AI Integration for Enterprise

We use APIs (OpenAI, Anthropic, Google) for most cases. Own or fine-tuned models when there are specific privacy, cost or performance requirements.

Depends on the use case. With Leonard AI, conversational AI absorbs a large share of repetitive queries (hours, services, appointments) and frees the team for higher-value work. Before starting we define what to measure, and we measure it from day one.

Not always. For chatbots and RAG your existing documentation is enough. For predictive models, we need historical data — we help you evaluate feasibility.

Yes, when designed properly. For sensitive data we implement: on-premise models (Ollama, vLLM) that never leave your infrastructure, audit trail of every model action, human supervision on critical decisions, encryption at rest and transit. We pass compliance audits with strict DPOs.

Depends on volume, model and complexity per interaction. Running cost has two parts: LLM usage (billed per token by the provider) and infrastructure. For high volumes (thousands of queries daily), we negotiate rates with OpenAI/Anthropic. The marginal cost per interaction is a small fraction of a human one. Tell us about your case at /contact and within 24h we'll estimate what it would cost to build and run.