How to Build AI Search and Insights Across Google Drive, Slack, Zendesk, and Notion
March 10, 2026
Many B2B SaaS products are adding AI search features that allow their customers to ask questions about their company's internal knowledge and operational data. Instead of manually searching documents, support tickets, or conversations, users can ask natural language questions and receive answers grounded in their organization's data.
For example, a support platform might allow teams to search past Zendesk tickets, Slack discussions, and internal documentation to resolve issues faster. A productivity platform might let employees ask questions about company policies stored in Notion or documents in Google Drive.
The challenge is that enterprise knowledge lives across many systems. Documents are stored in Google Drive, conversations occur in Slack, support issues are logged in Zendesk, and knowledge bases are managed in tools like Notion. Each platform exposes different APIs, schemas, and authentication models.
Unified simplifies this by providing normalized APIs across file storage, messaging, knowledge management, and ticketing platforms. Developers can retrieve documents, conversations, and support records through consistent objects and endpoints, enabling AI search features without maintaining dozens of integrations.
Why SaaS Products Build AI Search Features
AI search is becoming a core capability in many SaaS products.
Examples include:
- AI knowledge assistants
- Enterprise search platforms
- Customer support copilots
- Internal productivity tools
- AI-powered help desks
These products allow customers to search across their internal tools using natural language.
Typical capabilities include:
- Document search across company knowledge bases
- Conversation retrieval from chat platforms like Slack
- Support ticket search across help desk systems
- AI-powered question answering using enterprise data
- Knowledge discovery across files and internal documentation
By integrating with customer systems, SaaS products can build unified search experiences across multiple sources of knowledge.
Unified Categories Used for AI Search
An AI search feature typically combines data from several Unified API categories.
| Category | Description | Key Objects |
|---|---|---|
| File Storage | Documents and files from platforms like Google Drive | StorageFile |
| Messaging | Conversations from platforms like Slack | message, channel |
| Knowledge Management | Knowledge bases such as Notion or Confluence | page, space, comment |
| Ticketing | Support tickets from platforms like Zendesk | ticket |
Using normalized APIs across these categories allows developers to build search features that span multiple enterprise systems.
Unified File Storage Objects
Unified's [File Storage API](/storage) normalizes files and folders across storage providers.
The primary object is StorageFile, which represents both files and directories.
Key fields include:
| Field | Description | AI Search Usage |
|---|---|---|
| id | Unique identifier for the file | Internal reference for indexing |
| name | File name | Used as document title in search |
| description | Optional description | Additional searchable metadata |
| mime_type | File format | Determines how content is processed |
| size | File size in bytes | Used for processing limits |
| parent_id | Parent folder ID | Enables hierarchical navigation |
| permissions | Access control metadata | Used for permission-aware search |
| created_at | File creation timestamp | Useful for timeline filtering |
| updated_at | Last modification timestamp | Used for incremental indexing |
| version | Provider revision identifier | Detects content changes |
| download_url | Temporary URL to retrieve file contents | Used to fetch document text |
| web_url | Provider link to the file | Allows users to open the original file |
Files are retrieved through the storage endpoints:
GET /storage/{connection_id}/file
GET /storage/{connection_id}/file/{id}
To retrieve file content, applications call the file endpoint and then download the content using the download_url field.
Unified Knowledge Management Objects
Unified's Knowledge Management API normalizes knowledge base content across platforms like Notion and Confluence.
The API exposes three primary objects:
| Object | Purpose |
|---|---|
| page | Represents a knowledge article or document |
| space | Represents a workspace or knowledge collection |
| comment | Represents discussions attached to pages |
Page Object
Pages contain the primary knowledge content.
Key fields include:
| Field | Description |
|---|---|
| title | Page title |
| download_url | URL used to retrieve page content |
| web_url | Link to the page in the source platform |
| created_at | Creation timestamp |
| updated_at | Last modification timestamp |
| user_id | Author or editor |
| space_id | Parent knowledge space |
| type | Page type |
Pages can be retrieved using:
GET /kms/{connection_id}/page
GET /kms/{connection_id}/page/{id}
Because page bodies are not always returned inline, applications typically retrieve the content through the download_url.
Comment Object
Comments represent discussions attached to knowledge articles.
Important fields include:
contentcontent_typecreated_atupdated_atuser_idpage_id
These fields allow AI search systems to incorporate discussions into knowledge retrieval.
Unified Messaging Objects
Unified's Messaging API normalizes conversations across messaging platforms such as Slack.
Key messaging objects include:
| Object | Purpose |
|---|---|
| message | Individual email or chat message |
| channel | Messaging channel or conversation group |
Messages contain important search fields such as:
- message body text
- sender (
author_member) - recipients (
destination_members,mentioned_members) - timestamps (
created_at) - thread relationships (
parent_id) - attachments and links
Messages are retrieved using:
GET /messaging/{connection_id}/message
GET /messaging/{connection_id}/channel
Threads can be reconstructed by retrieving messages that share the same parent identifier.
Unified Ticketing Objects
Support systems like Zendesk store customer issues as tickets.
The normalized ticket object includes fields such as:
- subject
- description
- status
- priority
- requester
- assignee
- created_at
- updated_at
These fields allow AI systems to retrieve past support cases and provide context when answering questions.
Connecting Customer Integrations
Customers authorize their platforms through Unified Connect.
Typical process:
- Your application launches the Unified Connect authorization flow.
- The user selects integrations such as Google Drive, Slack, Zendesk, or Notion.
- The user authorizes the integration.
- Unified redirects back to your application with a connection_id.
Your application stores the mapping:
user_id → connection_id
All subsequent Unified API requests use this identifier to retrieve data for that customer.
Building an AI Search Pipeline
Most AI search systems use a retrieval-augmented generation (RAG) architecture.
A typical implementation includes the following steps.
1. Connect data sources
Customers connect their tools through Unified Connect.
2. Retrieve documents and conversations
Use Unified APIs to retrieve files, knowledge articles, messages, and support tickets.
3. Extract content
Download document bodies using download_url or retrieve message and comment text from API responses.
4. Chunk content
Split large documents or conversations into smaller segments suitable for embedding.
5. Generate embeddings
Use an embedding model to convert text segments into vector representations.
6. Store vectors
Persist embeddings in a vector database such as Pinecone or pgvector.
7. Retrieve relevant context
When a user asks a question, embed the query and retrieve the most relevant segments from the vector database.
8. Generate an AI answer
Pass retrieved context to a language model to generate a response grounded in the customer's data.
This approach allows AI systems to answer questions using enterprise knowledge across multiple systems.
Keeping the Search Index Updated
Enterprise data changes frequently, so search indexes must remain synchronized.
Unified provides webhook events that notify applications when records change.
Examples include:
- file updates in storage systems
- new Slack messages or channel changes
- knowledge base updates
- new or updated support tickets
Applications can subscribe to these webhook events to trigger re-indexing of updated documents.
The updated_at field can also be used to detect changed content and refresh embeddings.
Supported Platforms
Unified supports a broad ecosystem of integrations across knowledge sources.
Examples include:
File Storage
- Google Drive
- Dropbox
- Box
Messaging
- Slack
- Gmail
- Microsoft Teams
Knowledge Management
- Notion
- Confluence
- Guru
Ticketing
- Zendesk
- Jira Service Management
- Freshdesk
This allows SaaS products to support AI search across a wide range of enterprise tools.
Why Product Teams Choose Unified
Building AI search across enterprise tools normally requires integrating with many APIs.
Unified simplifies this by providing:
- normalized APIs across file storage, messaging, knowledge management, and ticketing platforms
- consistent objects for documents, messages, and knowledge content
- unified authentication through Unified Connect
- webhook events for real-time data updates
Instead of maintaining dozens of integrations, product teams can focus on building AI features that help customers discover and understand their data.
Start building AI search across Google Drive, Slack, Zendesk, Notion, and many other platforms today.