Collato

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.