[caseyy]

Seems a bit aggrandized. Elon Musk, Mark Zuckerberg, climate change, app-tier pricing packages.

You are very likely providing a thin layer on an LLM which does summarization based on criteria. It would be reasonable if that's either a few-shot approach ($0, one time) or a fine-tuned model ($6 - $9 per million tokens, one time), plus the running costs ($0.0001 - $0.01 per run) for llama2 3-70b or gpt3.5-turbo-instruct.

Is there any additional USP, like have you used a data set sourced from hires in specific market sectors to know what parts of a resume really enhance or hurt the chances? A similar study on ATS/real filters would be also be a USP. Or another USP could be that it's super easy to use - drop a resume in, Apple Pay $1, it's done. I'm not seeing a lot of value if I can discuss my resume with ChatGPT and other assistants for free to get a second perspective.

Best of luck, of course.

[Oras]

I haven't tested the link, but I'm familiar with similar products. You don't seem to know what exactly goes behind screening CVs.

To get structured data, it's not just about parsing the file (PDF, Docs) with an open source to get the text. You need to extract skills, qualifications, work history ... etc.

There are tools already doing this part very well (before LLMs), so you need to make multiple LLM calls to make the right match or provide useful information.

Your comment suggests that you think it: Hey ChatGPT, this is my CV, and this is the job description; review it and give me suggestions.

Building systems around LLMs is not that easy.

[caseyy]

Well, you should see the link :) Having ATS-based screening or research-based screening would be fantastic. But this is a thin wrapper on an LLM.

I build AI products in a known company and experiment on my own time. It is really easy to build a product like this without aligning it to solid research or recruiter ATSs. I know the value of this alignment because part of my job is interviewing candidates for my team. Easy isn’t bad, of course.

The tool above is undercooked. It’s a very cool week-long project or a nice back burner one. I have nothing against that, good on GP for building it.

My point was that the presentation and pricing was a bit aggrandizing. Given what the project is, which is indeed similar to running a query with ChatGPT on a resume as it mainly does structured prompting, it has a better shot at being a $1 product. For more, it needs some of the things you and I mentioned.

[JoeyJooste]

Very true, we make a number of requests to the LLM, specifically related to each field after data extraction. We will be better communicating this is our sales copy in the future, as we want to really show the value we are providing.

[JoeyJooste]

Thanks for the feedback, we have adjusted the pricing to $1 and have apple pay built in. Also applied a few of your other suggestions. Thanks

[caseyy]

Thank you for improving it and taking feedback well. Now it’s a product I’d consider using.

There may be further value in providing ATS capability to recruiters — a tool that maybe sorts hundreds of resumes by various criteria a recruiter inputs. Such a tool could also highlight blind spots — “many candidates mention X, Y, and Z”, “This candidate stands out because A and B”. Such work would need to be done very responsibly, however.