INNOFAME combines project collaboration, file context, conversational execution, sandbox runtime, and asset reuse to embed AI into enterprise R&D and testing workflows, with enterprise governance, long-term memory, and multi-agent collaboration for end-to-end automation.
Tasks, files, assets, and execution processes are managed around projects.
Each project can link multiple workspaces made of an Agent chat area and file area.
Large tasks can be split into independent but collaborative workspaces.
Upload, download, create, store, and preview multiple file formats.
The file area works like a standard workspace directory and gives Agents direct operating context.
Users can organize inputs, inspect intermediate outputs, and keep Agent-generated content connected.
Users start tasks, add constraints, and adjust goals through natural language.
The chat area carries intent, context continuity, and result feedback.
Manual processes are transformed into instruction-driven human-AI collaboration.
Each workspace runs in an independent virtual-machine sandbox.
Agents can run commands, scripts, file operations, and automation tasks safely.
Sandboxing supports stable parallel tasks as well as debugging, reproduction, and traceability.
Asset projects manage Skills, Flow, and knowledge bases in a unified way.
Assets can be created, edited, iterated, and published to the asset platform.
One-off capabilities become standard modules shared across projects.
Reusable capabilities are packaged as file collections with prompts, scripts, and business resources.
Skills inject domain knowledge, task logic, and execution capabilities into Agents.
Generic Agents can quickly become business Agents with consistent output quality.
Multiple Agents collaborate in a unified process.
Planner handles planning, scheduling, progress, and acceptance; Workers form quality loops.
Strongly dependent multi-stage tasks are decomposed into structured collaborative processes.
TC Power accumulates high-quality data during real task execution for model evaluation, pre-training, SFT, RLHF, and red-team testing.
SFT and evaluation
Behavior cloning and error analysis
RLHF preference signals
End-to-end evaluation
Red-team testing
High-quality prompts and responses teach the model expected output formats and behavior patterns.
Engineer preference signals train reward models and optimize model outputs toward human expectations.
Adversarial probing actively identifies and fixes model limitations and safety risks.
