Forethought vs Hatz AI
Side-by-side trajectory, velocity, and editorial themes.
Forethought pivots from answering questions to executing outcomes via Orchestrator and Browser Agents.
Forethought is in the middle of a deliberate platform-narrative shift. April shipped two foundational pieces: Orchestrator, which routes business signals into deterministic AI actions across channels, and Browser Agents, which can take actions in apps that don't expose APIs. Test Suite landed alongside as the validation tooling for agent behavior before deployment. The CEO's 'Next Chapter' post frames the same direction in plain language: AI moving from answering to resolving.
The company is repositioning from a customer-support intent and triage AI to an outcomes-execution layer for enterprise customer experience. Browser Agents are the bet that the long tail of CX work lives in apps without proper APIs — making the agent capable of clicking through them is the moat. Orchestrator and Test Suite are the deterministic-control and validation pieces that make this defensible enough for enterprise procurement.
Expect a tightening of the integration story — pre-built Browser Agent flows for common CX systems like Zendesk and Salesforce Service Cloud — and an explicit outcomes-priced packaging emerging over the next quarter as the company moves past per-seat or per-resolution pricing.
Hatz AI is building the AI workspace for MSPs — per-message model routing, tenant tooling, custom MCP.
Hatz AI is shipping at a high cadence across three connected themes. First, model routing: Auto-LLM picks the right model per message based on task and tools, then layered into Lite, Performance, and Turbo tiers; the catalog keeps adding models (Opus 4.7, Gemini 3.5 Flash, Gemini 3.1 Flash Lite, Gemma 4) with per-model credit multipliers surfaced in the UI. Second, MSP control plane: bulk tenant creation via CSV, custom roles with credit limits, workshop access controls, and embedded support chat in the admin dashboard. Third, surface expansion: audio uploads with auto-transcription, image generation in workflows, file output attaching to chats, 60+ supported file types, speech-to-text in chat, and a steady cadence of integrations and custom MCP server improvements.
The product is taking shape as a multi-tenant AI workspace tuned for MSPs and partner-led delivery — the tenant CSV, credit limits, and workshop sharing are unusual for a generalist AI tool and tell you who buys this. Auto-LLM and tiered routing make sense in that context: an MSP needs cost control across many tenants without micromanaging model picks. Custom MCP and the broad integration cadence position Hatz as a tools-aggregator over multiple LLMs rather than a model wrapper.
Expect more MSP-centric controls — per-tenant budgets, white-label theming, billing reconciliation — and Auto-LLM to grow visible routing telemetry so MSP admins can see why a given model was picked. The custom MCP surface is likely to evolve toward a marketplace pattern with shareable MCP packages across tenants.
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