Detect likely accessibility friction on mobile such as touch target issues, viewport clipping, gesture dependence, and c

Score app store review risk from metadata claims, permission use, crash history, privacy label, and policy changes

Mobile Dev Intel API — intelligence and analytics for AI agents.

Live mobile location intel data and intelligence for agents and automated workflows

Audit mobile SEO factors including viewport, font sizes, and tap targets

Live mobile seo checker data for agents and LLMs.

Score mobility aid device selection appropriateness

Score urban mobility gap from accessibility analysis

Calculate MoCA cognitive assessment score interpretations

Generate realistic mock API response data for testing

Mock API Response Generator

Generate realistic mock data for testing and development

Generate realistic mock data from schemas — names, emails, addresses, UUIDs, and 35+ types.

Generate realistic mock server responses from schemas with delay and error simulation

Generate realistic mock API responses from endpoint descriptions

Guide to test doubles: mocks, stubs, spies with jest.fn() and sinon.

Parse Modbus RTU and TCP data frames

Calculate urban transport mode share from survey data

Score ML model AB test design and statistical validity

Calculate model accuracy, error rate, 95% confidence interval, and improvement over baseline.

AI model benchmarks — MMLU, HumanEval, GSM8K and reasoning benchmarks explained. Built for AI developers.

Model Building Guide data API — search, random, bulk endpoints.

Audit model cards for intended use, metrics, limitations, risks, data, and evaluation coverage

Generate structured model cards for ML model documentation

Parse and extract metadata from AI model cards

Compare LLM benchmark scores — MMLU, HumanEval, GSM8K, HellaSwag, MATH — across 9 major models.

Diagnose overfitting/underfitting from train/val accuracy gap, loss gap and parameter-to-sample ratio.

Live model consensus live data and intelligence for agents and automated workflows

Detect model drift canaries from feature shift, output change, segment impact, freshness, and alert severity

Detect data and concept drift in ML models

Detect model performance drift from prediction distributions

Model Drift Intel API — intelligence and analytics for AI agents.

Reference for ML metrics: accuracy, F1, AUC-ROC, RMSE with when-to-use guidance.

Evaluate ML model performance with cross-validation, bias detection and fairness metrics

Route model fallbacks from task risk, latency pressure, cost ceiling, modality need, and quality tolerance

Fine-tuning tools: dataset preparation, PEFT/LoRA configuration, training job management, evaluation metrics, model comp

Build model gateway policies from data class, model tier, user role, action risk, and logging needs

Live model inversion intel data and intelligence for agents and automated workflows

Live model latency live data and intelligence for agents and automated workflows

Calculate ML model evaluation metrics from predictions and actuals

Score pruning sparsity impact on accuracy and size

Score ML model registry completeness and governance quality

Model Router

Live model router live data and intelligence for agents and automated workflows

Model Routing Cost Optimizer

Route AI tasks to the right model — map complexity to Haiku vs Sonnet vs Opus or GPT-4o vs mini.

Guide model selection for prediction tasks

Select the right AI model for any task — GPT-4, Claude, Gemini comparison by capability and cost. Built for AI developers.

Score ML model serving latency against SLA requirements

Calculate transformer model parameter count from architecture specs or look up 17 known models.