It's hard to finetune with such a large amount of data and existing models do not perform well.
Basic Finetuning would improve response styles but would suck if the model did not have any training related toknowledge domain.
For high compliance requirements, a model has to be customized specifically and updated periodically.
Clio AI connects with 300+ apps and can set up continuous learning based on updates from each app.
When you need to control your system prompt and initial instructions according to business requirements.
With granular permissions, Clio AI can ensure that every employee can answer questions from the info they have access to.
Accessing the proprietary knowedge and institutional memory buried deep in files would teams get that jump in productivity as it works for ChatGPT and generic tasks like Marketing copy generation.
Saving Costs
With Open AI, they say training a model will cost $2M-$3M at the minimum. Plus compute costs.
With Clio AI, we pretrain an open source model and you can lower the costs by about 90%.
It takes about 2-3 months for a custom model to go live.
Highly Skilled Team
Clio AI's CTO has deployed Machine Learning Models at a similar scale at Tokopedia in his previous life.
We have experience in taking a instruction following LLAMA-2 from pretraining to a RLHF level (ChatGPT) that can continuously learn from human feedback.
High Control and Repeatablity
We understand what it takes to deploy a large scale model for enterprises.
You can set custom policies, control access, and do a number of other stuff depending on regulatory and compliance needs.
less time on finding information
Hours saved per employee per year
Faster Decisions
We build and train a Gen AI model like GPT 4 or Llama-2-chat. Instead of being trained on generic data, we train Llama-2 foundational model on your company data so that it accurately answers questions about your business without having to build RAG or finetuning mechanisms.
Your company has three sources of data - employee interactions, reports/memos, and databases. We combine all of it, train a model pretaining with your data, build an RLHF pipeline and everything else to get you a best in class gen AI model that understands everything about your business.
Finetuning works when the data is small(~50K tokens) and you want to teach the model specific patterns. You cannot teach a model a new knowledge domain by using Finetuning no matter what the model is.
RAG (Retrieval Augmented Generation) works when the context is small enough to fit inside a 8k token prompt. It's accuracy decreases with more and more data and would not work that well when the number of tokens are huge.
Yes, we enable deployment on your own premises. This can further reduce the costs and ensure that your data and secrets literally do not leave your premises. You can just tell us your preferences while getting a demo if you would prefer a private cloud or on premise deployment.
While Open AI starts at $2M-$3M minimum for this kind of thing, we would do it at a fraction of the cost. You can expect about 90% cost reduction using Clio AI rather than Open AI
No. While the model is trained once, and would need to be every few years, we also build a poweful Reinforcement Learning and Human Feedback Pipeline (RLHF) which can keep the model updated and gives out better answers based on previous human feedback.
Clio AI is capable of training separate models for individuals and teams as well. We see the need and understand how some data has to be kept accessible to certain teams. We will be happy to assist you if you need this kind of tenancy.
Yes, our output is an API endpoint which you can use to send data and receive responses. We provide a chat app on top of that. If you need to integrate specific apps, you can do so easily via the same endpoint.
