Every capability, in one place.

The four-layer trust model (agency, engineering, governance, institutional) with ten constructs and 127 cues.

Browse, document and learn from trust incidents across sectors.

Regulatory rules, jurisdictions and trust constructs as a queryable graph.

The three-phase methodology behind every Validant engagement.

A complete fairness audit in one sitting: upload a dataset, get a plain-language verdict and a ship-ready report without writing a line of code.

Guided, end-to-end fairness audit: a supported eleven-step walkthrough from project setup to a signed model card.

Self-directed path: run any of the 27 fairness modules à la carte, in whatever order your project needs, instead of a fixed wizard.

Scan datasets for representation gaps, historical patterns and statistical disparities.

Surface features that quietly stand in for protected attributes.

Residualize features to break proxy chains.

Build a DAG and classify each path from a protected attribute to the outcome as direct, indirect or proxy discrimination.

Flip demographics, see what the model actually changes.

Auto-detects protected attributes and scans for fairness violations across all built-in metrics.

12+ criteria (demographic parity, equalized odds, calibration) mapped to jurisdictions.

Pre-built risk register against EU AI Act, NIST RMF, ISO 42001.

Rebalance, rebin and transform data before training.

Demographic Parity, Equalized Odds, Equal Opportunity, Adversarial, Counterfactual.

Exponentiated Gradient and Grid Search over fairness constraints.

Drop-in, sklearn-compatible fair model wrappers.

L2 parity, conditional independence and HSIC penalties.

Group-specific temperature and Platt scaling during training.

Platt, Isotonic, Beta and Temperature scaling per subgroup.

Per-group thresholds for any fairness constraint.

Map the accuracy ↔ fairness frontier and pick a defensible point.

Multiplicative or additive adjustments at inference time.

Boost confidence for marginal cases near the decision boundary.

One API for classification, regression and ranking metrics.

DP, EO, Equal Opportunity, Exposure, MAE/RMSE parity, ECE/MCE.

Bootstrap and Bayesian intervals, safe on small samples.

Plain-language explanations for every metric.

Detect fairness gerrymandering: strategic subgrouping that hides bias.

Automated intersectional analysis across multiple protected attributes simultaneously.

Show which features drove any single decision, behind one interface, with explanations tiered to the audience. Built for the GDPR Art. 22 right to an explanation.

Decompose a group-fairness disparity into the per-feature contributions that drive it.

Actionable what-if explanations: the smallest change that would flip an individual outcome.

Check that an explanation is faithful and stable, not merely persuasive.

Measure how strongly word and sentence embeddings tie social groups to loaded concepts, before that bias reaches a model.

Check whether a toxicity, sentiment or moderation classifier scores text differently just because it names a particular group.

CI/CD gate that blocks unfair deploys: overall, per-attribute, intersectional.

Pre-training dataset check, no surprises downstream.

Validate fairness configs and model cards before code lands.

Fairness assertions for your existing test suite.

Multi-level CI/CD gate that enforces overall, per-attribute and per-intersection thresholds together.

Sliding-window bias tracking in production.

Wavelet + KS, MMD, SPRT: multi-scale drift signals.

Trends, cycles and seasonality of fairness over time.

Rank upstream variables by their share of explained drift in outcomes.

Intersectional A/B testing with proper statistical machinery.

Sample sizes that actually detect what you care about.

Pareto frontier, causal decomposition and recommendations.

Privacy-aware metrics warehouse with health scoring.

Publication-grade SVG report templates with zero runtime dependencies.

Plotly + Dash, with what-if threshold sliders.

SHA-256 hash chaining, HMAC signing, zero-knowledge encryption.

Built-in Plotly and Matplotlib renderer for every metric, with multi-theme support.

Swap demographics in prompts, measure output drift.

BBQ, BOLD and HolisticBias out of the box.

Truthfulness, stereotypes, toxicity and robustness in one harness.

Sentiment, toxicity and refusal-rate profiling.

Noise floor and consistency baselines per model.

Does the chain-of-thought actually match the answer?

One interface across OpenAI, Anthropic and Hugging Face.

The shared graph store powering Trust KG, Compliance KG and fairness lineage queries.

Vector store with hybrid semantic + keyword retrieval feeding every agent and assistant.

Every RAG answer ships with source provenance you can audit and challenge.

Shared execution layer for every Validant agent: tools, memory, traces, guardrails.

Interactive in-app copilots that work with you live across audit, fairness and identity flows.

Compose, brand and export reports that pull live evidence from any body.

Optional AI-generated narrative sections, customizable in tone and prompt, generated on local hardware so evidence never leaves your environment.