Hexabot

Hexabot couples a visual drag-and-drop canvas with a self-hosted TypeScript runtime to execute chatbots, scheduled tasks, and autonomous AI agents. The platform connects incoming messaging channels to private databases and local or cloud language models without routing transcripts through vendor servers. That self-contained footprint ensures organizations maintain complete data sovereignty over customer inquiries and internal knowledge.

Why Hexabot instead of Voiceflow

Voiceflow centers its platform around a hosted cloud canvas where conversational designs, dialogue state, and user transcripts reside on external servers. For teams handling healthcare intake, financial support, or proprietary internal knowledge bases, hosted architectures introduce data privacy risks and compliance friction. Hexabot addresses that vulnerability by deploying entirely within your private infrastructure. Database tables, customer session logs, API credentials, and vector embeddings remain on your own servers, eliminating third-party inspection and reducing vendor risk.

The operational tradeoffs reflect this architectural difference. Voiceflow offers out-of-the-box team collaboration and cloud hosting that requires zero server administration, whereas Hexabot requires running a Node.js process and a PostgreSQL database. However, Hexabot removes the per-message fees, seat limits, and AI credit markups typical of commercial SaaS platforms. Non-technical staff can design multi-step conversational flows, trigger webhooks, and connect local language models like Ollama without worrying that increased conversational volume will inflate monthly software bills.

What it does well

• Visual flow builder: A drag-and-drop editor allows non-technical team members to create branching dialogue paths, collect user input, and display rich media without writing code.
• Unified multichannel inbox: Operators can review incoming visitor messages from web widgets, Telegram, WhatsApp, and Facebook within a single interface.
• Multilingual content management: Supports translations directly within flow settings, letting teams serve international audiences from a single workflow configuration.
• Local and private AI models: Connects to local engines like Ollama as well as external model providers, keeping sensitive prompts and responses inside your private network.
• Human handoff routing: Transfers complicated customer conversations from automated agent steps to live support agents when user inquiries require personal attention.