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Comparison · Support

Tiledesk vs Hatz AI

Side-by-side trajectory, velocity, and editorial themes.

T
Tiledesk
SUPPORT
2.5

Tiledesk's feed is agentic-AI thought leadership, not release notes

◆ Current state

The tracked feed is Tiledesk's blog, heavy on agentic-AI explainers — MCP-driven agents, self-learning support, and hybrid-search RAG. Entries read as marketing and architecture write-ups, not changelog releases, so the shipped-product state isn't directly observable. Tiledesk positions as an open-source, AI-agent customer-support platform.

◆ Where it's heading

Recent posts push an ecommerce AI sales advisor and MCP-based agents that take actions, suggesting Tiledesk is marketing toward agents that act rather than only answer. Publishing is irregular — a July post follows a months-long gap — so this reads as sporadic content, not a steady release cadence.

◆ Prediction

The messaging points toward more agentic, action-oriented and ecommerce use cases, but the actual product roadmap isn't visible until a real changelog feed replaces the blog source.

H
Hatz AI
SUPPORT
6.3

Hatz turns its MSP AI platform into an agent-composition and phone-automation system.

◆ Current state

Hatz AI is an MSP-oriented AI workspace: a governed model selector plus agents, workflows, integrations, and AI phone agents, sold through managed-service-provider tenancy. Recent releases push hard on two fronts: making phone agents a real front-line call system (routing, warm transfer, caller memory, business hours, post-call workflows) and making agents composable inside workflows. Model breadth keeps expanding, with Sonnet 5 and seven new LLMs added to the selector.

◆ Where it's heading

The direction is from a chat-with-models tool toward an automation platform where saved agents are reusable building blocks and phone agents replace human triage. Governance is a throughline: role-based model, integration, and tool controls, tenant templates, and usage budgets all deepen the MSP multi-tenant control plane. Model selection is increasingly abstracted behind Auto-LLM.

◆ Prediction

Expect further phone-agent autonomy and more agent-as-step composition across workflows, with continued MSP governance controls and ongoing additions to the model roster.

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