Springbok AI’s Post

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At the DLA Piper European Tech Summit, Victoria reflected on how Springbok AI works with our clients to rapidly implement fine-tuned document generation modules within their private, secure and fine-tuned GPT platform. Our clients' key to success? Moving quickly 🚀 Partner with a legal-specialised Generative AI firm like Springbok AI ⬇️ Get Springbok-GPT -->> Customise and fine-tune to your firm, and brand it however you like ⬇️ Turn your backlog of Generative AI ideas into scoped modules and assistants ⬇️ Roll-out the modules and assistants quickly within your Springbok-GPT platform ⬇️ Refine and repeat. This is effective for 3 reasons: 🏆 Familiarity with Generative AI drives better use case generation. The difference in idea quality between firms who have vs don't have Springbok-GPT is remarkable. 🏆 Your firm will be known for *shipping* solutions. Success loves speed. Conversely, the most costly mistake firms can make right now is inaction. Leveraging the expertise of a #generativeAI partner can help you to get to where you'd like to be, and get these tools into users' hands. Interested in a custom GPT, fine-tuned for your firm? Get in touch with us today: https://lnkd.in/eVMG5xcx 🚀 #AIInnovation #TechnologySummit #LegalTech #DLAPiper #EmpowerYourTeam #InnovateWithAI #GenerativeAIinlegal #GenAInlegal Hannah Smith

Transcript

With with one of our clients, we worked on creating a lease report generator and well initially before we we worked on it, they're they're data scientists had dabbled and using some openly available tools to see if they could use the RAG technique, retrieve, augment and generate technique that I mentioned to demonstrate that it was feasible. And they basically come to the conclusion that it wasn't feasible and then came to us and said you know we wanted to do this but it doesn't seem to be possible and so I think we're just going to have to. In this. And so my team said, OK, why don't we do half a Sprint to see if we can show you that it works and if it doesn't work, then we move on. And if it does, then we can move forward. And they're like, OK, great, let's let's go for it. And so, yeah, within a week we come back and we could demonstrate that it not only is it able to retrieve the right information, augmented in the right way and generate the right output, it was, but it was actually a whole combination of different models that we ended up using to generate the output in the standard that it was, that it was required and then ended up developing that. That's being launched now with, yeah, with one of our clients and you know there's so much to be done when you have the right expertise in the room from a legal perspective and the right expertise from the data science and like implementation perspective. And it's really about taking the ideas and yes, experimenting but also bringing them to fruition and actually just putting them into users hands then.

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