We don’t need to write once again about the upcoming disruption in the world with Generative AI solutions. Enough has been written about ChatGPT and the potential of Large Language Models(LLM’s).
At Inceptive, we are building POC’s across industries using LLM’s and fine-tuning them with data for multiple use cases. These POC’s are primarily based on Natural Language Processing(NLP) tasks such as Q&A, chatbots, agents, using text, audio & video as enablers to build robust solutions rather than being just multi-dimensional datasets.
Our Generative AI Services
Chatbot Development
We develop chatbot applications using OpenAI and similar Large Language Models, fine-tuned to your specific use case. Data privacy and security is core when internal data is passed for training to the model.
Q & A App
We develop Q & A applications that can quickly help solve your user’s questions easily using structured and unstructured data to learn for the model.
Content Creation
Content creation integration is becoming a defacto in every app, where AI is able to generate content based on single-shot or multi-shot use cases. The most common feature implementation after chatbots.
Greenfield AI implementation
End-to-end Gen AI implementation using foundation models on your specific use case, training, validation & testing of datasets for quality, ensuring cost and processing power balance.
…and what else? We are discovering new use cases every day across industries!
We are enabling these disrupters using Vertex AI by Google, OpenAI’s API, Azure OpenAI service, Fast.ai, and HuggingFace on pre-trained foundational models such as GPT-4, Dall-E, and BERT.
These cloud-based services are a great start to start your business Applied Intelligence journey using Generative AI without the high entry barrier of traditional Machine Learning models as these models are pre-trained, reducing computation and energy costs and carbon footprint.
As a starting point, we encourage businesses to identify the low-hanging fruits in their organizations which can benefit from pre-trained model’s or fine-tuned models and where a large existing dataset is available.
As with the tech side of things, the implications of using AI as a tool for the end-user have to be factored in with appropriate training and resistance to change.
AI Tech We Love
Generative AI Services For
Banking
Leverage Generative AI to create personalized financial insights, fraud detection, credit risk assessment, and customer support. Enhance user experience with real-time analytics and intelligent recommendations for financial growth.
Insurance
Utilize Generative AI for accurate risk assessment, claims processing, personalized policy recommendations, fraud detection, and customer service automation. Improve efficiency, reduce costs, and deliver tailored coverage solutions.
Retail
Enhance inventory management with demand forecasting, personalized product recommendations, pricing optimization, and customer sentiment analysis. Drive sales, improve customer experience, and streamline operations using AI-powered insights.
Health
Empower diagnosis with medical image analysis, personalized treatment plans, drug discovery support, patient risk prediction, and health behavior modeling. Transform healthcare delivery with advanced AI-driven insights and innovations.”
Software & Platforms
Optimize code generation, bug detection, performance tuning, user behavior analysis, and customer support automation. Empower developers with AI-powered tools for enhanced software development and user experience.
Manufacturing
Enhance production processes with predictive maintenance, quality control, supply chain optimization, anomaly detection, and product design simulation. Drive efficiency, reduce downtime, and boost productivity with AI-powered insights.
Key Factors For Embracing Generative AI
Let’s talk – we would love to hear your use case
FAQ
Generative AI is a type of artificial intelligence that focuses on creating new and original data rather than just recognizing patterns in existing data. It uses algorithms, particularly generative models, to produce new content that resembles the training data it has been exposed to. This can be applied to various tasks like generating realistic images, creating music, writing text, and even generating realistic human-like conversations. Generative AI is a powerful tool with applications in creative fields, content generation, and data augmentation for training other AI models. However, it also raises ethical concerns, as it can be misused to create fake content or spread disinformation.
Find problems which are low hanging fruits. Concepts like, creating new content, summarising data, labelling data, sentiment analysis, chatbots, etc. are easy to understand by business users and for acceptance. Once you have implemented a proof of concept, you can dive deeper into solving more complex problems.
Costs are not prohibitive if you compare to the business acceleration you will get. Most of the solutions are hosted with Microsoft, Google, OpenAI, HuggingFace, etc. You might incur costs in training foundation models, but most use cases are covered, so you will need to only fine-tune models, where costs are low.
A typical proof of concept should take a month once the objectives and Key Performance Indicators are established.
Your data is safe and private and is not used by the model to train itself back. Any fine-tuned model with proprietary dataset belonging to you and confidential is not used back by model creators. For eg, OpenAI has mentioned this very clearly at https://openai.com/policies/api-data-usage-policies. While selecting any model, it is essential to review the model’s data usage policy.
Yes, one of the primary use cases of Generative AI is to fine-tune the Large Language Models on your private data which you can then query for anything.