AI Platform

In-house orchestration. Multi-agent products.

We design how work is split, how agents collaborate, and how state is remembered. Products run on this layer. MindLog and Pulse are at prototype stage.

Talk with the team See what’s coming

Target stack

NVIDIA

PyTorch

vLLM

Ray

AWS

Docker

AI Platform

The layer the products will run on.

Multi-agent orchestration

Task split, roles, handoffs, and collaboration protocols — designed in-house.

Includes:

Role design and task decomposition
Handoffs between agents
Shared memory across a run
Protocols that stay stable as the fleet grows

Product runtime

The layer that actually runs: sessions, state, tool calls, and product actions.

Includes:

Session and conversation state
Typed tool calls
Product actions wired into the workflow
Traces you can replay

Memory and state

Context that lasts across steps, so the product starts to feel like yours.

Includes:

Working memory inside a run
Longer-lived product state
Retrieval when the next step needs it
A voice that stays consistent

Experience and safety

Guardrails, scoring, and replay — the layer that decides whether something can meet people.

Includes:

Step-level scoring
Budget and anti-exploit constraints
Replayable failure paths
Release gates on the experience

How a product is built

From one conversation to orchestration you can ship.

Every product follows the same path: split the work, orchestrate agents, connect tools and memory, add guardrails, then turn it into something people can meet.

01
Split the work
Break the product into agents, steps, and handoffs the orchestration layer can actually run.
02
Orchestrate agents
Roles, handoffs, and memory are written into our own multi-agent architecture.
03
Connect the tools
Wire outside capabilities and product actions into the workflow.
04
Guardrails and replay
Scoring, constraints, and replayable traces. Failed paths do not reach people.
05
Make it a product
The orchestration stays in the lab until the experience is ready to meet people.