| I understand where you're coming from - poor support experiences are are the bane of my existence. Preventing poor support experiences is the exact reason we started Fini. With a long career as a software engineer in the support industry, I have seen first hand when and why these initiatives fail. Unfortunately, when growth-stage companies experience a surge of signups, they face the choice between either 1) delay responding to all support issues by weeks, due to lack of staffing, or 2) trying to automate/offshore support, leading to poor coverage and accuracy. Both of these options usually lead to horrendous support experiences such as the ones you mention. Thankfully, with recent advances such as LLMs, we are one step closer to bridging this gap. In our most successful projects we are able to reach 95% accuracy specifically thanks to keeping a close eye on human evaluations. That's why we are launching this package as well, which we believe brings us one step closer towards our mission of helping millions of end-users receive the high quality support they deserve within seconds rather than weeks. It's a challenge which very few (if any!) companies have pulled off well, which is why we have decided to put all our effort into making scalable and hassle-free support our first priority. |
Is this the 95% of issues that are common between customers that could be and are handled at scale with a FAQ/knowledgebase that is surfaced to a user automatically through prompts?
How does it actually do on the smaller percentage of questions that are complex?
And that 95% is on the most successful projects. But what does the distribution of accuracy look like across all projects? If 2 projects out of 1000 are 95% accuracy that doesn't mean much to me.