GENERAITR Engine

Conversation is becoming the natural interface for creative work, whether that's a person working alongside an AI chat to make something, or an agent acting entirely on its own. GENERAITR Engine is built for that shift from day one: the MCP surface isn't bolted on as an afterthought, it's the execution model itself. Workflows are declarative step lists, not a graph built for a mouse, so an agent, or the LLM a person is already talking to, can solve a problem the way it judges best.

This isn't only about automation. Most professionals already have a favorite LLM they think and work through every day. The natural next step is giving that assistant direct access to the best generation models, not through a separate app with its own login, but through the interface they're already in, and doing it compliantly, with compute that stays inside the EU.

The core is deterministic and LLM-free by design, agents live outside it (that's TAOS's job), Engine just executes safely and predictably. That separation is what makes it infrastructure, not another opaque AI black box.

Above "just call a model API": a per-model knowledge bank of prompt grammar and parameter semantics, translation from creative intent to concrete parameters, upfront cost/time estimation, and guardrails, EU AI Act risk classification and content checks, that run before a job is ever queued.

Behind one interface, Engine routes across paid aggregators (fal.ai, Runware, Eden AI, AIMLapi) and self-hosted open-weight models on scale-to-zero GPU infrastructure at Verda, an EU provider, with three configurable warmup modes trading cost against latency. It already powers GENERAITR's SaaS but runs fully standalone. The bet: the next primary caller of generation infrastructure is a conversation, not a mouse, Engine is built for that world from first principles.