Build Enterprise AI Platform for Today and Tomorrow
Our robust, stable and trustful platform enables to deploy production-grade AI solutions that deliver sustainable value in real-world environments, boosting the success rates of AI projects. Our AI solutions are tailored to each customer’s data, business needs and use cases, and are delivered at unprecedented speed. The ability to quickly add self-adaptive, hyper - customized solutions on top of our platform enables our customers to scale up their AI transformation with con dence Self serving AI system at scale even for national wide deployment is our forte.
Business can expect from our digital transformation expertise
Our team of experienced engineers can help you with every step of the AI process. We will work with you to identify best-of-breed hardware, software, and expertise in digital security for your project. Together we can take on a tough challenge and operationalize an AI solution at scale. We'll accelerate your time to AI operationalization and industrialization while meeting your budget goals.
Our expertise understand to its core how the customer experience is significant for a business, therefore our poise of the customers digital transformation activity stems from customer experience. The customer experience plays a crucial role in digital transformation. Many digital transformation initiatives arise from pain points, business/innovation needs and growth/transformation imperatives on the customer (experience) side of business. An example that has already become classic is the customer who questions the seller within a physical store if he can discount certain merchandise because the customer has just seen on the internet that the competing store sells the products cheaper. Thus, sales channels, ways to advertise products or services and attract and engage consumers, points of contact, language and even prices and product and service attributes are affected by digital transformation, as the public, increasingly, becomes immersed in the virtual universe and feeling very happy inside of it.
- Vision
- NLP
- Speech
- Conversational AI
- Bots
The five layers of an enterprise AI platform work together to enable the use of today’s AI capabilities and set the stage for incorporating tomorrow’s as well.
Experience layer
Intelligence layer
Operations & deployment layer
Experimentation layer
Material Design
1. Data & integration layer
The Data & Integration Layer gives access to the enterprise data. This data access is critical because in AI, developers are not hand-coding the rules. Rather, the machine is learning the rules based on the data it has access to. Data components also include data transformation and governance elements to help manage data repositories and sources. Data sources may be wrapped in services that make it possible to interact with the data at an abstract level, providing a single reference point to an existing platform data ontology. Most importantly, the data must be of good quality, and the AI scientists must be able to build the data pipelines they need without dependence on the IT team—ideally through easy self-service—so they can experiment as needed.
2. Experimentation layer
The Experimentation Layer is where AI scientists develop, test and iterate their hypotheses. A good experimentation layer brings automation for feature engineering, feature selection, model selection, model optimization and model interpretability. Idea management and model management are both key to empowering AI scientists to collaborate and avoid repetition
3. Operations & deployment layer
The Operations & Deployment Layer is important for model governance and deployment. This is where the model risk assessment is conducted so that the model governance team or model risk office can validate and see proof-of-model interpretability, model bias and fairness, and model fail-safe mechanisms. The operations layer includes the results of experiments by AI DevOps engineers and system administrators. It offers tools and mechanisms to manage the ìcontainerizedî deployment of various models and other components across the platform. It also enables the monitoring of model performance accuracy.
4. Intelligence layer
The Intelligence Layer powers AI during run time, with training-time activities handled in the experimentation layer. It is the product of technical solutions and product teams working in conjunction with cognitive experience experts. The intelligence layer can expose both re-useable components, such as low-level service APIs, to intelligent products that are composite orchestrations of many low level APIs. Central to the orchestration and delivery of intelligent services, the intelligence layer is the primary resource for directing service delivery and can be implemented as simply as a fixed relay from requests to responses. Ideally, however, it is implemented using such concepts as dynamic service discovery and intent identification to provide a flexible response platform that enables cognitive interaction despite ambiguous directions.
5. Experience layer
The Experience Layer engages with users through technologies such as conversational UI, augmented reality and gesture control. AI platforms are a growing area encompassing the components that enable the visual and conversational design work for a solution. It is generally owned by a cognitive experience team with traditional user experience workers, conversational experience workers, visual designers and other creative individuals who craft rich and meaningful experiences enabled by AI technology