AI & LLM Development: RAG Chatbots & AI Agents
Retrieval-augmented chatbots, autonomous AI agents, and LLM features grounded in your data, with the evaluation and guardrails that make them production-ready.
Most AI projects fail the same way: a slick demo that confidently makes things up the moment it leaves the happy path. The fix is not a bigger model. It is retrieval, evaluation, and guardrails, the engineering that turns a clever demo into a system you can trust in production.
We build RAG chatbots grounded in your own documents, AI agents that take real actions through tools, and LLM features wired into your product. Every build is measured, monitored, and cost-controlled, because an AI feature that hallucinates or quietly burns your budget is worse than none at all.
What We Deliver
- RAG chatbots grounded in your data
- AI agents with tool use and actions
- LangChain / LangGraph pipelines
- Vector search and embeddings infrastructure
- LLM integration (GPT, Gemini and open models)
- Evaluation, guardrails, and monitoring
Outcomes
- Answers grounded in your data, not hallucinated
- Measurable accuracy through evaluation
- Safe, monitored, and cost-controlled in production
Tech Stack
We start from your data and the questions it has to answer, then build a retrieval pipeline (chunking, embeddings, and vector search) so the model answers from your facts rather than its imagination. We add evaluation to measure accuracy, guardrails to keep it on-topic and safe, and monitoring to track quality and cost in production. We work with GPT, Gemini and open models, and we pick the right one for the job rather than defaulting to the most expensive.
- Businesses that want a support or knowledge chatbot grounded in their docs
- Teams adding AI features to an existing SaaS product
- Founders building an AI-first product or agent
- Companies that tried an AI demo and need it made reliable
TokenOS
See how an initial AI-powered Web3 product expanded into the TokenOS ecosystem.
AI & LLM questions, answered
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