Production-Ready AI Requires More Than a Model
The first prototype is built quickly. What comes next is what matters.
Many companies are testing large language models, copilots, and AI agents. The real work begins after the initial demo success: 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.
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.
For us, 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 mit KI für komplexe Forschungsrecherchen
HMS entwickelte für einen globalen Chemiekonzern ein RAG-basiertes Chatbot-System, das komplexe Forschungsrecherchen über natürliche Sprache unterstützt und Antworten mit Quellenbezug nachvollziehbar macht.
Text-to-SQL: Unternehmensdaten per Chat abfragen
HMS entwickelte für eine internationale Finanzinstitution eine Text-to-SQL-Lösung, die quantitative Geschäftsfragen in Datenbankabfragen übersetzt und Ergebnisse interaktiv aufbereitet.
Rollenbasierter Dokumenten-Chatbot mit Agentic RAG
HMS entwickelte für einen global tätigen Pharmakonzern einen Chatbot, der interne Dokumente rollenbasiert durchsucht und relevante Inhalte über eine KI-gestützte Oberfläche nutzbar macht.
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. The handover to operations also remains part of the project.
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.
