Workflows
DSG supports organizations focused on continuous improvement and operational excellence.
Managing pricing, fleet allocation, and commercial flows across multiple routes, vessels, and international markets
Quoting cycles dropped from hours to minutes. Quotes are generated instantly from inbound emails using real-time data
Commercial teams respond immediately to demand, adjust pricing dynamically, and make faster routing decisions
Higher win rates, faster deal cycles, and improved profitability across routes
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Predicting fuel consumption across a global fleet under variable weather, cargo, and vessel conditions
Manual fuel estimates running at 68% accuracy were replaced by an AI system trained on historical voyage data, weather, and vessel telemetry
Operators receive speed and routing recommendations that minimize fuel use across every leg
94% prediction accuracy, 15% average fuel savings, EUR 12M annual savings across the fleet
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Customer service across 28 agencies globally, with fragmented inboxes and inconsistent regional handling
Manual triage was replaced by a hybrid AI system with multilingual transfer learning, integrated directly into the CRM
Inbound mail is automatically classified and routed, with human review only for low-confidence cases
80–97% classification accuracy, 95% of global email activity automated, 65% reduction in response time
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Managing high-value operational processes and tender execution across multiple projects, systems, and stakeholders
Tender processes moved from fragmented, manual workflows to a fully structured, end-to-end system
Teams operate from a single system with full visibility across requirements, documents, risks, and quote generation
Shorter tender cycles, improved accuracy, and higher bid success rates
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High operator-to-operator variability in managing complex autoclave processes was reducing recovery rates and driving unplanned downtime
A temporal deep learning model trained on years of plant data now recommends optimal control sequences for water injection, oxygen flow, temperature, and hold time
Operators receive actionable set points in real time, with very low recommendation error
Higher productivity, fewer unplanned shutdowns, more consistent recovery across shifts
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Limited week-by-week visibility into expected supply across multiple fields and crop varieties, leading to inefficient labor and logistics planning
Manual processes and static calendars were replaced by automated pipelines that consolidate weather, agronomy, and field data into weekly forecasts
Planners receive transparent, versioned weekly yield forecasts by field and variety through an interactive dashboard with chat assistant
Human-level prediction accuracy, sub-24-hour data freshness, 65% reduction in planning cycle time
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Harvest scheduling inefficiencies due to dry matter uncertainty led to quality issues and missed optimal harvest windows
Manual heuristics gave way to a system that consolidates weather and field data, enriches it with terrain and seasonality features, and predicts weekly dry matter
Versioned reports, interactive dashboards, and a natural language Q&A interface let teams self-serve harvest decisions
1.2% MAE on dry matter (vs 2% requirement), 100% weekly coverage across fields, sub-24-hour pipeline latency
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Managing approximately 40,000 properties engaging assets, tenants, and multiple operational units under regulatory oversight
Operations shifted from fragmented reporting to continuous, system-wide visibility across assets and performance
Management identifies anomalies early, monitors contractor performance, and acts on real operational signals
Improved cost control, faster issue resolution, and stronger operational accountability
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Predicting whether an inpatient with COVID-19 was likely to deteriorate within the next 6 hours, using routine EHR signals
Manual tracking was replaced by production deep learning models trained on the hospital's own historical event streams
Clinicians receive risk scores at the bedside with both local and global explanations, integrated into the standard EHR workflow
Production-grade early warning across hundreds of patients, supporting earlier care escalation in surge conditions
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Operating large-scale, highly regulated systems across multiple products, customers, and global environments
Audit cycles compressed from months to weeks, with control processes running continuously instead of periodically
Documentation, validation, and evidence collection run as continuous workflows, with teams focused on review and judgment
70 to 80 percent faster audit execution and more than 50 percent efficiency improvement
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Category arrangement optimizer ran in 6–10 minutes per scenario, blocking interactive planning sessions
A genetic algorithm optimizer was upgraded with stronger selection, mutation, and a fitness function reframed around real store features and sales targets
Merchandisers run what-if scenarios in seconds during planning, with layouts that map directly to commercial outcomes
10x faster (30–60 seconds per scenario), sales uplift improved from ~3% to 7–8% on optimized categories
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Millions of daily content submissions across multiple languages and formats, with manual moderation unable to keep pace
A closed-loop ML pipeline using multi-modal models over text and images replaced manual review for the routine majority of cases
Roughly half of all content is auto-moderated, with the system continuously improving from moderator feedback on edge cases
50% auto-review rate at millions-per-day scale, with reduced moderation cost and consistent policy alignment
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