Easiio helps companies design, build, and deploy AI products such as LLM-powered chatbots, RAG knowledge assistants, AI agents, and automation workflows that integrate with real business systems.

An LLM chatbot uses large language models to understand intent, answer questions in natural language, and generate helpful responses. It can be combined with tools and company knowledge to complete real tasks.

RAG lets a chatbot retrieve relevant information from your documents and knowledge bases before generating an answer. This reduces hallucinations and keeps responses grounded in your approved sources.

Yes. We can ingest content such as PDFs, Word/Google Docs, Confluence/Notion pages, and help center articles, then build a retrieval pipeline so the assistant answers using your internal knowledge base.

We use grounded retrieval (RAG), citations when needed, prompt and tool-guardrails, evaluation test sets, and continuous monitoring so the assistant stays accurate and improves over time.

Yes. We can implement role-based access control, permission-aware retrieval, audit logging, and deploy in your preferred environment including private cloud or on-premise, depending on your compliance requirements.

AI engineering is the practice of building production-grade AI systems: data pipelines, retrieval and vector databases, model selection, evaluation, observability, security, and integrations that make AI dependable at scale.

Agentic programming lets an AI assistant plan and execute multi-step work by calling tools such as CRMs, ticketing systems, databases, and APIs, while following constraints and approvals you define.

Multi-agent systems coordinate specialized agents (for example, research, planning, coding, QA) to solve complex workflows. It is useful when tasks require different skills, parallelism, or checks and balances.

Common integrations include websites, WordPress/WooCommerce, Shopify, CRMs, ticketing tools, internal APIs, data warehouses, Slack/Teams, and knowledge bases. We tailor integrations to your stack.

Timelines depend on data readiness and integrations. Many projects can launch a first production version in weeks, followed by iterative improvements based on real user feedback and evaluations.

We track metrics such as resolution rate, deflection, CSAT, groundedness, latency, cost, and failure modes, and we use evaluation datasets to validate improvements before release.