AI Development and Integration
We integrate artificial intelligence purposefully and responsibly — from strategy through model selection to production deployment.
Artificial intelligence has moved from research labs into business reality. Language models generate text, image recognition automates quality control, and recommendation systems shape purchasing decisions. The technology is powerful — but its successful deployment demands more than APIs and prompts. It demands understanding which problems AI actually solves, how to integrate it reliably into existing systems, and where its limits lie.
Parlant GmbH helps companies deploy AI purposefully: not as an end in itself, but as a tool that creates measurable value within existing business processes.
Our Approach to AI
We treat AI projects with the same engineering discipline we apply to all our software projects: clear requirements, iterative development, automated testing, and thorough documentation. Language models and neural networks are not magic — they are software components that need to be versioned, tested, and monitored.
Our focus lies on integration over experimentation. We do not build research prototypes; we build production systems. That means:
- Model selection over model training. For most business applications, existing foundation models (GPT-4, Claude, Gemini) are powerful enough. The art lies in choosing the right model for the task and designing the integration architecture around it — not in training a model from scratch.
- Turn-based agents that leverage carefully designed turn structures, tool calling, and application-controlled security boundaries to achieve human-level task completion. By decomposing complex workflows into discrete, auditable turns — each with explicit permissions and rollback points — we build agents that operate reliably within the constraints of real business environments.
- Retrieval Augmented Generation (RAG) as an architectural pattern for grounding LLM outputs in actual business data. We design RAG pipelines that combine semantic search with structured data access.
- Prompt engineering and orchestration. Well-designed prompts, output parsing, and multi-step agent architectures — these are software engineering challenges, and we treat them as such.
Technology Partners
We work with the leading inference providers — including OpenAI, Google, and Anthropic — and have integrated their technologies into both our own workflows and client projects. This hands-on experience allows us to realistically assess the strengths and limitations of each platform and make the right choice for any given use case.
Our approach follows the same principles that shape our software development: iterative, test-driven, and with a clear focus on measurable value.
Practical Examples
Myosotis Formfix — In developing the digital forms solution Formfix, we employ AI-assisted methods to process complex public-sector application forms more intelligently and optimise user experiences.
Tilores RAG — For Tilores GmbH, we contributed to integrating entity resolution into RAG architectures. Language models are combined with real-time identity resolution, enabling LLMs to deliver precise, customer-specific answers — rather than semantically similar but factually inaccurate results.
These projects illustrate how AI can be embedded into existing specialist processes without the effort and risk of a fundamental system overhaul.