Adaptation Guidance

Actionable strategies to improve AI performance through targeted changes, not just retraining.
We identify why your model is underperforming: pinpointing inputs, logic gaps, or usage patterns that lead to failure.
We simulate potential interventions (prompt tweaks, logic changes, or additional training) before you invest engineering time.
We provide a clear sequence of options, from quick wins to strategic overhauls, based on your system’s current behavior.

Our Process

We analyze failure patterns and success drivers across your model’s outputs to determine how to improve—not just whether.
Instead of defaulting to retraining, we explore alternate adaptation strategies like prompt engineering, feature gating, decision-layer adjustments, or fine-tuning.

We tailor our guidance to your technical constraints and business priorities, ensuring every change moves the needle.

Sample Timeline

  • Week 1: Error audit and review of current system setup
  • Week 2: Scenario modeling of proposed adaptations
  • Week 3: Stakeholder workshop to review tradeoffs
  • Week 4+: Delivery of prioritized roadmap with implementation support options
  • Sample Deliverable

  • Adaptation Guide including:
    • Root cause analysis with annotated error clusters
    • Simulated performance of proposed interventions
    • Tiered roadmap (e.g., low-effort, medium-investment, structural)
    • Supporting charts and guidance for implementation teams
  • Optional: Ongoing feedback loop post-implementation
  • Get started with
    Adaptation Guidance
    Root Cause and Error Pathway Analysis
    Scenario-Based What-If Evaluation
    Prioritized Adaptation Roadmaps
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