Original Summary

I’m building Quaspar, a platform for creating and deploying software workers for healthcare and life sciences. What we think makes Quaspar different from a typical AI agent builder is that we’re focused on what happens after the initial prompt. We’re building agents that can maintain relevant context across a workflow, understand what has already happened, adapt when an exception comes up, and know when to continue, retry, escalate, or ask for human approval. Teams can connect internal systems and APIs, define permissions and approval points, test workflows against failure cases, validate them before production, and then deploy and monitor them inside the organization. We’re starting with healthcare and life sciences because software working across these systems needs to be reliable, auditable, secure, and able to handle messy real-world situations instead of only working on the happy path. The product is still early, so I’m looking for honest feedback: Does this solve a real problem? Does the differentiation make sense? What would you want us to prove first? If you want more context, you can check out quaspar.com .   submitted by   /u/Automatic_Method4081 [link]   [comments]

  • 情报分类：商业与市场研究
  • 分类依据：内容涉及商业、投资或市场动态
  • 信息来源：Reddit · SaaS
  • 发布时间：2026/9/23 02:16:21