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Data and Model Engineering Services | iOPEX
Build an AI infrastructure to streamline data silos, fine-tune, test, manage, maintain, and govern smaller models that are cheaper to run along with mega models is the way forward for deploying enterprise-wide GenAI solutions.
Combining unified data with the power of AI models is an important phase in "Byte-Size" Total Digital Transformation journey to amplify the outcomes in enterprises. Data and Model Engineering is one of the key steps to build AI-embedded actions in business systems to enhance customer and employee experience. Enabling organizations to adeptly manage the nuances of integrating responsible AI, placing a high priority on security, compliance, and ethical development are imperatives.
Make AI Regulatory for all : Simplifying AI Compliance & Navigating Regulations for enterprise wide adoption
Protecting Enterprise Data :Empowering Secure AI Adoption in Regulated business functions and domains
Continuous AI Oversight : Ensure Compliance with appropriate Guard Rails, Model Monitoring and Feedback
Comprehensive Reporting: Promoting Transparency and Trust with Comprehensive AI Reporting
Our proprietary AI Engine and framework "elevAIte" plays a crucial role in aggregating, curating, detecting and enforcing right datasets are fueled to the AI models. From the perspective of ensuring Responsible AI, we uncovers potential risks, compliance & privacy challenges relating to data and provide options to govern, mitigate, and remediate continuously use ML-Ops.
Integrate, curate, & train your enterprise data into AI models, lay the foundation for building the infrastructure for AI.
Leverage best-of-bread foundation models / seamlessly integrate with the leading AI models like OpenAI etc., enabling the foundation for a strong enterprise-wide AI adoption.
Adapt best-in-class foundation models / incorporate custom models with Reinforcement Learning from Human Feedback (RLHF) & Prompts. Continuously improve context & relevance for predictable outcomes.
Build and deploy next generation applications leveraging Gen-AI framework to dip into the power of your enterprise data source.
Unlock your data’s full potential to fuel AI by efficiently managing and refining organizational datasets.
Continuously fine-tune base models using enterprise data for enhanced performance. Streamline experiment comparisons across multiple LLMs, prompts, and strategies and effortlessly deploy the most promising variants for consumption.
Private Long-Term Memory
Fine-Tuning
Reinforcement Learning from Human Feedback (RLHF)
Prompt Engineering
Improving network service management, enhance customer services via case summarization and agent assistances.
Improving Mean Time to Resolve (MTTR) and predictability for Technical support and Technology operations through K'base optimization and utilization
Personalizing brand-based content and campaign experiences and increase sales based on past performance and creative insights
Enhancing patient outcomes and streamlining operations by converting data to best step actions and feed operators
Optimize production processes and improve compliance & quality control via information summarization and smart searches
Customized and fine-tuned models lead to improved accuracy, reducing errors and ensuring more reliable AI-driven solutions.
Tailored models and prompt engineering can give your organization a competitive edge by offering more effective and responsive AI solutions, setting you apart from competitors.
Well-engineered models and prompt-driven interactions are scalable, allowing businesses to handle growing volumes of data and user interactions without sacrificing performance or quality.
Customized AI interactions and user-centric prompt engineering lead to better user experiences, higher engagement, and increased user satisfaction, all of which can positively impact customer loyalty.
Streamline operations by optimizing data retrieval processes, reducing manual intervention, and improving overall efficiency to free up resources and reduce overhead.
Optimized models and AI systems are more efficient, requiring fewer resources and reducing operational costs.
