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.

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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

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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

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Get started with

Adaptation Guidance

Root Cause and Error Pathway Analysis

Scenario-Based What-If Evaluation

Prioritized Adaptation Roadmaps

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Our other services

Custom Evaluation Suite

Targeted evaluation infrastructure tailored to your use case, success criteria, and implementation.

Production Monitoring

Continuous, contextual monitoring designed to surface issues before they impact users or regulators.

Data Augmentation

Targeted data expansion to improve model performance, coverage, and robustness without manual labeling.