Version 3 concept / Use-case first

One runtime. Many AI businesses.

On IronHeart.AI, companies can build AI employees, expert clones, companions, teachers, sales agents, call-center agents, and physical AI systems without rebuilding the runtime layer from scratch.

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Voice is the interface The runtime is the company.

V3-B

Real use cases, tested outside the lab.

Each case looks different on the surface. Underneath, they all need the same system: voice, memory, context, tools, knowledge, orchestration, and deployment.

AI employee

Operations agent

Voice-first AI workers for customer communication, internal operations, sales follow-up, and workflow execution.

Expert clone

Scalable judgment

Educators, doctors, coaches, and consultants can scale knowledge, voice, and recurring consultations.

AI companion

Relationship product

Persistent personalities that remember context, build continuity, and monetize conversations.

Call center

Realtime sales agent

Assist or automate customer calls, transcribe conversations, summarize outcomes, and trigger CRM workflows.

AI teacher

Education runtime

Classroom robots, language tutors, exam simulations, student memory, and multilingual tutoring.

Physical AI

Robots and kiosks

Humanoid robots, event hosts, brand ambassadors, retail assistants, and public-facing embodied AI systems.

Why this matters

Every case runs on the same infrastructure.

A robot teacher, call-center agent, expert clone, AI companion, and enterprise assistant are different businesses. But under the surface they all need persistent memory, realtime voice, context continuity, agent orchestration, knowledge retrieval, and deployment infrastructure.

IronHeart Runtime
├── realtime_voice_runtime
├── memory_engine
├── knowledge_processor
├── agent_orchestration
├── edge_runtime
└── physical_ai_device_layer

Product pages behind the story.

This variant leads with outcomes, then lets technical buyers go deeper into each infrastructure module.

Core

Start with the businesses. Reveal the infrastructure underneath.

This variant is better for non-technical visitors. It makes the market feel large before asking them to understand the platform architecture.