From global banks to pharmaceutical giants — measurable outcomes delivered in production, not in slide decks.
Tier-1 North American bank needed to modernize its fraud detection infrastructure, replacing aging rules-based systems with ML-powered real-time decisioning at enterprise scale.
The bank's legacy fraud system generated false positive rates exceeding 34%, costing over $80M annually in manual review and customer friction. Fraudsters were adapting faster than static rule updates could respond.
QantumIQ deployed a three-phase AI at Scale engagement: a 30-day discovery and data audit, followed by a 60-day ML model development sprint using gradient boosting and transformer-based sequence models, culminating in a 90-day hardened production deployment with full MLOps infrastructure.
The platform now processes over 2 billion transactions daily with sub-40ms latency, achieving 87% fraud detection accuracy — a 31-point improvement — while reducing false positives by 62%.
A leading global streaming platform with 180M subscribers across 35 countries faced accelerating churn driven by content discovery fatigue and commoditized content libraries.
With 45% of cancellations citing 'nothing to watch,' the client needed a personalization engine capable of real-time content discovery across 180M diverse profiles, localized across 35 markets.
QantumIQ built a multi-armed bandit recommendation system layered on a deep collaborative filtering model, with a content embedding pipeline ingesting metadata, viewing behavior, and contextual signals. Deployed via microservices on Google Cloud.
Churn fell 40% within 90 days of full rollout, retaining $95M in annual recurring revenue. Average session length increased 23%. The recommendation engine now serves 1.4 billion API calls per day with 99.7% uptime.
A top-5 global pharmaceutical company sought to compress its Chemistry, Manufacturing and Controls (CMC) timeline across six manufacturing facilities through AI-driven process optimization.
CMC development delays were adding 18–24 months to drug development timelines, costing the client $310M+ per year in delayed revenue and regulatory overhead.
QantumIQ deployed an AI platform combining process analytical technology (PAT) sensor integration, Bayesian optimization for formulation development, and a digital twin model of each manufacturing facility. The system ingests real-time batch data to predict deviations before they occur.
Development timelines compressed by 40% across all six facilities. Batch compliance reached 99.2%, up from 87.4%. The platform identified three previously unknown process optimization windows.
A national defence agency needed to migrate its classified communications infrastructure to NIST-approved post-quantum cryptographic standards ahead of regulatory deadlines.
The agency's classified communications relied on RSA-2048 and ECC-256 encryption across 14,000+ endpoints. The 'harvest now, decrypt later' threat model made immediate migration critical.
QantumIQ conducted a full cryptographic asset inventory, designed a phased migration to ML-KEM (FIPS 203) and ML-DSA (FIPS 204), and deployed automated certificate rotation across all endpoints with zero-downtime switchover.
Full PQC compliance achieved in 120 days. Zero classified communications were disrupted during migration. The framework has since been adopted as a template by three allied nations.
A 12-hospital health system with 2.4M annual patient encounters needed to reduce diagnostic imaging backlog and improve triage accuracy for emergency radiology.
Emergency radiology turnaround averaged 4.2 hours, with critical findings occasionally delayed by 8+ hours. Staff burnout was accelerating radiologist attrition.
QantumIQ deployed a computer vision AI model trained on 2.8M de-identified studies to auto-prioritize critical findings (stroke, PE, pneumothorax) and surface them to radiologists within minutes.
Diagnostic wait times dropped 60%. Critical finding escalation time reduced from 4.2 hours to 18 minutes. Radiologist satisfaction scores improved 34%, and attrition reversed within 6 months.
A major North American utility serving 8M customers needed to modernize grid management for renewable integration and reduce costly unplanned outages.
Renewable intermittency was causing 200+ grid stability events annually, and aging infrastructure lacked real-time predictive capability. Manual dispatch responses averaged 45 minutes.
QantumIQ built a predictive grid management platform combining weather forecasting, IoT sensor telemetry, and graph neural networks to model grid topology and predict failure cascades before they occur.
Unplanned outages fell 45% in the first year. Automated response reduced dispatch times from 45 minutes to under 4 minutes. The platform manages 1.2M IoT sensor feeds in real-time.
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