“LLM evaluation”

Run bounded spike work to test feasibility before committing to a normal implementation path.

Process multiple artifacts efficiently with comparative analysis and trend reporting

Indicative US 409A and UK EMI share value estimates from cap table and financial inputs.

Use when running client-side security stress harnesses against security challenge packs, measuring leak posture, Agent Vault routing, or HashiCorp Vault…

Use when evaluating local or open-weight language models with the AI Lab OS benchmark workflow, including raw prompt evidence, dashboard-compatible scores…

Discounted cash flow valuation and intrinsic value analysis for public companies.

Deterministic finance tools for AI agents — IRR, NPV, MOIC, DCF, WACC and sensitivity.

Design and validate Product Factory evaluation corpora, benchmark adapters, scorecards, model routes, local/cloud comparisons, skill or prompt promotion, and…

Run a golden set and gate LLM model swaps on accuracy, with latency and cost per request.

Full-stack observability for AI applications, LLMs and agents, covering performance, cost, quality and compliance

Evaluate factual claims against inspected sources and identify unsupported assertions, contradictions and missing evidence.

Eval-driven skill tuning.

Use when designing and implementing evaluation loops for AI agents, including reflection, evaluator-optimiser patterns, rubric scoring, LLM-as-judge review…

9,846 ADHD evaluation providers from the CMS NPI Registry.

Run the optional deterministic local writing evaluation composite without model calls.

Design agent evaluations that separate execution success from correctness, scope discipline, safety and recovery behavior.

Extract metrics, score traces, and evaluate benchmark task results.

Evaluate interface descriptions against 195 research-backed UX/UI principles.

Specialized evaluation of prompt quality, clarity, and effectiveness

Scores candidate engineering ideas using Signal Detection Theory (d' >= 1.80) and empirical complexity-vs-value gates.

How to judge whether information is reliable.

Post-implementation review — summarizes what was built, then quizzes you on design decisions and tradeoffs.

Run a strict research benchmark for a model that will

MCP server providing access to the Scorecard API to evaluate and optimize LLM systems.