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Research

Research for
safe AI improvement

Connect agent execution, data generation, training, and safety verification, so experience from real tasks feeds the next round of learning

01 / Data Intelligence

Turn failures into
the next improvement

AI needs the right data for what it fails at next

We identify capability gaps in real-world agent trajectories and turn them into targeted, high-value training data. The model’s own limits become the signal for what to learn next

  • Real-world agent trajectories
  • Automated failure discovery
  • Targeted training data
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Anifold’s Data Intelligence story scene

02 / Continuous Learning Infra

From one training run
to continuous learning

Data becomes capability when it returns to training

Agent execution, evaluation, data generation, training, and model updates belong in one connected system. Every iteration becomes the starting point for the next

  • Connected execution and evaluation
  • Data-to-training orchestration
  • Continuous model updates
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Anifold’s Continuous Learning Infra story scene

03 / Safe RSI

Get stronger
Keep the boundaries

New capabilities can bring new risks

We embed safety evaluation, automated red teaming, and safety constraints into model iteration. Each update is evaluated against its deployment boundaries before it enters the next cycle

  • Safety evaluation
  • Automated red teaming
  • Deployment constraints
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Anifold’s Safe RSI story scene

SAFE RECURSIVE SELF-IMPROVEMENT

Bring experience back to learning
Verify the next step

Data guides what to learn. Infra connects each iteration. Safety sets the conditions for returning

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BUILDING SAFE RSI SYSTEMS

Build the
next iteration

Bring research, engineering, and safety together for continuous improvement

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