AI & Generative AI

LLM & generative AI applications

Retrieval-augmented generation, copilots, document intelligence and content generation grounded in your data.

Überblick

Most enterprise value from generative AI comes from combining a capable model with your own documents, records and systems. We build assistants and applications that answer questions, draft content, summarize and extract information from documents, and work inside the tools people already use.

Retrieval-augmented generation (RAG) is often at the core. We design the ingestion, chunking, indexing and access-control layers so the model only sees content the user is entitled to, and cites its sources. Where documents are complex, we combine OCR, layout parsing and structured extraction to turn contracts, forms and reports into usable data.

Quality is engineered, not assumed. We build evaluation sets from real examples, measure accuracy and groundedness on every change, and add guardrails for sensitive content, prompt injection and data leakage. We also design for cost and latency through model selection, caching and routing, so the solution stays affordable as usage grows.

Was Sie erhalten

01Solution architecture covering models, retrieval, integration and security
02Ingestion and indexing pipelines for your document sources
03Working application or copilot integrated with your systems
04Evaluation set and automated quality tests
05Guardrails, usage logging and cost controls

Technologien, mit denen wir arbeiten

OpenAI, Anthropic and Google model APIs
Azure OpenAI
Amazon Bedrock
LangChain and LlamaIndex
Vector databases such as pgvector, Pinecone and Azure AI Search
Azure AI Document Intelligence
Open-weight models such as Llama and Mistral

Wir sind technologieunabhängig: Werkzeuge wählen wir passend zu Ihren Anforderungen und bestehenden Investitionen aus.

Fragen, die wir häufig hören

How do you stop the model from making things up?

We ground answers in retrieved content, require citations and instruct the model to say when it does not know. We then measure groundedness against an evaluation set and add human review where the cost of an error is high.

Can the solution run without sending data to a public AI service?

Yes. Options include enterprise model endpoints within your cloud tenancy, private networking and open-weight models hosted in your own environment. We recommend an option based on data sensitivity, residency requirements and cost.