Argus
AI validation, governance & compliance
Every AI decision, defensible.
Validate, govern and monitor classical ML, GenAI and AI agents for regulated industries — in real time, 24×7.
What Argus is and how it works
Product film6:30Every AI system. One control pane.
Why AI now needs independent validation, and how Argus validates, monitors and governs classical ML, GenAI and AI agents.
The Argus product film, with English captions.
Argus in action
Classical ML1:08A credit model meets its independent exam.
Follow a credit scorecard through validation, a fairness finding and council approval.
Illustrative scenarios. Companies, people and figures shown in these films are fictional.
Validation coverage
- Model families
- 22
- Algorithms
- 206
- Validation metrics
- 177
Connected across your AI ecosystem
- Platforms
- 13
- Model registries
- 9
- GenAI providers
- 11
- Data sources
- 18
Governance built in
- Approval council
- Segregation of duties (SoD)
- 83 permissions
- Tamper-proof audit trail
- Kill switch
Regulatory mapping
Mapped to SR 11-7, the EU AI Act, the NIST AI RMF, RBI, IRDAI, SEBI, DPDP and other major regulators.
Across industries. In your environment.
Multi-industry deployment with options for your operating environment.
- On-premises
- SaaS
- Air-gapped
An independent validation, observability and governance layer for AI systems. Validate before deployment, observe and monitor in production, and govern throughout — a rigorous, repeatable, regulator-defensible process.
Works with any model or AI application
- Credit-scoring gradient-boosted trees
- Actuarial pricing models
- Fraud-detection ensembles
- GenAI assistants built on RAG
- Autonomous multi-agent workflows
The gap Argus closes
Model development is fast and tooling-rich. Independent validation and ongoing governance remain manual and poorly evidenced.
Almost no existing model-risk tooling understands agents, where the unit of failure is a multi-step trajectory rather than a single prediction.
As regulators formalise expectations — SR 11-7, the EU AI Act, the NIST AI RMF and India's evolving RBI, IRDAI and SEBI posture — organisations need an auditable system of record across all three system types.
What Argus does
One inventory
A central inventory of every model, GenAI application and agent, with risk tiering and full lifecycle state.
Evidence-generating validation
A structured, evidence-generating validation battery for ML, GenAI and agents.
AI observability as a foundation
Distributed tracing, cost attribution, prompt and embedding drift, and SLOs that feed the validation and monitoring engines.
Regulator-ready certificates
Time-boxed validation certificates and continuous-monitoring dashboards, backed by a tamper-evident audit trail.
Governed remediation
Findings, remediation, model-risk acceptance and periodic re-validation, run as a governed workflow.
One platform, three kinds of AI
The validation battery is built for each system type.
Classical ML
- Statistical performance
- Fairness
- Robustness
- Stability
Generative AI
- Faithfulness
- Hallucination
- Retrieval quality
- Safety
AI agents
- Trajectory
- Coordination
- Tool & memory use
- Environment
- Safety
Model families and algorithms
22 model families, each with its own validation view and measures. Open a family to see its algorithms.
Predictive machine learning
Regressionpredict a number
Measured with R² · RMSE · MAE · MAPE · AIC · BIC
- Linear Regression
- Polynomial Regression
- Ridge
- Lasso
- ElasticNet
- Bayesian Linear Regression
- Poisson / GLM
- Quantile Regression
- SVR
- Decision Tree Regressor
- Random Forest Regressor
- XGBoost / LightGBM / CatBoost Regressor
- KNN Regressor
- Gaussian Process Regression
- MLP Regressor (DL)
Classificationpredict a category
Measured with accuracy · precision · recall · F1 · sensitivity · specificity · AUC-ROC
- Logistic Regression
- Decision Tree
- Random Forest
- Extra Trees
- AdaBoost
- Gradient Boosting / XGBoost / LightGBM / CatBoost
- SVM (SVC)
- KNN
- Naive Bayes (Gaussian/Multinomial/Bernoulli)
- LDA / QDA
- MLP Classifier (DL)
- Voting / Stacking ensemble
- Champion–Challenger set
Multi-class classificationthree or more categories
Measured with macro F1 · macro AUC · calibration (ECE) · fairness gaps
- Logistic Regression
- Decision Tree
- Random Forest
- Extra Trees
- AdaBoost
- Gradient Boosting
- XGBoost
- LightGBM
- CatBoost
- SVM
- KNN
- Naive Bayes
- LDA
- QDA
- MLP
- Voting Ensemble
- Stacking Ensemble
Propensity / scorecardcredit-risk style
Measured with KS · Gini · AUC · PSI
- Logistic scorecard (WOE/IV)
- Gradient-boosted scorecard
Time-series / forecasting
Measured with MAPE · RMSE · MASE · AIC · BIC
- AR / MA / ARMA
- ARIMA
- SARIMA
- ARIMAX / SARIMAX
- VAR / VARMAX
- Exponential Smoothing / Holt-Winters
- Prophet
- GARCH / ARCH
- TBATS / Theta / Croston
- LSTM / Bi-LSTM / GRU (DL)
- DeepAR / N-BEATS / Temporal Fusion Transformer
Survival / time-to-event
Measured with C-index · time-AUC · Brier · log-rank
- Kaplan-Meier
- Cox Proportional Hazards
- Weibull / Log-logistic AFT
- Random Survival Forest
- DeepSurv (DL)
Anomaly / outlier detection
Measured with AUC · precision@k · specificity
- Isolation Forest
- One-Class SVM
- Local Outlier Factor (LOF)
- Elliptic Envelope
- Autoencoder anomaly (DL)
Patterns, structure and text
Clusteringgroup without labels
Measured with silhouette · DB · CH
- K-Means
- K-Medoids / PAM
- Hierarchical / Agglomerative
- DBSCAN
- HDBSCAN
- Gaussian Mixture (GMM)
- Mean-Shift
- Spectral Clustering
- BIRCH / OPTICS
Dimensionality reduction
Measured with explained variance · reconstruction error
- PCA
- Kernel PCA
- t-SNE
- UMAP
- ICA
- NMF
- Autoencoder (DL)
Association rules / Market Basket
Measured with support · confidence · lift
- Apriori
- FP-Growth
- ECLAT
Markov / state models
Measured with transition probs · steady-state · attribution
- Markov chain
- Hidden Markov Model (HMM)
- Markov attribution
Topic modeling / text analytics
Measured with coherence · perplexity
- LDA
- NMF / LSA
- BERTopic
Graph / network & GNN
Measured with link-AUC · node-F1 · modularity
- Community detection (Louvain)
- Node2Vec / embeddings
- GCN / GraphSAGE / GAT (DL)
Decisions, ranking and optimisation
Recommenders / ranking
Measured with Precision@k · NDCG · MAP · MRR
- User/Item collaborative filtering
- Matrix Factorisation (SVD / ALS / SVD++)
- Factorisation Machines
- Neural CF / Wide&Deep (DL)
- Content-based / Hybrid
Causal inference / Uplift
Measured with ATE · CATE · Qini · AUUC
- A/B test / hypothesis testing
- Propensity score matching / DiD / IV
- Uplift (T/S/X-learner, causal forest)
Marketing Mix Modeling
Measured with contribution · ROI · adstock · saturation
- Regression-based MMM
- Bayesian MMM (adstock+saturation)
Reinforcement learning
Measured with cumulative reward · success rate · regret
- Q-learning / SARSA
- DQN
- Policy Gradient / A2C / PPO / SAC
- Multi-armed bandits
Optimisation / operations research
Measured with objective value · optimality gap · feasibility
- Linear / Integer Programming
- Genetic algorithm / Simulated annealing
Deep learning, GenAI and agents
NLP / Transformerstext
Measured with F1 · accuracy · LLM judge
- TF-IDF + classifier
- Word2Vec / GloVe / fastText embeddings
- RNN / LSTM / Bi-LSTM (text)
- Attention / Transformer
- BERT / RoBERTa / DistilBERT
- Sentence-Transformers
- T5 / BART (seq2seq)
Deep learningcomputer vision
Measured with accuracy · top-k · mAP · IoU
- ANN / MLP
- CNN (LeNet/VGG/ResNet/EfficientNet)
- Object detection (YOLO/Faster-RCNN/SSD)
- U-Net / segmentation
- Autoencoder / VAE
- GAN
GenAI / LLM / Chatbots
Measured with faithfulness · hallucination · relevance · toxicity · win-rate
- Instruction-tuned LLM (GPT/Claude/Llama/Mistral)
- RAG chatbot
- Summariser / extractor
- LLM Champion–Challenger
Agentic AI
Measured with task success · tool-correctness · trajectory · cost
- Tool-using agent (ReAct)
- Planner–Executor agent
- Multi-agent (CrewAI/AutoGen)
- RAG agent
Who Argus is for
Validators & model risk
Independent validators and model-risk teams.
Model owners
Data scientists, ML engineers and agent engineers.
Compliance & leadership
Compliance, audit and senior leadership.
Platform administrators
The teams who run the platform.
Role-based access control enforces separation of duties between owners and validators — a core regulatory principle.
Most tools tell you what happened. Argus helps prevent it.
Argus adds predictive and preventive capabilities to observability.
- Validates an agent's architecture before any code runs
- Calculates compound trajectory reliability
- Auto-generates regression tests from production failures
- Detects goal drift across a full trajectory
- Issues validation certificates that expire as a model drifts
Prove a system is safe, fair and correct before shipping it — then keep proving it in production.
Take Argus with you
Eight pages on why it matters now, how Argus works, what sets it apart, where it runs and how to get started. Easy to share with your risk, compliance and security teams.
Let's get your data working harder.
Reach us on WhatsApp or email — we usually reply within a day.