Governed AI decision products
Harbor Nexus combines specialized models with governed data, MLOps and LLMOps, human oversight, evaluation and security to support personalization, fraud prevention, recommendation, value management, market intelligence and forecasting.
Each product has a distinct decision objective, method stack, control boundary and evidence model. They share data, evaluation, operations and security capabilities without collapsing into one generic AI endpoint.
Contextual bandits, Thompson Sampling, uplift and causal models operate within consent, eligibility, frequency, fairness, inventory and budget constraints.
Operational evidence: Incremental lift, regret, calibration, policy violations and customer impact.
Rules, supervised learning, anomaly detection, device and entity signals, graph analysis and case workflows separate detection from final operational action.
Operational evidence: Loss avoided, false decline, precision/recall, investigation yield, latency and appeal.
Candidate generation, vector retrieval, ranking, sequence models and policy re-ranking balance relevance, diversity, discovery, availability and long-term value.
Operational evidence: Recall, ranking quality, diversity, coverage, incremental engagement and downstream value.
Elasticity, survival, churn, uplift, CLV and constrained optimization are separated so predictive scores do not become uncontrolled prices or offers.
Operational evidence: Margin, retention lift, calibration, constraint compliance, fairness and rollback rate.
Multilingual NLP distinguishes sentiment, stance, emotion, topic, event and narrative; licensed sources, deduplication, entity resolution and permission-aware RAG preserve evidence.
Operational evidence: Extraction quality, source coverage, citation precision, unsupported claims and analyst correction.
Statistical, machine-learning, RNN/LSTM, transformer and time-series foundation models are compared against baselines through rolling-origin tests and calibrated intervals.
Operational evidence: MASE/WAPE, interval coverage, calibration, hierarchy coherence, drift and decision value.
The platform manages the full lifecycle from legitimate data use and design through approval, serving, oversight, incident response and retirement.
Rights, consent, quality, point-in-time joins, lineage, semantics, feature and knowledge services.
Models, rules, retrieval, prompts, tools, constraints, registries and approval state.
Task quality, calibration, robustness, fairness, incrementality, RAG evidence, agent behavior and human review.
Batch, real-time and edge delivery with identity, routing, timeout, fallback, override and appeal.
Versioned artifacts, deployment, rollback, drift, prompt and retrieval traces, incidents and retirement.
Least privilege, red teaming, tool boundaries, model routing, quotas, caching and unit economics.
Every release preserves responsibility, version identity, approval evidence, fallback and an accountable human path.
Rights, consent, quality, point-in-time context, lineage and retention.
Features, retrieval, models, rules, constraints and human responsibility.
Offline, scenario, fairness, incrementality, red-team and release review.
Identity, routing, fallback, review, override and appeal.
Drift, cost, incidents, retraining, rollback and retirement evidence.
ISO/IEC 42001, 23894, 22989, 23053 and 42005, NIST AI RMF and GenAI guidance, OWASP LLM and Agentic risks, MITRE ATLAS, security and privacy controls inform our delivery system.
AI management, risk and impact assessment are integrated across the product lifecycle.
Trustworthiness, LLM and agentic risks guide design, evaluation and red-team coverage.
Permission-aware retrieval, minimization, identity, logging and human appeal shape production controls.
Evaluation covers data, models, retrieval, agents, runtime service, business incrementality and governance completeness.
Task performance, calibration, robustness, latency, availability and cost.
Causal lift, loss avoided, margin, retention and decision value beyond baseline.
Fairness, override, complaint, incident, drift and evidence completeness.
Each layer has distinct evidence requirements so accuracy is never treated as a substitute for operational, business or governance performance.
Representativeness, point-in-time integrity, labels, license, personal data, quality and drift.
Task performance, calibration, uncertainty, robustness, slices, fairness and failure behavior.
Retrieval recall, evidence precision, citations, unsupported claims and permission enforcement.
Task success, tool correctness, identity, policy, looping, human takeover, cost and recovery.
Latency, availability, fallback, security, observability and change failure.
Incremental value, operational workload, customer or employee impact, cost and incidents.
Owner, impact assessment, approval, record, appeal, incident response and retirement.