Awful example to use, in the end the LLM here would have ended up buying a video for $80
We have lists of demos and sales closed with this already I can happily show anyone who wants to see over a call.
Personalized agents are the UPGRADE to the automation our customers have been using for months: https://drive.google.com/file/d/1DPPjnMvRJI0N__PrrbBFi3UDsD8...
Seems odd that people would get irritated by this since all startups could use this
I think LLMs have a huge potential for changing the way we work, and creating hybrid human/AI interfaces where the AI does the boring parts and the human becomes more productive as a result – but with these agent systems it feels you're aiming to completely replace the human and instead go for quantity over quality
At Salesforce and Experian I worked with some exceptional sales people, who would be very likeable and turn a cold email into a $1m/yr contract. These people would be very smart and leverage their network, background and expertise. Maybe AI can completely replace them one day, but in the meantime I think the real money in this area is building tools that one of these professionals can use to 2x, 3x, 10x their workflow.
Parker Harris was actually one of my early mentors, because my first co-founder was the first investor in Salesforce(Halsey Minor). A lot of his advice went into building this.
We're not selling to the CEO, we're selling to the VP of sales who can now a/b test this, or the startup founder who can't afford a sales team yet, and I'm very sure a large percent of reps will fall below the line vs the auto-responders. We increased the number of demos our sales team saw with just GPT-4 auto-responding, then we doubled the total reply rate with personalized outbound. It makes sense that it wouldn't have the exact same reply rate. I'd be happy to give you access to get your insight on the direction we should go if you're interested.
This agent was built for influencer partnerships to help us growth hack
We're also using it to sell Cheat Layer, and getting 10% reply rates vs the cold static outbound in Apollo.io
Some improvements we've made: 1) Agents use a global supabase to store "successful" code from tasks so new users don't have to go through regenerations to find the same solution.
2) Each user has their own supabase to grow valuable business data, and our autonomous sales agents can now maintain conversations across email, sms, and voice calls. This is our defensive moat vs Microsoft, since they can't hire overnight to copy this data and can't sell it back to customers. This allows agents to implicitly learn things like "high converting copy" and our new users don't have to start from 0 when starting a new business. We even had a customer reply to one agent 5+ times: https://twitter.com/CheatLayer/status/1676959310562349059?s=...
3) We designed a new 'guidelines' framework that dynamically constructs the system prompt based on context, which allowed us to push the limits in specific use cases.
4) A library to divide up tasks and solve them as a hierarchy https://www.youtube.com/watch?v=FFb59WmQoFU
5) Most exciting recent update was adding voice synthesis to our autonomous sales agents, who can continue the conversations across email, sms, and now voice. https://www.youtube.com/watch?v=2s4iQ_joToc
6) We setup a testing framework for the guidelines I mentioned above to make sure we constantly iterate. In this way we can also prove the old GPT-4 model is definitely worse and some automations don't work at all if you take full advantage of the new model.
Check out our weekly live streams for all the updates, since there's been a lot since we've been working on this since 2021: https://www.youtube.com/@CheatLayer/streams