Achieve Human-Like Efficiency and Machine-Like Scalability with Generative AI
AI ranks among the top five investment priorities for over 30% of CIOs worldwide, with Generative AI (GenAI) quickly gaining prominence. Businesses globally are significantly investing in Generative AI use cases to achieve their distinct business objectives.
By integrating Generative AI, you can automate and improve content creation, producing high-quality, varied content at scale. This technology finds applications across various sectors, including research and development, marketing, sales, software engineering, and customer operations, underscoring its importance and the value of investing in it for businesses.
Prescience offers full cycle Generative AI services including discovery consulting, use case validations through Proof of Concept (PoC) developments, pilot projects, and large scale deployments thereby delivering productivity, efficiency, scalability, and cost benefits.
What is Generative AI?
Generative AI is a continuum of advancements in AI. In simple words, it is human-machine collaboration for a better future, fueling human creativity and enabling problem-solving at scale.
Moving on from traditional data handling and interpretation, built upon deep learning technologies, Gen AI is capable of producing new data in the form of images, text, audio, and video. From marketing to retail, healthcare and software, it is transforming every sector across a spectrum of industries.
Key Benefits of Generative AI
Generative AI Use Cases Across Functions
Summarization of documents, extraction of themes, extraction of key information ability to query large documents.
Automatic Code Generation, Code migration, test case and test data generation.
Agents that automate individual and organizational tasks, workflow automation, assistants that improve productivity via data & information integration.
Connect to authoritative data sources to query data using natural language.
Research commentary, identifying trends and summarization, translation, co-pilot for ideation.
Image generation, concept wireframes, synthetic data creation, missing value imputation.
Unsure how to kickstart your Generative AI journey?
Our Approach
No matter where you are in your Generative AI development journey or what your current business challenge may be, we have a structured approach to assist you at any stage. Whether you’re initiating, advancing, optimizing, or rebuilding a solution, or seeking expert guidance for innovative enhancements, our experienced team and proven processes are perfectly aligned to support your business goals.
DISCOVERY PHASE
Get started with a consulting project to identify the right use cases for your organizationRAPID PROTOTYPING
Build quick protypes with shortlisted use cases to check the validityPILOT PROJECT
Test your use cases with pilot projects to check initial impact /outcomesSCALED DEPLOYMENT
Organization wide deployment based on the success of the pilot projectsOur Expertise as a Generative AI Consulting & Development Company
Is your business Generative AI ready?
Generative AI Case Studies
A market data provider faced the challenge of efficiently analyzing extensive financial documents within limited time constraints. By leveraging a Language Learning Model (LLM), we developed a solution that integrates with financial data, offering a robust decision support tool for analysts. This approach enhances their ability to derive actionable insights rapidly and effectively.
We created an automated pipeline using LLM to solve grading and assessment challenge for teachers. This solution has dramatically improved assessment quality and reduced variability. It automatically evaluates answer scripts, with the final output subject to teacher review and approval for added reliability. The process involved digitizing student answer scripts, training pipeline to LLM, employing an inference pipeline to automate the assessment process.
An LLM based solution built for senior management that automates the query generation from natural language. Senior management required the query to get adhoc answers for making management decisions so we launched a pilot for HR Datamart with a roadmap to create the same for other Datamarts. The solution result in improved management reporting ability and shrinking of decision-making time.
Developed an LLM-based solution to extract key terms from lengthy vendor contracts, along with a query bot for specific questions, enhancing decision-making for category managers. This approach, after testing multiple LLMs for performance, led to significant cost savings by streamlining the analysis of complex, 50+ page documents and identifying critical information like payment terms and return periods.
Frequently Asked Questions
Generative AI works by learning from a vast dataset to generate new content or solutions that are similar but not identical to what it has seen before. It uses algorithms to understand patterns, structures, and relationships within the data and applies this understanding to generate new, original outputs.
Generative AI capabilities include creating text, images, music, and videos, generating realistic simulations, providing data augmentation, creating virtual environments, and even innovating new product designs or chemical compounds.
Many experts believe generative AI represents a significant step forward in the AI field, with potential transformative impacts across various industries. It’s seen as a key driver for innovation, enhancing creativity, decision-making, and problem-solving processes.
Generative AI focuses on creating new data or patterns based on learned information, while analytical AI is about analyzing and interpreting existing data to make predictions, decisions, or insights.
Risks include the potential for generating misleading or harmful content, privacy concerns, copyright infringement, the propagation of biases found in training data, and challenges in ensuring the AI’s outputs are accurate and ethical.
Yes, generative AI often uses deep learning, particularly neural networks like Generative Adversarial Networks (GANs) and Variational Autoencoders (VAEs), to learn from data and generate new, unique outputs.
The future beyond generative AI might focus on more advanced forms of AI that exhibit higher levels of understanding, reasoning, and even consciousness or AI systems that can innovate or create at levels surpassing human capabilities.
A generative AI development company specializes in creating AI models that can generate new content or solutions. They work on developing, training, and deploying AI systems tailored to specific creative or problem-solving tasks across various industries.
Generative AI learns from large datasets to create new, original outputs that mimic the learned data. It employs complex algorithms, such as neural networks, to understand data patterns and relationships. After training, the AI can generate new content, predict trends, or solve problems, producing results that are similar to, but not exactly the same as, the input data it was trained on. This enables AI to innovate across various applications, from art and design to scientific research.
Businesses can use generative AI for content creation, design, product development, customer engagement, process automation, and more. It can enhance creativity, reduce repetitive tasks, and provide insights or solutions that may not be immediately obvious to human operators.
The cost varies widely depending on the complexity of the project, the data requirements, and the specific goals of the AI system. It can range from thousands to millions of dollars, including expenses for data acquisition, model training, computing resources, and ongoing maintenance and development.
