We benchmark candidate AI models against your specific use case requirements — latency, cost, compliance, task fit, and more — so your architecture decisions are backed by engineering data, not marketing benchmarks.
A new foundation model drops every week. Benchmarks are gamed, pricing changes quarterly, and context windows keep expanding. Most teams pick models based on blog posts and Twitter threads — then discover in production that latency is too high, costs don't scale, or the model can't meet compliance constraints.
The Model Efficacy Audit replaces opinion with engineering data.
The full audit is tailored to each engagement, but every evaluation covers at least these production-reality constraints. A model that scores well on accuracy but fails on cost, compliance, or task fit is not a viable production choice.
Visualizes the trade-off between response speed and reasoning quality for your specific task. A real-time customer-facing chat has different latency tolerance than a 30-second batch decision pipeline. We map the frontier for your workload — not against generic benchmarks.
Key metric: p95 latency at target accuracy threshold
Forecasts operational cost at production volume — not the demo-day volume that makes every model look cheap. We model token price times expected workload with safety margin, including input/output ratios, caching efficiency, and batch vs. real-time cost structures. Then we ask: what happens when usage 10x's?
Key metric: Monthly cost at projected production throughput
Verifies data handling against your specific constraints: data residency, retention policies, bring-your-own-key requirements, PII handling, and audit trail needs. This axis often eliminates otherwise attractive options before we even discuss performance.
Key metric: Pass/fail against client compliance matrix
How well does the model actually perform on your workflow — not on generic benchmarks, but on golden sets and realistic eval tasks built from your data? This is where we compare a frontier model against an open-source or fine-tuned specialist. Marketing benchmarks are irrelevant; your workflow is the only benchmark that matters.
Key metric: Accuracy on client-specific eval set vs. baseline
The output is a model portfolio — primary model plus fallback — with routing logic that degrades gracefully under load or constraint changes. Single-model architectures are brittle. Production systems need alternatives.
Model selection is a snapshot, not an eternal truth. New model families ship quarterly, pricing changes without notice, and your own data volume grows. The audit includes explicit re-evaluation triggers so your architecture stays current.
A model drops pricing by 50%, adds tool-use support, or expands context windows. We flag when a re-benchmark is warranted.
Once you collect 5-10k labeled examples from production, a fine-tuned specialist may outperform the frontier model. The audit defines that threshold upfront.
New data residency requirements, regulatory guidance, or retention policies can eliminate models that previously passed. The compliance screen re-runs automatically.
The Model Efficacy Audit runs during the Validation phase of the Synapse Cycle™ — after the Action Potential Index™ has filtered use cases down to the highest-probability production bets. We don't audit models in the abstract. We audit them against a specific, validated use case with defined requirements.
Action Potential Index scores and filters use case portfolio
Model Efficacy Audit benchmarks candidates against selected use case
Architecture decisions finalized with audit data as evidence
A comparative engineering stress-test that benchmarks candidate AI models against the specific requirements of a validated use case. The full audit is tailored to each engagement, but always includes latency vs. reasoning depth, cost profile at production scale, security and compliance constraints, and task fit against your actual workflow data — among other dimensions. The output is a portfolio recommendation (primary model + fallback) with routing logic, not a single-model bet.
Leaderboards benchmark against generic tasks. We benchmark against your workflow using golden sets and realistic eval tasks built from your data. A model that tops MMLU might underperform on your specific document analysis or pricing decision pipeline. Marketing benchmarks are irrelevant — your workflow is the only benchmark that matters.
The audit includes explicit re-evaluation triggers: a model drops pricing by 50% or adds new capabilities, your production data volume crosses the fine-tuning threshold (typically 5-10k labeled examples), or compliance requirements change. Model selection is a snapshot, not a permanent decision — the landscape shifts quarterly.
It runs during the Validation phase of the Synapse Cycle™, after the Action Potential Index has filtered your use case portfolio down to the highest-probability production bets. We don't audit models in the abstract — we audit them against a specific, validated use case with defined latency, cost, and compliance requirements.
Start with a Pathfinder Engagement™. We run the Action Potential Index on your use case portfolio, then apply the Model Efficacy Audit to the top candidate — delivering a data-backed architecture recommendation in 4-6 weeks.