AI Integrations for Products — LLM, RAG & Automation
LLM integrations, RAG search, AI assistants and automated workflows for existing and new products. Production-ready AI features that work reliably at scale — 70 €/h + VAT.
Start a projectWhat we build
LLM API integrations
OpenAI (GPT-4o), Anthropic Claude, and open-source models. Prompt engineering, function calling, structured outputs, cost optimisation and latency tuning.
RAG pipelines and vector search
Retrieval-Augmented Generation that queries your own knowledge base. Document ingestion, chunking, embedding, vector storage (Pinecone, pgvector) and retrieval quality tuning.
AI assistants and chatbots
Conversational AI features embedded in your product: customer support bots, internal knowledge assistants, copilot features, and guided onboarding flows.
Automation and document processing
Automated pipelines for document classification, extraction, summarisation, translation, and structured data generation from unstructured sources.
Who it's for
- SaaS companies adding AI features to increase engagement and reduce churn
- Companies building internal tools that process or generate text at scale
- Startups building AI-native products or AI-powered workflows from the ground up
- Businesses that want to automate repetitive knowledge work without sacrificing accuracy
- Product teams that have tried AI in a demo and now need it production-hardened
Common questions
Which AI providers do you work with?
OpenAI (GPT-4o, GPT-4 Turbo), Anthropic Claude (3.5 Sonnet, Haiku), and open-source models via Ollama or HuggingFace. We select the model based on cost, latency, accuracy, and data privacy requirements for each use case.
What is RAG and when does it make sense?
Retrieval-Augmented Generation lets an LLM query your own documents or knowledge base instead of relying on its training data. It makes sense when you need accurate, up-to-date, company-specific answers from a large document corpus — and when hallucinations in answers are unacceptable.
How do you make AI features reliable in production?
LLM outputs are non-deterministic. We implement output validation, fallback handling, structured output schemas, rate limiting, cost monitoring, and eval frameworks to ensure your AI feature behaves predictably under real load — not just in a demo.
Can you audit an existing AI integration?
Yes. We review prompt design, chunking and embedding strategy, retrieval quality, latency, cost structure and reliability. We provide a written report with specific, prioritised improvements.
Ready to add AI to your product?
Tell us what you're trying to build or automate. We'll confirm approach and get back to you within one business day. 70 €/h + VAT.
Start a project