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An executive decision-support whitepaper translating the official FAO Food Loss Index (SDG Indicator 12.3.1a) into actionable intelligence for 2021–2023. This study establishes a data-driven monitoring baseline across major commodity groups, identifying Fruits & Vegetables (3-year mean index of 109.65) as the primary priority for diagnostic follow-up while offering structured strategies for public policy, industry benchmarking, and evidence-based resource allocation.
Addressing reverse-logistics friction and return-rate costs in fashion e-commerce, this study leverages the 1.37M-record ASOS GraphReturns dataset to evaluate product, pricing, and country-level return drivers. Using Terno AI for structured analysis alongside a deployed Logistic Regression baseline, the platform provides purchase-time risk scoring to power relative risk ranking, operational prioritization, and non-punitive intervention pilots.
As enterprise adoption pivots from raw model parameter scale to verifiable trust, CertusAI introduces a compliance-first LLM architecture engineered specifically for highly regulated industries. By integrating real-time PII redaction, deterministic policy enforcement, and cryptographically signed audit chains directly into the inference pipeline, CertusAI unlocks agentic AI productivity while ensuring total data sovereignty and statutory compliance.
A Principle-Grounded Synthetic Data Approach to Identity-Level Fraud Detection
A Proof-of-Concept for a Decentralised Fraud Defence System Using Federated Learning and Graph Neural Networks in Indian Banking
A daily, exposure-weighted risk signal built from GDELT news data that outperforms the GPR Index on 5 of 8 commodities. Built with Terno AI.
The same complaint-routing model scores 96.3% or 83.2% depending on which three columns you leave in the training data. The high number is the intake form being read back to you. This is what the leakage audit found before a single model was trained, why 83.2% is the honest figure, and how the same model — given permission to say "I don't know" — becomes deployable at 90.7% accuracy on 79.5% of traffic.
Governments publish decisions continuously, and each one quietly changes the ones before it. The obvious way to find the contradictions is to train a model to read pairs of clauses and judge them — which requires labels nobody has. This paper takes the other route: the documents already say what they amend, cancel and supersede, in their own words. Recovering that structure took no model, no labels and no training run — and the analysis was run entirely by asking an AI data assistant questions in a chat window.
Power transformers are among the most critical assets in electrical distribution infrastructure. Their unexpected failure can result in power outages, safety hazards, equipment damage, expensive repairs, and long service interruptions. Traditional transformer maintenance practices often rely on periodic manual inspection, offline testing, or run-to-failure maintenance. These methods are expensive, slow, labor-intensive, and unable to detect rapidly developing faults in real time.
Analyzing 119,390 hotel bookings, this white paper shows a tuned Random Forest model beats Logistic Regression and XGBoost at predicting cancellations, powering segment-specific deposit policies and model-driven overbooking strategy.
A data-driven fraud detection framework to identify anomalous insurance claims and improve healthcare payment integrity through predictive analytics.
Analyzing 207,862 human–AI interactions, this study introduces a proxy-based framework (HRS + CEP) showing that hallucination risk and response verbosity together drive a moderate, statistically significant increase in human collaboration effort.
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