AI integration, agents, and RAG, built for production
AI integration means adding language models to your product where they genuinely help, like smart search, a copilot, or an agent that handles the busywork, and doing the careful engineering that keeps them dependable once real users arrive. Orchidix builds custom agents, RAG systems over your own data, and the evals and guardrails that keep them accurate.
Agents that take real actions in your product, and copilots that help your users and team move faster.
Retrieval-augmented generation grounded in your knowledge base, so answers come from your content instead of being guessed.
Test suites that score accuracy on real examples and guardrails that catch bad or low-confidence outputs before your users do.
Route each request to the right model, OpenAI, Claude, or open-source, to balance quality against cost.
- ◇Model-agnostic: OpenAI, Anthropic Claude, or open-source (Llama, Mistral).
- ◇Human handoff whenever the model is not confident.
- ◇Built by an ex-Oracle AI/ML engineer who shipped production LLM systems.
Customer-service automation on AI agents and a RAG pipeline.
AI that matches people with the right therapist.
Vote-driven model routing for a generative-media platform.
What is a RAG system?
RAG (retrieval-augmented generation) connects a language model to your own documents or data. When someone asks a question, the system retrieves the most relevant content and the model answers from it, so responses are grounded in your knowledge base instead of guessed. It is how we keep AI answers accurate and current.
Can you add AI to a product we already have?
Yes. Most of our AI work is integrating agents, search, or copilots into software you already run, rather than starting over. We scope the integration during discovery so it fits your codebase and holds up in production.
How do you keep AI answers accurate?
We ground answers in your data with RAG, add evals that score accuracy on real examples, set guardrails for unsafe or low-confidence outputs, and hand off to a human when the model is unsure.
Which AI models do you use?
We are model-agnostic. We use OpenAI, Anthropic Claude, or open-source models like Llama and Mistral, and often route between them per request to balance quality and cost.