Let Generative AI Help You Reinvent Your Platform and Customer Experiences

Our expert AI developers understand how powerful and effective generative AI is for future-proofing enterprises. We help you craft unique AI models based on your proprietary data, accomplishing a tailor-made solution that unleashes the full potential of your business. At Signity, we will help create AI-powered solutions and also provide comprehensive and ongoing support to ensure an unstoppable experience for your customers.

 OpenAI API Vs Custom LLM

OpenAI API Vs Custom LLMs

Talk to our experts to know about when to use readymade OpenAI API vs Custom fine tuned LLMs according to your requirement.

 Develop products with ChatGPT

Develop Products with LLMs

Utilise LLMs for customised product and application development with minimal fine-tuning as per the business use case.

 Utilise your Live Data to build apps

Utilise Your Live Data to Build Apps

Remove limitations of ChatGPT by binding it with your business’ live data and make it more accurate and relevant.

 Integrating AI models into your existing products

Reduce LLM Hallucinations

Say goodbye to LLM hallucinations by leveraging the knowledge within private data.

Unlock Endless Possibilities With LLMs

LLM Development

We handle the whole LLM development process, from designing the model architecture to developing and tuning it. We can define your own custom models using PyTorch, TensorFlow and any other suitable frameworks.

 LLM Development
StrategizingSolution

Strategizing Solution

Our software development process is beyond providing generic solutions but is offering a thorough analysis of your unique use case, understanding your business requirements and designing a AI solution that best matches you needs. 

LLM Powered App Development

We can help effortlessly create or modify Large Language Models like ChatGPT or even more complex Generative AI models as per your requirements. Our expert consultants and developers will help you define the right feature-fed LLMs Application that goes with your business vision and also supports its growth.

 LLM powered App development
 Support & Maintenance

Support & Maintenance

We develop comprehensive support and maintenance capabilities tailored to your LLM-based applications. Our MLOps platform provides a full-blown comprehensive monitoring capabilities to ensure the optimal performance and reliability of your ML models, allowing you to track key metrics and indicators to assess the model's accuracy, efficiency, and effectiveness. Furthermore, our platform enables automatic retraining and deployment of ML models.

Unlock New Possibilities with Cutting-Edge LLM Solutions!

Collaborate with our LLM experts today and get customized solutions for your business.

Our Technical Expertise

Natural Language Processing

Natural Language Processing

Build custom NLP models by accessing structured and semistructured content. The model frameworks and libraries such as TensorFlow, NLTK, and spaCy. Help your business discover fresh data from search queries, mined web data, business data repositories, and audio sources and conduct predictive analysis.

Machine Learning

Machine Learning

Our developers leverage various ML development kits, such as Scikit-learn, Keras, PyTorch, TensorFlow, and Caffe. With these extensive toolkits, they can deploy and implement advanced ML-based solutions that use various learning algorithms, such as supervised, unsupervised, and reinforcement learning. Their diverse skill set allows them to tackle a broad spectrum of machine-learning tasks and deliver robust and effective solutions to meet our client's needs.

Fine-Tuning

Fine-Tuning

We collect data and adjust the model parameters, tailor the model architecture, and perform other essential actions to optimize pre-trained large language models, such as OpenAI models, BERT, LaMDA, or BLOOM, for particular language-related tasks and domains.

In-context learning

In-Context Learning

In-context learning is a technique used to improve language models’ performance in handling contextual information by incorporating additional context during the training phase. It empowers language models with an enhanced understanding of context, improved reasoning and inference skills, and tailored problem-solving capabilities. It is also known as prompt engineering from a machine-learning perspective

Few-shot Learning

Few-Shot Learning

In many real-world scenarios, obtaining a large amount of labeled data can be very difficult, expensive and time-consuming. Few-shot learning enables machine learning models to learn from only a few labeled data samples. The goal of few-shot learning is to enable models to generalize new, unseen data samples based on a small number of samples we give them during the training process

Sentimental Analysis

Sentimental Analysis

Sentimental Analysis helps in understanding whether a set of words are positive, negative, or neutral. This help businesses improve their products and services, which can be conducted by techniques such as Naive Bayes.

Our Advanced Artificial Intelligence Models Expertise

  • Image Recognition & Processing
  • Large Language Models
  • Speech Recognition
  • check Dall.E
  • check Stable Diffusion
  • check MidJourney
  • check GPT 3.0 /3.5
  • check LLaMA (Large Language Model Meta AI)
  • check Turing-NLG
  • check Claude
  • check BLOOM
  • check GPT-NeoX
  • check Whisper

Our Advanced Artificial Intelligence Models Expertise

Image Recognition & Processing icon

  • check Dall.E
  • check Stable Diffusion
  • check MidJourney

Large Language Models icon

  • check GPT 3.0 /3.5
  • check LLaMA (Large Language Model Meta AI)
  • check Turing-NLG
  • check Claude
  • check BLOOM
  • check GPT-NeoX

Speech Recognition icon

  • check Whisper

Our ML Model Based Development Process

Our OpenAI Models based Development Process
1

Problem Formulation

Signity's researchers will understand your problem statement and provide you with AI/Automation models and the relative data sets needed to create your AI-powered solution. The development platforms can range from generating images, translating between languages, customer support, and much more depending upon the exact scope and objectives of the model.

2

Data Collection

After listing the requirements, data collection and pre-processing are conducted so that the feature extraction from this data set can be conducted correctly. Data Collection and pre-processing help in building predictive models using trends and insights harnessed from data sets.

3

Model Architecture

Researchers at Signity design and develop neural network architecture suitable for specific tasks. This could be an LSTM for sequence modelling, CNN for images, Transformer for language, etc. With time we experiment with different architectures to optimise the performance of your AI model.

4

Hyperparameter Tuning

Researchers fine-tune hyperparameters like learning rate, batch size, number of layers/neurons, etc., to improve the model's training and performance. This also maximises your model's predictive accuracy.

5

Training

We will train models on high-powered GPUs, using techniques like reinforcement learning, supervised learning, etc. Most of the time, transfer learning is used to fine-tune existing large language models (LLMs) for specific tasks or domains. This involves taking a pre-trained LLM and further training it on a smaller dataset that is specific to the task or domain of interest. This significantly reduces the amount of data and computational resources required to train a model from scratch while achieving good performance.

6

Evaluation

The trained model will be evaluated to measure its performance on test data and the target metrics defined in the requirement step. If the performance is not satisfactory, our researchers will rework the architecture below to improve the overall model.

7

Deployment

If the model achieves the target performance and expected output, deployment on the production environment will be initiated.

8

Feedback and Iteration

Signity incorporates feedback and review meetings on their deployed models to continue improving them over time through continued data collection, model updates, and retraining. It is an iterative process.

Our Solution Development Stack Empowered by OpenAI Models

Our Technology stack to develop AI solutions for business. Our team of developers, testers and analysts are equipped with a powerful stack of AI and machine learning frameworks, including:

OpenAI Models

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DALLE 2

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GTP-3

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GA

AI Frameworks

Tensorflow_logo

TensorFlow

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PyTorch

Keras_logo

Keras

Cloud Platforms

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Microsoft_Azure_Logo

Integration and Deployment Tool

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Ansible_logo

Programing Languages

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R_logo.svg

Database

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MySQL-Logo

Need LLM-Powered Solutions for Your Business?

Generative AI & LLMs Use Cases

Chatbot Assistance and Language Understanding

Large Language Models dramatically improve customer experience and relationships by creating chatbot systems that provide conversational assistance, answer user queries, and demonstrate an understanding of natural language, enhancing customer support and user engagement of the business.

Multilingual Translation and Language Generation

Large Language Models have helped develop products like Google Translate that power multilingual translation. The language generation model highly improves accurate language translation among numerous languages, improving cross-cultural communication and accessibility.

AI-Powered Legal Analysis

Another greater use case that LLMs assist in is legal research and documentation review. The generative AI has the capability to analyse large volumes of legal documents, identify relevant case precedents from that dataset, and then provide supportive information to legal professionals.

Content Creation for Virtual Influencers

Generative AI and language models help influencers on social media platforms to automate their content as per their content genre such as posts, captions, and interactive conversations, revolutionising influencer marketing.

Automated Coding and Program Synthesis

Generative AI models help developers and product owners in creating automated coding tasks by generating code snippets, program synthesis, and supporting coding workflows for architectures, all in all accelerating software development processes without any bugs.

Product Recommendations

Relevancy and Personalization

Our LLM solutions help develop models that can:

  • Improve search relevancy: Our models can understand and learn about the meaning of customer queries and provide more accurate results. Resulting in obtaining search results that are more relevant to the query. 

  • Offer personalised results: Our solutions models take into account customer purchase behaviour, their browsing patterns, and their previous purchase to provide them more personalised results. Resulting in  increased customer engagement, satisfaction, and better purchasing experience.
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Exploratory Data Analysis

Our LLM and ML solutions can help businesses to:

  • Drive insights from unstructured data: Our LLM and ML models analyse texts, social media, images, and other unstructured data to identify and investigate trending patterns, providing insights that would be difficult to conclude in a traditional method. This helps businesses with better decision making and also enhances their operations.

  • Turn unstructured data into human-generated documents: Our solutions can analyse and convert unstructured data into human-generated documents which are easy to understand and operate. This can help businesses with their documentation and reporting processes.

Market Intelligence

Our LLM based market intelligence and sentiment analysis help businesses to:

  • Understand current and upcoming market trends: Our LLM models can analyse news, social media platforms, and other sources to recognize emerging market trends. Guiding businesses to identify new opportunities and invest in them.

  • Understand consumer behavior: Our solutions can analyse customer reviews on various websites, social media, and other sources to understand how customers think and feel about products and services. This can help businesses to improve their products and services and better target their marketing efforts.

  • Understand competitors and partners: Our solutions can analyse competitor websites, social media, and other sources to understand their strategies, strengths, and weaknesses. This can help businesses to develop competitive strategies and build stronger partnerships.
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Related Articles

Frequently Asked Questions

Have a question in mind? We are here to answer. If you don’t see your question here, drop us a line at our contact page.

What are Large Language Models (LLMs)? icon

Large Language Models (LLMs) are AI models that are trained and fine-tuned on large datasets and bring out outputs as expected in human-like language. LLMs is a deep learning technique capable of generating text according to query, making language translation, and improving natural language understanding.

What are training processes for Large Language Models? icon

Large Language Models can be trained by a process called unsupervised learning. They are fed with a huge amount of text data, and with that data, the model recognizes and learns to predict the next word in a sentence based on the context. This process is usually used on high-performance computing infrastructure. 

Can Large Language Models be fine-tuned for specific tasks? icon

Yes, Large Language Models can be trained and fine-tuned according to specific tasks by training them on domain-specific datasets or labelled data. The fine-tuning process allows the models to be further refined and help them adapt to specific contexts, making them more suitable for tasks such as sentiment analysis, entity recognition, and question-answering.

What are some practical applications of Large Language Models? icon

Large Language Models have a lot of applications, such as creating chatbots and virtual assistants, content generation, language translation, sentiment analysis, recommendation systems, and text summarization. They can also assist in researching, writing, and analysing data.

Are there any limitations of Large Language Models? icon

Large Language Models require a lot of data and substantial computational resources for training. The data can impact its output as biased data can lead to biased results making the information misleading and incorrect. Handling long-term context and maintaining consistent responses still pose challenges for these models.

What are Large Language Models (LLMs), and how are they used in various industries? icon

Large Language Models (LLMs) are AI models that are trained on extensive text data. So different industries can utilise LLMs for tasks like customer service, content generation, language translation, sentiment analysis, legal research, financial analysis, recommendation systems, medical diagnosis, and a lot more.

What is the difference between ChatGPT and Large Language Models (LLMs)? icon

ChatGPT is a specific model of a Large Language Model (LLM) developed by OpenAI. LLM is a broader category that helps develop AI models that could serve various tasks, including ChatGPT, sentimental analysis, legal research and all the listed above tasks. ChatGPT is a conversational application defined to adhere to a specific task, while LLMs can have a wider range of applications. 

Can ChatGPT be considered as a standalone Large Language Model? icon

While ChatGPT has made its mark and is a remarkable tool that is a powerful conversationalist, it is a smaller and more specialised version of LLMs. Large Language Models typically refer to models that can be applied to a wide range of language-related tasks beyond just chat-based interactions and can be trained on massive datasets. 

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