kama DEI
kama.ai's Designed Emotional Intelligence, kama DEI, truly understands the meaning and human impact behind your client or user's situation or inquiry the way we as people understand each other.
Our Natural Language Understanding (NLU) technology, combined with our proprietary knowledge base, and our human value guidance algorithm supports true human-like understanding and inference behind the interactions with users. Our knowledge base content is easily 'programmed' in natural language, rated by human values, that we all understand, creating an ever expanding Virtual Agent that can answer questions for your clients, employees or other stakeholders.
Conversation journeys deliver prioritized product and service information, directly the way your product or service experts or client practitioners want to communicate it. No data scientists or programmers are required.
kama DEI Agents can 'speak' over our website chat interface, Facebook Messenger, smart speakers, or from within mobile applications. Ultimately, we help you get the right information, to the right people, at the right time, providing any-time client engagement, increasing your marketing ROI and building your brand's loyalty
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Vertex AI
Fully managed ML tools allow you to build, deploy and scale machine-learning (ML) models quickly, for any use case.
Vertex AI Workbench is natively integrated with BigQuery Dataproc and Spark. You can use BigQuery to create and execute machine-learning models in BigQuery by using standard SQL queries and spreadsheets or you can export datasets directly from BigQuery into Vertex AI Workbench to run your models there. Vertex Data Labeling can be used to create highly accurate labels for data collection.
Vertex AI Agent Builder empowers developers to design and deploy advanced generative AI applications for enterprise use. It supports both no-code and code-driven development, enabling users to create AI agents through natural language prompts or by integrating with frameworks like LangChain and LlamaIndex.
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Amazon SageMaker
Amazon SageMaker is a comprehensive machine learning platform that integrates powerful tools for model building, training, and deployment in one cohesive environment. It combines data processing, AI model development, and collaboration features, allowing teams to streamline the development of custom AI applications. With SageMaker, users can easily access data stored across Amazon S3 data lakes and Amazon Redshift data warehouses, facilitating faster insights and AI model development. It also supports generative AI use cases, enabling users to develop and scale applications with cutting-edge AI technologies. The platform’s governance and security features ensure that data and models are handled with precision and compliance throughout the entire ML lifecycle. Furthermore, SageMaker provides a unified development studio for real-time collaboration, speeding up data discovery and model deployment.
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IBM watsonx.governance
Although not every model possesses the same quality, it is crucial for all models to have governance in place to promote responsible and ethical decision-making within an organization. The IBM® watsonx.governance™ toolkit for AI governance empowers you to oversee, manage, and track your organization's AI initiatives effectively. By utilizing software automation, it enhances your capacity to address risks, fulfill regulatory obligations, and tackle ethical issues related to both generative AI and machine learning (ML) models. This toolkit provides access to automated and scalable governance, risk, and compliance instruments that encompass aspects such as operational risk, policy management, compliance, financial oversight, IT governance, and both internal and external audits. You can proactively identify and mitigate model risks while converting AI regulations into actionable policies that can be enforced automatically, ensuring that your organization remains compliant and ethically sound in its AI endeavors. Furthermore, this comprehensive approach not only safeguards your operations but also fosters trust among stakeholders in the integrity of your AI systems.
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