Adopting AI at scale can be difficult. Enterprises around the world are discovering the pace of AI deployment is frustratingly slow as they face implementation, integration, and customisation challenges. Generative AI is undoubtedly powerful, but it can be complex, particularly for businesses starting from scratch.
To help organisations overcome the hurdles associated with AI adoption, Reply has introduced ‘Prebuilt’ AI apps designed to reduce the time, technical expertise, and risk required to deploy AI solutions.
Prebuilt AI Apps aim to streamline a business’s access to information, boost operational efficiency via smart conversational interfaces and automated workflows, and improve the quality of decision-making. Enterprises rely on such apps for fast and sustainable adoption, limiting the need for human intervention, especially during the earliest stages of AI implementation.
Reply’s Prebuilt AI apps are “ready-to-use agentic systems” that can be applied in different business contexts. One such example would be simplifying CV updates and producing dynamic skills maps that identify and connect an employee’s competencies, helping HR teams view their strengths more clearly and support staff career development.
Each Reply Prebuilt AI app can be customised and/or extended to better suit a company’s workflows and data, while the organisation can remain in complete control of regulations, privacy, and compliance.
Claim Digital Agent
Claim Digital Agent automates data extraction from medical documents. It helps construct a “personalised knowledge base,” speeding up claims processing and health insurance operations. To process health insurance claims, several documents typically need to be reviewed, including medical reports and invoices, which differ in format and quality. The process can be slow, prone to errors, and less sustainable if the volume of claims increases.
Claim Digital Agent uses a document processing system that employs AI and Optical Character Recognition (OCR). Businesses can extract important information from unstructured documents, sort claims by type, and convert data into standardised formats. It creates a personalised knowledge base that improves with each claim processed. Claim Digital Agent uses document pre-processing, AI-driven extraction, enhanced OCR, classification, knowledge graph construction, and a validation layer for accuracy checks.
AI-powered HR assistant
HR Assistant is designed to deliver employee support 24/7 via conversational AI technology, HR system integration, and knowledge management. The self-service assistant understands several languages and is aware of context. It helps employees find information more easily, complete tasks, and access HR services without having to navigate more traditional complex systems or wait for staff assistance.
Reply uses AI which allows the assistant to give personalised answers based on an employee’s role and location. It operates with various HR systems, like ServiceNow and SharePoint. HR teams can use the app to manage their knowledge base and update or refine documents without needing technical skills.
Internal Knowledge Optimiser
Internal Knowledge Optimiser transcribes video into an “interactive knowledge base for fast, contextual answers via natural language.” Webinars, videos, meetings, and documents can go unused in many companies, creating a backlog of unstructured content that can be hard to access and add to its disuse. AI can be used to extract useful insights from data. Reply’s Internal Knowledge Optimiser uses AI to convert unstructured content into a coherent knowledge base, allowing users to ask natural language questions to gain information from complex content. The system combines different search methods.
Reply hopes its Prebuilt AI apps will help change AI adoption in enterprises in many sectors. They provide practical and sustainable pathways from experimentation to scaled adoption, and are helping businesses turn AI into scalable solutions that deliver measurable impact.
(Image source: “Skyscrapers in the Storm” by Trey Ratcliff is licensed under CC BY-NC-SA 2.0.)
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