Overview
Generative AI is reinventing how work gets done
Generative AI is an advanced AI technique that involves training models to generate new and original content such as images, text, music, videos, etc. Gramener helps clients develop intuitive solutions across processes by building patterns from existing data producing new data that closely resembles the training examples.
Generative AI has gained significant popularity in recent months and has been of much relevance across industries due to its ability to enhance creativity, automate content generation, and assist in problem-solving.
Our Successful Gen AI Customer Stories
A Central Government Public Policy Think Tank’s Knowledge Portal had tens of thousands of documents on public policies across 40 sectors and all states.
To enhance accessibility for administrators and citizens, we implemented a solution utilizing semantic similarity and metadata and captured nuanced meanings and contextual embeddings for efficient document search.
The solution identifies top documents matching the query’s context and offers them as search results. Quantifiable outcomes include a remarkable 90% reduction in time to access information and a significant savings of 36 hours per research report creation effort.
The project addressed a challenge faced by the Public Policy Think Tank, involving a vast Knowledge Portal containing documents on policies, acts, and schemes.
The objective was to enable users to pose questions and receive answers using the appropriate documents within the portal.
Our solution utilized semantic similarity and Gen AI with a Large Language model. It first extracted specific content related to the user’s question from all documents and then used the extracted content to generate suitable responses. Users can customize responses by adding prompts such as “Display as bullet points” or “Elaborate.”
Quantifiable benefits include a significant 70% reduction in the time required to obtain accurate answers and a saving of 4 hours in writing clarification or addendums.
The project addressed the need of a Southeast Asian Government agency to automate data extraction and document handling, aiming to streamline record-keeping processes.
Our proposed solution involved two modules. The first, an OCR module, read digital format documents, while the second utilized Large Language Models (LLMs) to identify specific details from diverse document types, such as birth certificates, marriage certificates, death certificates, and passports.
Quantifiable outcomes include a substantial 65%-time savings in the document review process and an annual cost savings of 1 million for the Ministry of Health (MoH).
A global pharmaceutical company aimed to enhance the efficiency of marketing promotional content creation, seeking 50-70% automation for a process marked by complexity and delays.
The existing process involved multiple steps, including identifying main research papers, creating summaries, searching for complementary papers, and so on.
Our proposed solution was an interactive and user-friendly platform featuring four foundation modules with configurable layers. The modules encompassed Generation, Presentation, Validation, and Guidelines, simplifying the creation of promotional material.
Quantifiable outcomes include a significant 60%-time savings in creating draft material ready for review and a cost savings of $217K per quarter.
A global FMCG giant wanted to accelerate the research and analysis of their social media platform data to understand current customer behavior trends for informed product development, marketing, and branding strategies.
The existing process involved manually using specific keywords and social listening platforms, incurring additional costs. Gramener’s solution replaced these platforms, utilizing Large Language Models (LLMs) and BERT models to cluster discussion topics into multiple hierarchies. This not only reduced dependency on external services but also allowed for multiple iterations at minimal additional cost.
Quantifiable outcomes of the solution include a significant 55%-time savings in analysis generation and a cost savings of $18K per campaign analysis due to reduced analyst time.
A leading US-based e-commerce floor tile printing company sought to reduce dependency on designers for generating new wood designs patterns from a single input design.
Our proposed solution utilized stable-diffusion, a digital technique, to generate high-quality designs resembling the input image. The solution ensured that the final designs met specifications, including 4K resolution, and captured the desired look and feel. Each generated design was unique in finer patterns like knots and fine lines.
The outcome is an efficient tool that generates multiple designs based on a single input, significantly saving designer time and effort. This allows designers to focus on creating new designs, thereby generating more opportunities for revenue growth.
Quantifiable outcomes include an impressive 70% reduction in designer time required to replicate wood patterns and a time savings of 50 hours per unit (1 unit = 300 square feet).
Gramener applications in Generative AI
- Compliance: LLMs can be used to scan contracts, regulations, and other documents for compliance issues. This can help companies avoid costly fines and penalties.
- Warehouse management: LLMs can be used to optimize the layout of warehouses, assign tasks to workers, and track inventory levels. This can improve efficiency and productivity
- Risk management: LLMs can be used to identify and assess risks in the supply chain, such as supplier disruptions or natural disasters. This information can be used to develop mitigation strategies and improve resilience.
- Customer service: LLMs can be used to automate customer service tasks, such as answering questions and resolving complaints. This can free up human agents to focus on more complex issues.
Generative AI can be used to create synthetic medical data that preserves important statistical properties of real-world data while ensuring patient privacy. This synthetic data can be valuable for training AI algorithms without compromising patient confidentiality. It can also power conversational agents like chatbots and virtual assistants in healthcare settings.
These AI-driven tools can assist patients with medical advice, appointment scheduling, and personalized health recommendations, enhancing patient engagement and access to healthcare services.
Generative AI enables automated design generation that bears close resemblance to natural textures and patterns. It is ideal for domains such as architecture, fashion, and graphic design. These domains are utilizing Generative AI to create repetitive as well as bespoke designs in a fraction of a time.
Generative AI can provide personalized recommendations by analyzing user preferences and behaviors to improve user engagement, customer satisfaction, and sales conversions. It is ideal for B2C-specific industries such as retail, ecommerce, CPG, consumer durables, etc.
Generative AI can be used for AI-powered image and video generation for promotional activities such as ATL campaigns, in-store branding, social media content, and more. It can be utilized by marketing and advertising agencies, in-house marketing teams in organizations across industries.
Benefits of Generative AI
Our Resources
GenAI in Pharma: Unlocking Possibilities Responsibly
Join us for an insightful and informative webinar that delves into the possibilities and practical applications of Generative AI in revolutionizing clinical trial design, data analysis, and regulatory compliance.
Top 10 AI Development Companies in the USA
AI development companies are essential in shaping the future of technology and business. They are important because they use generative AI (GenAI), artificial intelligence (AI), machine learning, and data.
Using ChatGPT for Sentiment Analysis: A Beginner’s Guide
ChatGPT is a powerful tool, and as it continues to develop, it will likely become even more valuable for businesses. It may also be used for other applications beyond sentiment analysis. As it continues to evolve, in all probability.
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