Souvenir
Workspace connecting AI models with shared context
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Colin JO

Souvenir - Workspace connecting AI models with shared context

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Most AI tools isolate models, lose context between tasks, and require constant switching. This connects multiple models in one workspace with shared memory, so work carries across prompts. Builders can iterate faster without re-explaining context, operators can run workflows without tool-hopping, and teams can reuse knowledge instead of repeating work. Tasks are routed to the most suitable model automatically, improving output consistency and reducing manual effort.

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Colin JO
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We didn’t start by trying to build another AI tool. We started by using AI every day, writing, building, researching, running workflows — and kept running into the same problem: Nothing worked together. Context would reset between chats. Good outputs were hard to reuse. We were constantly switching between models, rewriting prompts, and stitching things together manually. For builders, it slowed down iteration. For operators, it made workflows messy and repetitive. For teams, knowledge just didn’t carry over. At some point, it became clear the issue wasn’t the models — it was the lack of a system around them. So we focused on connecting everything: - Shared context that persists across tasks - Workflows that don’t break between tools - Routing tasks to the model that fits best The goal was simple: make AI usable in a way that actually holds up in real, day-to-day work. We’re still early and actively shaping this with users. Curious, what’s the most frustrating part of your current AI workflow?