Question 1

How can a company govern AI use across business teams and software developers?

Accepted Answer

Waibee provides a centralized governance layer across both business and engineering AI usage. Organizations can manage access and policies while maintaining visibility into users, models, usage, security, and AI spending across the company.

Question 2

Does Waibee maintain an audit trail?

Accepted Answer

Yes. Waibee provides audit trail capabilities to support governance, security, and visibility into AI usage.

Question 3

Can different teams have different AI policies?

Accepted Answer

Policies can be configured for different users and organizational requirements. The exact wording around teams/groups should be confirmed before publishing.

Question 4

Can we see how AI is being used across the company?

Accepted Answer

Yes. Waibee provides centralized visibility into AI usage, including users, models, requests, token consumption, and spending.

Question 5

How can a company compare the cost of different AI models for its workflows?

Accepted Answer

Waibee provides visibility into model usage and the cost of AI requests, making it easier to understand how different models affect overall AI spend. Smart Routing can also route requests to an appropriate model based on the task instead of using the same model for every request.

Question 6

Can we use private or self-hosted LLMs within Waibee?

Accepted Answer

Yes. Waibee is designed to provide a centralized layer for accessing and managing AI models.

Question 7

How many AI models are available in Waibee?

Accepted Answer

Waibee provides access to 100 LLMs through a single platform, giving users the flexibility to choose the right model for each task or use Smart Routing to select one automatically.

Question 8

Can teams switch models without changing tools?

Accepted Answer

Yes. Users can access multiple supported models through Waibee without moving between separate AI products.

Question 9

Why use multiple AI models instead of one?

Accepted Answer

Different models are better suited to different tasks. Waibee provides access to 100 LLMs, allowing users to choose the right model or use Smart Routing to select one automatically.

Question 10

Can users select an AI model manually?

Accepted Answer

Yes. Users can manually select from the AI models available to them instead of using Smart Routing.

Question 11

How does Smart Routing choose a model?

Accepted Answer

Smart Routing analyzes the type, complexity, and context of a request and automatically routes it to a suitable model, balancing the requirements of the task with model capabilities and cost.

Question 12

Can users have different access levels within the same workspace?

Accepted Answer

Yes. Waibee provides a centralized workspace for your organization where users can have different access levels, permissions, and policies based on their roles and needs.

Question 13

Can an employee’s access be changed or revoked centrally?

Accepted Answer

Yes. User access can be centrally managed and revoked when needed.

Question 14

Can administrators see everything employees do in Waibee?

Accepted Answer

Administrators have visibility into platform usage, users, models, access, policies, and spending. Access to employee content depends on the permissions and policies configured for the organization.

Question 15

How is Waibee different from ChatGPT Business or Claude Team?

Accepted Answer

ChatGPT Business and Claude Team give organizations access to models from a single provider. Waibee is a vendor-neutral enterprise AI platform that brings 100 LLMs from multiple providers into one managed environment, with centralized governance, DLP, RBAC, audit trails, Smart Routing, and FinOps.
Instead of managing separate AI subscriptions and tools, companies can manage model access, security, usage, and AI spending through one platform.

Question 16

Can business teams use Waibee for document analysis, summaries and content drafting?

Accepted Answer

Yes. Waibee Chat provides a single AI workspace where business teams can analyze documents, create summaries, draft content, prepare reports, and work with AI across everyday business tasks.

Question 17

How can product teams use AI to analyze customer feedback and prepare requirements?

Accepted Answer

Product teams can use Waibee Chat to analyze and summarize customer feedback, identify recurring themes, support research, and prepare product requirements and PRDs.

Question 18

Which teams can use Waibee?

Accepted Answer

Waibee can be used by Engineering, Management, Product, Marketing, and Sales teams. Different roles can use AI for their own workflows while the organization maintains centralized control.

Question 19

What is the difference between Waibee Chat and Waibee Code?

Accepted Answer

Waibee Chat is an AI workspace for business teams, supporting tasks such as analysis, content creation, research, reports, and everyday knowledge work. Waibee Code is built for engineering teams, with a CLI, SWE Agents, and ACP for software development, refactoring, testing, debugging, and working with existing codebases.

Question 20

Can teams save and reuse prompts, skills and workflows in Waibee?

Accepted Answer

Teams can reuse skills for repeatable tasks and workflows.

Question 21

What is the difference between an AI gateway, an AI workspace and an enterprise AI platform?

Accepted Answer

An AI gateway provides a centralized way to access and route requests across AI models. An AI workspace gives users an environment where they can work with AI in their day-to-day tasks. An enterprise AI platform brings these capabilities together with governance, security, cost management, integrations, and organizational controls. Waibee combines the gateway and workspace layers within one managed enterprise platform.

Question 22

Can teams extend Waibee with custom skills and plugins?

Accepted Answer

Waibee can be extended with skills and plugins to support company-specific tasks and workflows. This allows organizations to go beyond general-purpose AI and tailor how Waibee works with their processes and tools.

Question 23

What should a company look for in an enterprise AI platform?

Accepted Answer

An enterprise AI platform should provide more than access to AI models. Companies should look for centralized access to multiple models, security and governance controls, visibility into usage and costs, support for both business and engineering workflows, and the ability to integrate AI with existing tools and processes.

Question 24

Can we manage business and engineering AI usage from one platform?

Accepted Answer

Yes. Waibee brings business and engineering AI workflows into one platform. Business teams can work through Waibee Chat, while engineering teams use Waibee Code, with centralized governance, security, and visibility.

Question 25

Who is Waibee built for?

Accepted Answer

Waibee is built for organizations that want to use AI across multiple teams while managing models, security, governance, usage, and costs from one platform.

Question 26

Can organizations set AI budgets for teams or users?

Accepted Answer

Yes. Organizations can set AI budgets and spending limits for users and different parts of the organization. These controls are currently configured through Waibee policies, with self-service budget management for administrators planned for a future release.

Question 27

What tools does Waibee integrate with?

Accepted Answer

Waibee can connect to external tools and data sources through MCP (Model Context Protocol), a standard way for AI to securely access and work with external systems. This allows companies to connect their existing tools to Waibee without building a separate integration for every AI model.

Question 28

Are all integrations available now?

Accepted Answer

Not all integrations are available out of the box today. Waibee’s MCP Gateway is already available, while additional ready-to-use connectors are being added over time. This allows organizations to start connecting external tools through MCP now, without waiting for every connector to become a native integration.

Question 29

How does the Waibee team help with AI adoption?

Accepted Answer

We provide hands-on support for enterprise teams adopting AI. We map your current processes, work with you to find specific use cases where AI can drive high ROI, and build solutions tailored to them.

Question 30

How can organizations measure AI adoption with Waibee?

Accepted Answer

Waibee provides centralized usage analytics across teams and users, so you can see how many people with access actively use AI and compare adoption across teams

Question 31

What does successful AI adoption look like?

Accepted Answer

Successful adoption is not just a growing number of users, requests, or tokens. It means employees consistently apply AI to relevant workflows and the organization can identify measurable improvements in speed, quality, cost, or capacity.

Question 32

Which development workflows does Waibee Code support?

Accepted Answer

Waibee Code supports code generation, codebase analysis, refactoring, debugging, testing, code review, and technical documentation across the software development lifecycle.

Question 33

Can Waibee Code work with an existing codebase?

Accepted Answer

Yes. Waibee Code can analyze an existing codebase, explain its architecture and dependencies, and use that context when generating or modifying code.

Question 34

Can Waibee Code generate tests and documentation?

Accepted Answer

Yes. It can help generate and run tests, document code, explain technical decisions, and prepare materials that support maintenance and developer onboarding.

Question 35

Can Waibee Code be configured for our team’s development workflows?

Accepted Answer

Waibee Code supports different engineering workflows through its CLI, SWE Agents, and ACP. It can be used for tasks such as codebase analysis, refactoring, testing, debugging, documentation, and other multi-step development tasks.

Question 36

How should an engineering team evaluate an AI coding assistant for an existing codebase?

Accepted Answer

Teams should consider whether the AI can understand an existing codebase and its context, support workflows such as refactoring, testing and debugging, and fit into the tools developers already use. Waibee Code is designed to work with existing codebases through its CLI, and AI agents.

Question 37

How does Waibee prevent sensitive data from being sent to AI models?

Accepted Answer

Waibee uses DLP to scan requests before they are sent to AI models. Sensitive information such as PII, payment data, API keys, and confidential code can be detected and handled according to configured policies.

Question 38

What is the difference between blocking, warning and masking in DLP for AI?

Accepted Answer

These controls determine what happens when Waibee detects sensitive information before a request reaches an AI model. Block prevents the request from being sent, Alert warns or flags the detected data, and Mask replaces sensitive information before the request is sent to the model.

Question 39

Can we control who has access to specific AI models and features?

Accepted Answer

Yes. Access to AI models and features can be controlled through role-based access control (RBAC) and policies. At the moment, model restrictions are configured through policies rather than directly by customer administrators in self-service admin controls.

Question 40

How does Waibee reduce shadow AI risks?

Accepted Answer

Waibee gives employees a governed environment for accessing AI models while giving the organization centralized visibility, security controls, and policies. This reduces the need for employees to use unmanaged AI tools and accounts.

Question 41

Can Waibee protect confidential code and business data?

Accepted Answer

Yes. Waibee combines DLP, RBAC, policies, and audit capabilities to help protect confidential code and business data when employees work with AI.

Question 42

How does Waibee help protect personal data when using AI?

Accepted Answer

Waibee uses DLP to detect sensitive information before a request is sent to an AI model. Depending on the configured policy, sensitive data can be blocked, masked, or flagged, helping prevent personal information from being unintentionally shared with AI providers.

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