FAQ

By Yoav Crombie
4 min

Functional FAQ

How are permissions managed?

When a user manually uploads a document/transcript or website, they only have permission to view and ask questions about that content.

When integrating systems like SharePoint, Teams chat, and Google Drive, BusinessGPT syncs the permission from the source system so only users who have access to the source system can ask questions or get answers from that content.

If permissions are changed in the source system, BusinessGPT syncs them back to the dashboard.

Company Chat and Assistant knowledge chats provide answers based on all the documents a user can access, not just the ones they’ve uploaded. Uploaded documents are private by default and only accessible to the uploader, unless they add permissions to others. 

Permissions set in the Assistant Knowledge Chat page determine which users can see the assistant Knowledge chat in the Lite Mode page. However, they do not control access to the content itself and certainly can't add permissions beyond the source permissions. To receive an answer, a user must have both permission to view the assistant and permission to access the relevant content.

The screenshot below shows the error message that a user will see when he has access to assistant chat but not to the content of the assistant chat. 

image-20250427-143025.png

 

At which point are permissions applied

Every user can only see items he has access to and, therefore, can only ask questions on content he has access to.

When asking a question about all company data, the system will find the most relevant documents that the user can access, and only these documents will be sent to the AI to generate the answer.

How does the AI Firewall detect bias, and is there a specific mechanism to support ethical usage?

We don’t detect Bias. Instead we give the company the tools to manage the risks of Hallucinations  and Bias by enforcing policies measuring the risks of different use cases. This allows the company to decide for which use case they are OK with potential Bias/ Hallucinations   issues.
In addition- when asking on your company data – you reduce / remove Bias because the answers are based on your data and not pretrained data.

 

Technical FAQ

  • Where can Business GPT be hosted?

    • You can deploy it in your own AWS tenant or on-prem.

  • Where is our data stored?

    • In a database on your servers. It never gets sent to other servers.

  • How do you provide answers to questions without sending our data to the cloud?

  • What hardware does the product require?

    • It requires one Windows server and two Linux servers as well as at least one GPU with a minimum of 24 GB of VRAM. More details here.

  • Do you train LLM models with customer data?

    • No, we store customer data in segregated vector databases and use the RAG methodology to provide answer generation.

  • Which components use the SQL server?

    • The Dashboard, Ingestor and Gateway

  • Is the Ingestor service developed by AGAT?

    • Yes.

  • Scalability of Data Processing

    • What is the maximum database size the tool can efficiently process?

      • The AI only requires the database schema within its context window. For models with large context windows (e.g., GPT-4o with 128K tokens), it can handle up to around 100 tables. The number of rows isn't a limiting factor, as the AI interacts with the database using standard SQL queries. With proper indexing, large datasets can be efficiently queried

    • Have experiments been conducted with datasets of 1GB, 10GB, or 100GB? If so, what were the performance results?

      • Yes. It is important to note that performance is not related to the DB size, as the AI engine translates the natural language prompt into SQL.

  • Handling and Processing New Data

    • How does the tool handle newly added data?

      • BGTP periodically scans data sources like SharePoint and Confluence and re-ingests items that have been modified as well as new items.

      • The Data Analysis tool always performs queries on the current data in the database.

    • What is the typical processing time for newly ingested data to be available for analysis, search, and other AI-driven tasks?

      • Individual new documents are available in seconds.

      • A small environment with 2 small GPUs can process 100 average documents in 25 mins.

  • Database Compatibility and Requirements

    • What types of databases can the tool connect to (e.g., relational, NoSQL, data lakes)?

      • Currently, we support only Transact-SQL databases such as SQL Server.

    • Are there specific requirements or constraints for database integration?

      • The only requirement is a connection string with read-only access to the relevant tables.

  • AI Agent Framework and Customization

    • Does the tool use an AI agent framework? If so, which one?

    • Can users create and customize their own AI agents? If so, how can they be connected to process data?

      • Yes, it's possible to integrate tools with the AI agent, such as custom APIs. This is done by passing predefined Python functions to the agent. The agent can then call these functions when appropriate. For example, you can define a function that sends an email and instruct the agent to use it when needed.

  • Integration with Data Pipelines

    • What integration options are available for incorporating the tool into existing data processing pipelines?

    • Are there APIs, SDKs, or other integration methods available?

      This question is unclear—could you clarify what kind of integration you're referring to?

  • RBAC

    • Which LDAP directories are supported?

      • We work with Microsoft Active Directory using the LDAP protocol, either directly or via the Global Catalog (GC).

 

 

 

 

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