Production-Ready AI Requires More Than a Model
A first prototype can be built quickly. What happens next is what really matters.
Many companies are testing large language models, copilots, and AI agents. The real work begins once the initial demo has proven successful: data quality, permissions, interfaces, user guidance, evaluation, costs, and operations must all align.
HMS helps companies evaluate GenAI and agentic AI solutions from a business perspective and implement them in production environments. To do this, we bring together expertise in AI engineering, software engineering, data engineering, and architecture within a single project team.
GenAI and Agentic AI Solutions Require a Common Foundation
- relevant business knowledge
- appropriate data and models
- clearly defined business processes, as well as
- controlled interfaces to existing IT systems.
Only by combining these elements can AI applications deliver practical business value while remaining traceable in production.
Technology Selection
The Use Case Determines the AI Approach
AI consulting begins with the question of which approach best meets the business, technical, and economic requirements. Generative AI is suitable for some tasks, while others are better suited to traditional machine learning models or rule-based software.
At HMS, AI consulting and implementation go hand in hand.
Architecture, AI engineering, software engineering, and data engineering are all intertwined in the implementation process. We make technology decisions independently of vendors, with a focus on integration capabilities and long-term maintainability.
Enterprise Search with AI for Complex Research Queries
HMS developed a RAG-based chatbot for a global chemical company. The system handles complex research queries in natural language and delivers traceable answers with source citations.
Text-to-SQL: Retrieve Company Data via Chat
HMS developed a Text-to-SQL solution for an international financial institution that translates quantitative business questions into database queries and presents the results in an interactive format.
Role-Based Document Chatbot with Agentic RAG
HMS developed a chatbot for a global pharmaceutical company that searches internal documents based on user roles and makes relevant information accessible through an AI-powered interface.
Your Partner for Data & AI Engineering
AI Consulting with Responsibility for Integration and Operations
HMS takes responsibility for technical decisions in AI projects. We document architectural decisions for traceability and take security, governance, and production requirements into account right from the design phase.
Our teams handle consulting, architecture, and technical implementation. Our responsibility also extends to the transition into production operations.
A copilot typically assists users within a specific task or interaction. Agentic workflows can also retrieve data, use tools, and coordinate multiple steps. Which tasks can be automated depends on the context of use and the defined permissions.
No. When there are several ideas on the table, the AI potential analysis can help with evaluation and prioritization. The goal is to establish a clear basis for prioritization and next steps.
AI integration is planned based on existing data sources, APIs, line-of-business systems, and authorization models. HMS develops the necessary components and interfaces to align with the target architecture.
Role-based permissions, approvals, logging, and escalation procedures are defined in accordance with the risk and operational context. Human-in-the-loop steps are retained in situations where decisions are not to be delegated to an AI system.
