BounceGrip

BounceGrip is the secure bring-your-own-key (BYOK) AI workspace for founders and small teams that want flexibility with leading language models without chaos from separate AI tools, fragmented prompts, hidden token markups, or unmanaged API keys. It gives a team one place to work with OpenAI, Anthropic, Gemini, and other enabled providers while keeping provider relationships and usage billing in the team’s control. Most teams begin with individual AI subscriptions and a collection of copied prompts. That works briefly, then creates problems: people cannot tell which model is best for a task, useful work disappears in private chats, API keys get shared too widely, and nobody has a view of usage or cost. BounceGrip is designed to turn that scattered activity into a governed, collaborative workflow without forcing a team to choose a model vendor. Workspace owners connect provider API keys once and decide which models a workspace can use. Requests are routed server-side, and keys are encrypted at rest rather than exposed to members in browsers. Team members can use approved models without ever seeing the underlying credentials. BounceGrip also keeps workspace data and permissions isolated, while prompts and files remain the customer’s data and are not used to train BounceGrip. The workspace is built around three ways of working. Chat gives the team one interface for leading models. Compare lets users send one prompt to up to four models and review responses side by side, making it easier to balance answer quality, speed, and provider price. Agents turn recurring work into reusable roles: teams can start with packs for founder operations, marketing, support, sales, and product work, or describe a job and have BounceGrip draft an agent to help complete it. BounceGrip adds the context a team needs to make AI useful beyond one-off chats. Projects hold relevant work together. A prompt library makes repeatable prompts easy to save, share, and run with variables. Teams can attach useful file context, preserve strong answers as saved outputs, and give everyone access to workflows they need. A usage dashboard organizes token activity by model, provider, member, and project, with cost estimates where provider rates are available.