Generative AI Development Service.

Shaping India's AI Frontier.

Conversations with Innovation:
Harnessing Generative AI for Comprehensive Business Solutions

Language-driven innovation is revolutionizing the way businesses interact with data, eliminating complexities and the steep learning curve associated with traditional query systems. Simply pose your queries in natural language, and let our generative AI and LLM technologies work their magic. At Edubild AI, we offer both public and customized models through our consulting services, enabling businesses to craft dynamic narratives, automate processes, and create seamless conversational experiences using their unique datasets.

Generative AI: Redefining Industry Standards

If you’re poised to unlock the vast potential of Generative AI, you’ll discover key applications across all industries: enhancing customer experiences, leveraging enterprise knowledge, and optimizing processes.

1. Customer Experience Offerings

In the realm of customer experience, 81% of companies view CX as a critical differentiator. The adoption of Generative AI transforms this landscape, moving beyond the limitations of traditional chatbots. Our AI-driven solutions understand and adapt to user preferences in real-time, delivering hyper-personalized interactions. This capability allows for handling complex queries and providing tailored experiences, enhancing customer satisfaction significantly.

2. Enterprise Knowledge Offerings

For businesses, data is a treasure trove of knowledge, driving innovation and competitiveness. Generative AI revolutionizes how enterprises harness this data, creating structured knowledge bases that enhance semantic search capabilities and decision-making. Our systems use advanced NLP to deliver precise, context-aware insights, empowering users with actionable information tailored to their specific roles and historical interactions.

3. Process Optimizer

Generative AI is pivotal in optimizing organizational processes by analyzing data and performance metrics to pinpoint inefficiencies and suggest improvements. It utilizes machine learning, NLP, and computer vision to transform raw data into strategic insights, offering scenario analysis and predictive capabilities that enable proactive decision-making and strategic planning.

Generative AI development and large language model services

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LLM Business Integration in India

Utilize generative AI models to supercharge your decision-making and operational efficiency. Gain a competitive edge with insights derived from advanced AI analytics.

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Vector Semantic Search Technologies

Utilize vector-based semantic searches and robust NLP technologies to understand user intentions accurately, enhancing search functionality without strict keyword dependencies.

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Generative AI Development for Enhanced Productivity

Boost developer productivity with innovative AI tools designed to streamline coding processes and enhance workflow efficiency across tech industries.

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Autonomous Agent Development

Deploy self-learning autonomous AI agents that independently adapt and optimize daily operations, reducing manual overhead and increasing efficiency.

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Text Generation for Communication

Employ generative AI to craft detailed, contextually relevant content that boosts communication and streamlines operational procedures.

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AI Model Fine-Tuning Services

Customize and enhance your AI model outputs to meet specific organizational needs, significantly improving performance while cutting down on development time.

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Creative Image Generation

Quickly access a diverse range of AI-generated images to support creative projects, significantly reducing time and costs associated with traditional content creation.

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Reinforcement Learning from Human Feedback (RLHF) in AI

Enhance your AI models with adaptive reinforcement learning informed by human feedback, improving the accuracy and efficiency of AI responses.

LLMs and AI models we use

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VERTEX AI

Google’s experimental conversational AI service powered by LaMDA. It fetches data from the web to offer fresh, high-quality responses.

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OpenAI

GPT 3.5,4 the trailblazer of AI language models, continues to inspire and innovate with its remarkable ability to understand and generate human-like text.

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LLaMA 2

Staight from Meta, It’s next generation of open source large language model, trained on 40% more data than LLaMA 1. It is fine-tuned for dialog, called LLaMA-2 Chat.

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MISTERAL AI

A cutting-edge conversational AI platform. It excels in understanding and generating human-like text, offering users high-quality responses and dynamic interactions.

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HUGGING FACE AI

Like GPT models and Google's LaMDA, it excels in human-like text generation, delivering quality responses and interactions, epitomizing NLP tech.

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STABLE DIFFUSION

Like GPT models and Google's LaMDA, it excels in human-like text generation, delivering quality responses and interactions, epitomizing NLP tech.

Technology Stack

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EDUBILD TECHNOLOGIES LLP

Edubild is a dynamic ed-tech & hr-tech startup leveraging the power of AI to bridge the gap between academia and the professional world for students.

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Generative AI:
Applications Across Key Indian Industries

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FinTech

  • Enhance Customer Experience: Provide AI-driven personalized financial advice and automated customer service.
  • Leverage Enterprise Knowledge: Utilize AI to analyze large volumes of financial data for better investment and risk management decisions.
  • Optimize Processes: Automate fraud detection and claims processing using AI algorithms.
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EdTech

  • Enhance Customer Experience: Develop AI-driven personalized learning experiences and adaptive testing.
  • Leverage Enterprise Knowledge: Analyze student data to tailor educational content and identify learning gaps.
  • Optimize Processes: Streamline course design and grading systems through AI automation.
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Healthcare

  • Enhance Customer Experience: Use AI to offer personalized treatment plans and virtual health assistance.
  • Leverage Enterprise Knowledge: Implement AI for predictive diagnostics and patient data analysis.
  • Optimize Processes: Automate administrative tasks like patient scheduling and medical record keeping.
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Manufacturing

  • Enhance Customer Experience: Custom manufacture products based on AI analysis of customer preferences.
  • Leverage Enterprise Knowledge: Use AI for predictive maintenance and quality control.
  • Optimize Processes: Automate production lines and optimize supply chain management.
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Retail and eCommerce

  • Enhance Customer Experience: Personalize shopping experiences with AI-powered recommendation systems.
  • Leverage Enterprise Knowledge: Analyze customer behavior data to optimize marketing strategies.
  • Optimize Processes: Improve inventory management and logistics with predictive AI models.
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Telecom

  • Enhance Customer Experience: Enhance customer service with AI-driven support systems and personalized offers.
  • Leverage Enterprise Knowledge: Use AI to manage and analyze network traffic data for improved service quality.
  • Optimize Processes: Automate network operations and maintenance tasks.

Our Trusted Development Process

1
Consulting Phase

Objective: To understand client-specific needs and the potential of LLMs to meet those needs.

Initial Client Meeting: Engage with the client to explore and define potential use-cases relevant to their business.

Evaluation: Assess the client's existing content and data infrastructure to identify opportunities for LLM enhancement.

2
Identifying Scope

Objective: To define and confirm the feasibility of the LLM solution for the client’s issues.

Data & Content Assessment: Review the client’s data types and volumes to ensure alignment with LLM capabilities.

Problem Definition: Clearly define the client's objectives and expectations from the LLM deployment.

Approach Selection: Choose the most suitable LLM frameworks and tools based on the client’s needs.

Ethical & Bias Evaluation: Assess and plan for any potential biases or ethical concerns related to LLM use.

3
MVP (Minimum Viable Product)

Objective: To implement and validate the effectiveness of a basic LLM solution.

Setup: Integrate the LLM into the client’s environment using appropriate platforms or APIs.

Prototype Development: Develop a simplified version of the solution tailored to the defined use-case.

Initial Testing: Conduct tests to evaluate the accuracy, relevance, and reliability of the LLM outputs.

Feedback Collection: Gather and analyze feedback from end-users or stakeholders to gauge the solution’s impact.

4
End-to-end Development

Objective: To develop a full-scale, comprehensive LLM solution.

Fine-Tuning: If permitted, optimize the LLM with specific data sets to enhance performance.

Integration: Seamlessly incorporate the LLM into the client’s existing digital ecosystems.

UI/UX Development: Design user interfaces that enhance interaction with the LLM, ensuring a positive user experience.

Comprehensive Testing: Perform extensive testing to identify and correct any issues, focusing on output accuracy and potential biases.

5
Scaling

Objective: To expand the LLM’s application scope and user base.

Infrastructure Enhancement: Upgrade infrastructure to support increased data and query volumes.

Parallel Processing: Implement methods to manage multiple simultaneous LLM requests efficiently.

Deployment Strategies: Determine optimal deployment solutions (cloud vs. on-premises) based on scalability needs and client preferences.

6
Maintenance

Objective: To provide continuous support and updates for the LLM solution.

Continuous Monitoring: Regularly monitor the system to ensure optimal performance.

Regular Updates: Implement necessary updates and adjustments to the LLM based on new technology developments and client feedback.

User Feedback Loop: Establish a continuous feedback mechanism to improve and adapt the LLM solution over time.

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