Data is the fuel for generative AI, as it provides the information and inspiration for the models to learn and create new data or content. However, not all data is the same, and different domains and geographies may have different data characteristics, requirements, and challenges. For example, data in the education domain may have different formats, sources, and standards than data in the banking domain. Data in the healthcare domain may have different privacy, security, and ethical issues than data in the edtech domain. Data in the cybersecurity domain may have different threats, risks, and solutions than data in other domains. Therefore, to build effective and reliable generative AI models, it is essential to have a deep understanding of the data and the domain.

At Helical IT Solutions, we have more than a decade of experience in working with data across various domains and geographies, such as education, banking, healthcare, edtech, cybersecurity, and more. We have a team of data experts who can collect, clean, integrate, and annotate data from various sources and formats, ensuring the data quality and relevance for the generative AI models. We also have a team of domain experts who can provide insights, guidance, and feedback on the data and the models, ensuring the domain-specificity and accuracy of the generative AI models. Our deep technical expertise and diverse domains knowledge goes a long way in delivering high quality output. We can help you build domain-specific generative AI models that can meet your unique needs and challenges.

Generative AI is a powerful and versatile technology that can create new data or content that can enhance your business and customer experience. However, to harness the full potential of generative AI, you need a partner who can understand your goals, challenges, and expectations, and provide you with the best solutions and services. At Helical IT Solutions, we are more than just a generative AI development company. We are your trusted and reliable partner who can help you achieve your goals with generative AI. Contact us today and discover how our generative AI development services can help you.

Data collection is the process of acquiring data from different sources, such as databases, web pages, social media, sensors, etc. Data cleansing is the process of removing or correcting errors, inconsistencies, outliers, or missing values from the data. Data integration is the process of combining data from different sources into a unified and consistent format. Data annotation is the process of adding metadata to the data, such as labels, categories, tags, sentiments, etc., to make the data more informative and useful for LLM and analytics models. At Helical IT Solutions, we have extensive experience in data collection, cleansing, integration, and annotation. We use various tools and techniques, such as web scraping, data extraction, data validation, data transformation, data normalization, data deduplication, data imputation, data augmentation, data classification, data clustering, data sentiment analysis, etc., to gather and pre-process large datasets from various sources and formats.

Data quality is the measure of how well the data meets the requirements and expectations of the LLM and analytics models. Data quality can be assessed by various dimensions, such as accuracy, completeness, consistency, timeliness, relevance, etc. Data quality tools and frameworks are software applications or libraries that help to monitor, measure, and improve the data quality. Data quality tools and frameworks can perform various functions, such as data profiling, data auditing, data cleansing, data matching, data enrichment, data governance, etc. At Helical IT Solutions, we use data quality tools and frameworks, such as Talend, Trifacta, Informatica, IBM, Microsoft, etc., to ensure that the data is consistent, accurate, and relevant. We also use data annotation tools and frameworks, such as Labelbox, Prodigy, Snorkel, Doccano, etc., to label and enrich the data with metadata, such as categories, tags, sentiments, etc. By doing so, we ensure that we have a high-quality training data that can support various LLM and analytics models.

Natural language processing (NLP) is a branch of artificial intelligence that deals with the interaction between computers and human languages. NLP enables computers to understand, analyze, generate, and manipulate natural language, such as text or speech. NLP can be used for various purposes, such as information extraction, sentiment analysis, question answering, text summarization, machine translation, chatbots, and more. At Helical, we have expertise in working with various NLP technologies, such as spaCy, NLTK, Hugging Face, Google Cloud, etc., to create new applications as well as interact with existing applications that can handle various tasks involving natural language.

Deep learning is a branch of artificial intelligence that uses multiple layers of artificial neural networks to learn from large amounts of data and perform tasks such as image recognition, natural language processing, speech synthesis, and more. At Helicaltech, we have a team of experts in deep learning who can design, develop, and deploy complex neural networks for various applications and domains. We use the latest tools and frameworks, such as TensorFlow, PyTorch, Keras, and OpenCV, to implement state-of-the-art algorithms and models that can achieve high accuracy and performance.

However, we do not just use off-the-shelf solutions for our clients. We understand that each client has unique needs and challenges, and we strive to provide customized solutions that suit their specific goals and requirements. We can modify, optimize, and fine-tune existing algorithms and models, or create new ones from scratch, to address the problems and opportunities that our clients face. We can also integrate our deep learning solutions with other technologies, such as cloud computing, big data, and IoT, to create scalable and robust systems that can handle large and complex data sets. By doing so, we help our clients unlock the full potential of AI and gain a competitive edge in their respective fields.

Large Language Models (LLMs) are powerful and versatile tools that can generate high-quality and diverse outputs, such as texts, images, codes, etc. However, LLMs may not always meet your specific needs or expectations, as they are trained on general and large datasets that may not capture your domain, task, or style. Therefore, you may need to tune or adapt the LLMs to suit your requirements and goals.

At Helical IT Solutions, we offer two types of services to help you customize and optimize the LLMs for your use cases: Model Fine Tuning and Prompt Engineering.

Model Fine Tuning is the process of further training the LLM on a smaller and task-specific dataset, using your own data or data from other sources. This allows the LLM to adjust its parameters and learn from the new data, improving its performance and accuracy for your task or domain. Model Fine Tuning can also help you reduce the size and complexity of the LLM, making it more efficient and cost-effective.

Prompt Engineering is the process of refining the input prompt that you provide to the LLM, using various techniques and strategies, such as context, instruction, examples, etc. This helps the LLM to generate the desired output that matches your intent and expectation, without changing its parameters. Prompt Engineering can also help you control and guide the LLM’s responses, making it more consistent and reliable.

We have a team of experts who can work closely with you to understand your needs and challenges, and provide you with the best solutions and services. We use the latest and most advanced LLM technologies and frameworks, such as GPT-4, Llama, PaLM-2, etc., to create intelligent and adaptable applications that can produce high-quality outputs.

Contact us today and discover how our Model Fine Tuning and Prompt Engineering services can help you achieve your goals.