RPA uses rule-based automation to handle processes, whereas AI interprets messy, unstructured inputs to improve upon RPA.

A WhatsApp chatbot can be production-ready in 5 minutes wit ha Meta Business Account and access to a bot building framework.

SaaS companies are adapting their products to integrate with AI tools that automate their processes and orchestrate logic, improving user experiences.

Setting clear goals, building a cross-disciplinary team, and defining KPIs are important in ensuring successful chatbot deployment.

ERP workflows can be wrapped into chatbot interfaces to automate approvals, requests, and reporting in a seamless conversational flow.

A vector database stores semantic representation of texts, documents and media, letting LLMs perform semantic search.

Vertical AI Agents combine LLMs' conversational abilities with tool-calling and API access, resulting in smooth-but-powerful workflows.

Separate AI Agents are organized with one main, routing agent, to let users access many different complex, specialized workflows through a single bot.

Starting an AI agency requires considerations like find a niche, choosing a delivery system, assembling a team, and using a CRM, among others.

Chatbot APIs are cross platform ways to access chatbots through HTTP requests— which are supported by all common programming and scripting languages.

Hubspot chatbots let sales reps automate lead generation processes, organize information, and get real-time updates in a powerful CRM.

Model Context Protocol (MCP) is a protocol that optimizes real-time data access by standardizing the interface between applications and AI agents.