Gen AI Services
At Shaeryl Data Tech, we capitalize on Generative AI from intelligent content generation to sophisticated design and workflow automation. Our services enable companies to innovate more rapidly with our Gen AI solutions, increase experience personalization, and create additional monetization opportunities to –
- Stay ahead of the curve.
- Transform operations with automation.
- Scale smarter with Shaeryl Data Tech.
Gen AI Capabilities
RAG Based Application
Design Automation
Problem- solving
Personalization
Discover GEN AI's Impact: Explore Real-world Applications
Case Study Showcasing the Creative and Transformative
Potential of Generative Artificial Intelligence
- Background
- Solution
Data Collection and Preprocessing
We collected a diverse dataset of resumes, including various formats (PDFs, Word documents, etc.). The GenAI model was trained on this dataset to learn patterns, keywords, and relevant features.
Resume Scoring
When a new resume is submitted, the GenAI application analyzes it. It assigns a score based on factors such as relevant skills, experience, education, and alignment with the job description (JD).
Ranking and Filtering
Resumes are ranked based on their scores. The top-ranked resumes are presented to the recruitment team for further review.
- Benefits
Time Savings: Recruiters now spend 40% less time analyzing JDs and resumes.
Accuracy: The AI-driven platform reduces recruitment errors by 30%.
Cost Reduction: Faster hiring processes lead to substantial cost savings.
- Results
Our GenAI application transformed our recruitment process:
Efficiency: Recruiters focus on high-potential candidates, improving overall efficiency.
Quality: The system ensures consistent and unbiased resume evaluation.
Cost-effectiveness: Reduced manual effort translates to cost savings.
- Conclusion
Curious minds welcome: Dive into our FAQs for insights
Curious minds are welcome to explore our FAQs for valuable insights. Discover how Shaeryl Data Tech’s expertise and services can drive your success by diving into the answers to common questions.
Gen AI is a branch of artificial intelligence dedicated to generating new content, including images, text, and music, rather than merely analyzing or identifying existing data patterns. Unlike traditional AI, which typically uses supervised or unsupervised learning to handle specific tasks with existing data, generative AI employs methods such as generative adversarial networks (GANs) and variational autoencoders (VAEs) to create original content by understanding the underlying data distribution.
Gen AI offers diverse applications across multiple fields. In art and design, it is used to create digital art, generate lifelike images, and design products. In entertainment, it helps compose music, write scripts, and develop virtual characters for games and films. In healthcare, Gen AI supports the creation of synthetic medical images for training diagnostic systems and simulating patient data for research. Furthermore, it is applied in content creation, data augmentation, and improving user experiences in sectors like marketing, fashion, and social media.
Generative AI methods use neural networks to analyze patterns and relationships within a dataset, creating new content based on this understanding. For instance, in image generation, a GAN (Generative Adversarial Network) involves two neural networks: a generator and a discriminator. The generator produces synthetic images from random noise, while the discriminator assesses their authenticity. Through repeated training cycles, the generator improves its ability to create increasingly realistic images that resemble the original dataset.
Common algorithms in generative AI include Generative Adversarial Networks (GANs), Variational Autoencoders (VAEs), autoregressive models such as Transformers, and Markov Chain Monte Carlo (MCMC) techniques. Each algorithm has distinct strengths and uses, enabling the generation of various types of content, including images, text, music, and videos.
The application of Gen AI brings up important ethical issues, especially concerning the authenticity and ownership of generated content. There are worries about its misuse for creating deepfakes or disseminating false information. Moreover, concerns about privacy, consent, and bias can emerge when generating synthetic data or altering digital content. As generative AI advances, it is crucial to develop ethical guidelines and regulatory measures to promote responsible use and address potential risks to individuals and society.
