Hoopstr.ai (Formerly Vaave), uses CometChat to power communication across its alumni platform and explore an AI-driven interface that helps alumni find people, services, and information through natural-language conversations.
Hoopstr is an alumni engagement platform that helps educational institutions build and activate their alumni networks. Its platform gives institutions tools for alumni directories, networking, mentoring, events, jobs, fundraising, and communication. Today, Hoopstr serves more than 1,200 institutions and connects millions of alumni.
For Hoopstr, communication is central to the alumni experience. Alumni need to find people, start conversations, participate in groups, and access services from their institutions. The team was already using CometChat to power these interactions and began exploring how AI could make those experiences more intuitive.
Moving beyond a traditional alumni directory
Hoopstr’s existing platform already allowed alumni to search for members, view profiles, and start one-to-one or group conversations.
But the team saw an opportunity to make those interactions more natural.
Instead of asking an alumnus to navigate through filters and directories, Hoopstr wanted them to be able to simply ask for what they needed.
For example:
Who are my classmates currently working in California?
Which alumni are working at a particular company?
Can you help me find someone from my batch in Hyderabad?
How do I access my transcripts?
How do I book a service through my institution?
How can I become a mentor?
The vision was to create a single conversational interface that could search the alumni network, retrieve information from institutional systems, and direct users to the right service or person.
Turning the alumni network into a conversational interface
Hoopstr explored CometChat's AI agent capabilities as a layer between the user and its existing data and APIs.
The idea was straightforward: an alumnus interacts with an AI agent through chat, and the agent determines what needs to happen based on the request.
For a member search, for example, the agent could interpret a natural-language query, construct the appropriate search parameters, call Hoopstr’s APIs, and return the relevant alumni.
The same approach could extend to institutional services and knowledge.
Rather than forcing users to navigate through separate sections of the platform, the conversational interface could become a single entry point for finding people, accessing information, and completing common tasks.
Keeping people and conversations connected
The AI experience also needed to work with Hoopstr’s existing communication layer.
Once the platform identifies an alumnus through a search, the next step may be a conversation with that person. Hoopstr wanted the user to be able to move from discovery to communication without leaving the experience.
That could mean showing a chat action alongside a member's profile or allowing the agent to initiate a one-to-one conversation after identifying the right person.
For group conversations, the team was also exploring how an AI agent could participate alongside alumni, helping members search for people or information within the context of a group.
This creates a more connected flow:
Ask → discover → connect → converse.
Connecting AI to Hoopstr’s existing data
The use cases required more than a static knowledge base.
Hoopstr’s alumni information lives in its own databases and systems, so the agent would need to retrieve information dynamically through APIs or an MCP server rather than relying solely on documents.
CometChat provides the conversational layer while allowing Hoopstr’s backend systems to remain the source of truth.
This gives Hoopstr the flexibility to connect the agent to different tools and data sources while keeping its existing platform and APIs at the center of the experience.
Building on the chat infrastructure already in place
Hoopstr had already implemented CometChat's SDKs in its mobile application, giving the team an existing foundation for one-to-one and group communication.
The AI layer could build on that foundation rather than requiring Hoopstr to create a separate messaging infrastructure.
CometChat handles the underlying communication infrastructure, including WebSockets and message history, while APIs and webhooks allow Hoopstr to connect its own backend systems and extend the experience.
The approach also leaves room for Hoopstr to support the experience across mobile and web as its requirements evolve.
The result
Hoopstr is using CometChat as the communication foundation for a more conversational alumni experience, connecting its existing chat capabilities with AI-powered search and service discovery.
Natural-language alumni search:
Users can describe the person or connection they are looking for instead of navigating multiple filters.
Search-to-chat workflows
Alumni discovered through the platform can be connected through one-to-one conversations
Service discovery
The same conversational interface can help users find institutional services and information.
Dynamic data access
AI can retrieve information from Hoopstr's APIs and databases rather than relying only on static content.
AI and human interaction
The platform can combine agent-led interactions with existing user-to-user conversations.
Built on existing chat infrastructure
Hoopstr can extend its existing CometChat implementation rather than building a separate communication layer for the AI experience.
By combining its alumni network with conversational AI and CometChat's communication infrastructure, Hoopstr is moving the alumni experience from simply finding connections to helping users discover the right person, information, or opportunity and act on it.
