGPT"],"limitations":["Intent classification accuracy depends on training data; may misclassify ambiguous queries","Query expansion with synonyms may introduce false positives (e.g., 'auth' expanded to 'authorize' matching unrelated content)","No explicit query syntax support; users cannot override inferred intent or filters","Conversational queries may be slower than structured queries due to NLP processing overhead"],"requires":["NLP model for intent classification (likely fine-tuned LLM or classifier)","Domain-specific synonym/expansion dictionary","Query processing pipeline with latency budget (typically <500ms)"],"input_types":["natural language text queries"],"output_types":["classified intent","expanded query terms","inferred filters (date range, source platform, etc.)","adjusted ranking strategy"],"categories":["text-generation-language","planning-reasoning"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"tool_collato__cap_5","uri":"capability://search.retrieval.source.attribution.and.context.linking","name":"source attribution and context linking","description":"Collato preserves and displays source attribution for all search results, including direct links back to the original content in source platforms (Slack message permalink, Google Doc URL, Jira ticket link, Linear issue URL). The system maintains bidirectional mappings between indexed content and source identifiers, allowing users to click through to the original context without leaving their workflow. This design ensures search results are actionable and traceable.","intents":["Click through from search results directly to the original Slack message, Doc, or Jira ticket","Verify context by viewing the full conversation or document around a search result","Share search results with teammates by providing direct links to source content"],"best_for":["Teams needing to verify context or view full discussions around search results","Organizations with compliance or audit requirements for tracing information sources","Workflows where users frequently need to jump from search results back to source platforms"],"limitations":["Links may break if source content is deleted or moved; no link validation or dead-link detection","Permissions are not checked; users may click links to content they don't have access to in source platforms","Snippet context may be truncated or misleading if the original content is very long or has been edited","Source links require users to authenticate with source platforms; not all users may have access"],"requires":["Persistent mapping between indexed content and source identifiers (Slack channel/timestamp, Doc ID, Jira key, etc.)","Source platform API knowledge to construct valid permalinks","User authentication with source platforms to access linked content"],"input_types":["source platform identifiers (channel, message timestamp, document ID, issue key, etc.)"],"output_types":["direct links to source content","source attribution metadata (platform, author, timestamp)","snippets with context"],"categories":["search-retrieval","tool-use-integration"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"tool_collato__cap_6","uri":"capability://safety.moderation.workspace.level.access.control.and.permission.enforcement","name":"workspace-level access control and permission enforcement","description":"Collato enforces workspace-level access control, ensuring users only see search results from content they have permission to access in source platforms. The system queries source platform permission APIs (Slack channel membership, Google Docs sharing settings, Jira project access, Linear team membership) during indexing and search time, filtering results based on the authenticated user's permissions. This approach maintains security boundaries without requiring manual permission configuration in Collato.","intents":["Ensure search results only include content from channels, projects, or documents the user has access to","Prevent accidental exposure of private or restricted information through search results","Maintain security compliance by respecting source platform permission models"],"best_for":["Organizations with strict information security or compliance requirements","Teams with sensitive information in restricted channels or projects","Enterprises using Slack workspaces or Jira instances with granular permission models"],"limitations":["Permission enforcement depends on source platform APIs; if APIs are slow, search latency increases significantly","Permission changes in source platforms may not be reflected immediately in search results (eventual consistency)","No fine-grained permission control within Collato; all-or-nothing access per source platform","Caching permission data may create security gaps if permissions are revoked but cache is not invalidated"],"requires":["Source platform permission APIs (Slack conversations.info, Google Docs permissions, Jira project access, etc.)","User authentication with source platforms to determine permissions","Permission caching strategy with invalidation logic"],"input_types":["authenticated user identity","source platform identifiers (channel, document, project, etc.)"],"output_types":["filtered search results respecting user permissions","permission status per result"],"categories":["safety-moderation","tool-use-integration"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"tool_collato__cap_7","uri":"capability://search.retrieval.snippet.generation.with.context.preservation","name":"snippet generation with context preservation","description":"Collato generates contextual snippets from search results that show the matched content with surrounding context (e.g., a few lines before and after a matched sentence in a Doc, or surrounding messages in a Slack thread). The system uses source-aware snippet extraction logic that respects content structure (paragraph boundaries in Docs, message threading in Slack, issue description vs. comments in Jira) to provide meaningful context without truncating mid-sentence. This approach helps users understand relevance without clicking through to source content.","intents":["Preview search results with enough context to understand relevance without clicking through","See surrounding conversation or discussion around a matched item","Quickly scan multiple results to find the most relevant one"],"best_for":["Teams with large result sets where preview quality significantly impacts usability","Workflows where users need to quickly evaluate result relevance before clicking through","Organizations with long documents or active Slack channels where context is important"],"limitations":["Snippet extraction logic may fail on unstructured content (e.g., code blocks, tables in Docs)","Context window size is fixed; may be insufficient for complex discussions or long documents","Snippet generation adds latency to search results (typically 50-200ms per result)","Formatting is lost in snippets; rich text, images, and links are not preserved"],"requires":["Source-aware content parsing logic (Markdown for Docs, message threading for Slack, etc.)","Context window configuration (number of surrounding lines/messages)","Snippet generation pipeline with acceptable latency"],"input_types":["matched content with position information","source platform type (Slack, Docs, Jira, Linear)"],"output_types":["text snippets with context","match highlighting or position information"],"categories":["search-retrieval","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"tool_collato__cap_8","uri":"capability://memory.knowledge.saved.searches.and.search.history","name":"saved searches and search history","description":"Collato allows users to save frequently-used searches with custom names and retrieve them later, as well as maintains a searchable history of past queries. Saved searches can be shared with teammates, and the system tracks which saved searches are most popular. This feature reduces repetitive typing for common queries and enables knowledge sharing about useful search patterns within teams.","intents":["Save a complex search query for reuse without re-typing","Share useful search patterns with teammates (e.g., 'recent product decisions', 'open bugs in auth module')","Review past searches to find previously-discovered information"],"best_for":["Teams with recurring information needs (weekly status checks, common troubleshooting queries)","Organizations wanting to codify institutional knowledge about useful search patterns","Power users who frequently search for similar information"],"limitations":["Saved searches are static; if source content changes significantly, results may become stale or irrelevant","No versioning or change tracking for saved searches; updates overwrite previous versions","Sharing saved searches requires manual permission management; no automatic access control","Search history may grow unbounded; no automatic cleanup or archival"],"requires":["Persistent storage for saved searches (database table with user ID, query, name, timestamp)","Search history storage with retention policy","Sharing mechanism with permission checks"],"input_types":["search query (natural language or structured)","custom name for saved search","sharing preferences"],"output_types":["saved search ID","search history list","sharing status"],"categories":["memory-knowledge","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"tool_collato__cap_9","uri":"capability://text.generation.language.natural.language.query.to.filter.conversion","name":"natural language query-to-filter conversion","description":"Collato interprets natural language queries to automatically infer and apply filters (date ranges, source platforms, authors, etc.) without requiring explicit filter syntax. For example, a query like 'recent decisions about pricing' automatically filters to recent content and may prioritize Jira decisions or Google Docs. The system uses NLP and heuristics to extract filter parameters from query text and applies them to narrow the search space before semantic retrieval.","intents":["Filter search results by date range using natural language ('last week', 'this month', 'since launch')","Restrict search to specific platforms or content types using conversational language ('in Slack', 'from Jira', 'in docs')","Filter by author or team using natural language ('from the design team', 'by Sarah')"],"best_for":["Teams wanting natural language search without learning filter syntax","Non-technical users or stakeholders who prefer conversational queries","Organizations with complex filtering needs where explicit syntax would be cumbersome"],"limitations":["Filter extraction accuracy depends on NLP model; ambiguous queries may be misinterpreted","No explicit filter syntax fallback; users cannot override inferred filters","Date range extraction may fail on ambiguous expressions ('next quarter', 'fiscal year')","Author/team filtering requires name resolution; may fail if names are ambiguous or misspelled"],"requires":["NLP model for filter extraction (likely fine-tuned LLM or rule-based parser)","Filter parameter mapping (natural language expressions → structured filters)","Name resolution logic for authors and teams"],"input_types":["natural language query text"],"output_types":["extracted filters (date range, source platform, author, etc.)","confidence scores for extracted filters"],"categories":["text-generation-language","planning-reasoning"],"confidence":0.5,"matches":0,"success_rate":0}],"trust":{"score":43,"verified":false,"data_access_risk":"high","permissions":["Active accounts on at least one supported platform (Slack, Google Workspace, Jira Cloud, Linear)","OAuth/API token permissions for read access to workspace content","Network connectivity to Collato's indexing infrastructure","OAuth application credentials registered with each source platform","User account with workspace admin or sufficient permissions on source platforms","Network access from Collato infrastructure to source platform APIs","Content hashing or similarity detection algorithm (e.g., MinHash, Jaccard similarity)","Deduplication rules and thresholds (configurable per workspace)","Duplicate tracking and merging logic","Search query logging with user and timestamp information"],"failure_modes":["Semantic search quality depends on embedding model capacity and training data; may struggle with highly domain-specific jargon without fine-tuning","Indexing latency for large Slack workspaces or document repositories may introduce 5-30 minute delays in search freshness","No built-in deduplication across sources—same information indexed from multiple platforms may appear as separate results","Context window limitations may truncate long documents or threads before embedding, losing semantic information","Connector coverage limited to officially supported platforms; custom or self-hosted tools (Mattermost, Confluence Server) not supported","OAuth token refresh logic may fail silently if user revokes permissions, causing indexing to stall without clear error messaging","Rate limiting from source APIs (e.g., Slack's 1 request/second limit) may cause indexing delays during high-volume syncs","Webhook-based sync depends on platform support; some sources may require polling, introducing latency","Deduplication logic may be too aggressive, merging related but distinct content","Detecting near-duplicates requires similarity thresholds that may need tuning per workspace","builder identity is not verified yet","no observed match outcomes yet"],"rank_breakdown":{"adoption":0.36666666666666664,"quality":0.78,"ecosystem":0.15000000000000002,"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":"2026-05-24T12:16:29.717Z","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=collato","compare_url":"https://unfragile.ai/compare?artifact=collato"}},"signature":"vRNm1mOxbODfF6uJQubhElptFwQIzMVOkPaloGqUnf++jxEbvudWtzYcLAIXhMELGqfALHq5VKs+c8mEW0LPCg==","signedAt":"2026-06-21T09:01:20.533Z","signedBy":"unfragile.ai","version":1},"_links":{"self":"https://unfragile.ai/api/v1/passport/collato","artifact":"https://unfragile.ai/collato","verify":"https://unfragile.ai/api/v1/verify?slug=collato","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"}}