Hiver vs Hatz AI
Side-by-side trajectory, velocity, and editorial themes.
Hiver pivots from Gmail-only to AI-grounded omnichannel.
The recent feed shows two parallel pushes: an AI knowledge layer (Google Drive, Confluence, and Google Sheets becoming Ask-AI-queryable sources) and a channel-expansion push (Slack as a managed customer-service channel inside Hiver Omni, plus omnichannel search and automation primitives that work across email/chat/Slack). Automation gets meaningful new building blocks too — API calls as actions, new triggers and conditions.
Hiver is repositioning from 'shared inboxes inside Gmail' to 'AI-grounded omnichannel customer service platform.' The Slack-as-channel and API-call automation moves directly compete with Front, Help Scout, and the lightweight tier of Zendesk. The AI knowledge-source work is laying the grounding layer that turns Hiver AI from a reply-suggester into something closer to a tier-1 agent.
Expect a Microsoft Teams channel addition, more knowledge-source connectors (Notion, SharePoint, Salesforce KB), and a packaged 'AI Agent' tier that bundles Ask AI + grounded sources + automation actions into something that resolves tickets autonomously. Pricing for AI usage is the next question — flat seats won't survive heavy Ask-AI workloads on customer data.
Building an MSP-native AI platform with model routing, governance, and PSA integrations.
Hatz AI is rapidly assembling an MSP-targeted AI workspace: both major PSAs (ConnectWise Manage rebuilt May 15, Autotask added May 22) are now first-class, Auto-LLM routes each message to the appropriate model in real-time, and admin governance controls (credit limits per role, direct-download restrictions, custom-role AI permissions) keep pace with the capability expansion. The catalog of integrations crossed roughly 20 in six weeks, and the file-handling surface now covers 60+ extensions including audio and code archives.
Three threads run in parallel: (1) model abstraction — Auto-LLM with Lite/Performance/Turbo modes treats Hatz as a routing layer above OpenAI, Anthropic, and Google; (2) MSP-native integrations — PSA, monitoring (Sentry, Jam, Netdata), and support (Pylon, Intercom) tools landing several per release; (3) tenant governance — usage dashboards, download restrictions, role-based AI controls, copy-across-tenant operations. The product is positioning itself as the AI control plane MSPs deliver to their downstream customers, not a chat tool.
Expect more PSA-adjacent tooling (RMM platforms like NinjaOne or Kaseya are the obvious next targets) and deeper governance — likely audit log exports and tenant-level model allow-lists. The Auto-LLM routing layer will keep absorbing new models (it already onboarded Gemini 3.5 Flash and Opus 4.7 within days of release).
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