Hey guys, how do you tackle finding company domains if they weren't a part of the data set? I currently us a claygent on gpt 4o mini. The biggest problem with the setup is that it finds wrong domains. I would rather have it output nothing then to spend money enriching the wrong company. Prompt suggestions and other methods are welcomed 🙂
Hi Mihajlo, I think it will depend on the additional information you have. I mostly use AI for this use case as well. So my suggestion to you is to use a more recent model and to pass more information beyond the company name. Like if you have the company name and address you can mostly get the correct domain from a quick google search. If you only have the company name you can at least give it more context on your ICP so the llm can select the most likely domain.
Hey Diego, yeah, I agree. I usually provide the company name and some firmographi data. I’m now building a Clay workflow that also includes an additional_information field. Depending on the company and data source, this might include the address for a local business, the country for a SaaS company, or the industry if the data came from Apollo or a similar source. I then pass all of that context into the Claygent through the additional_information field. One reason I’m not using newer models is that they’re significantly more expensive. Even a model like 5.6 Luna has fairly high input/output-token costs. That alone is not a major issue, but reasoning tokens are counted as output tokens, which add to the cost much more. When running large campaigns, using bigger models therefore becomes difficult to sustain. Ideally, Clay would allow us to use models through OpenRouter, since that would provide more flexibility around model selection and costs.

