In-depth stories of AI transformations that delivered measurable business impact.
How we reduced trade reconciliation time from 2 days to 4 hours while improving accuracy and eliminating $340M in annual risk exposure.
A leading global investment bank was struggling with manual trade reconciliation across 47 international markets. Their team of 80 analysts was processing 2.3 million transactions daily, with reconciliation taking up to 48 hours. The manual process had an error rate of 0.8%, creating significant settlement risk and regulatory exposure.
The bank faced several critical issues:
Previous attempts to automate the process using rule-based systems had failed due to the complexity and variability of confirmation formats. The bank needed an intelligent solution that could understand context, handle exceptions, and adapt to new scenarios.
We developed an end-to-end intelligent reconciliation system powered by custom fine-tuned large language models. The solution combined natural language processing, computer vision for document analysis, and deterministic matching logic into a unified platform.
Our system processes trade confirmations in any format—emails, PDFs, structured files, even faxes—using GPT-4 fine-tuned on 10 years of historical confirmations. The model extracts key fields with 99.97% accuracy across 23 languages.
The system architecture included:
We executed a rapid 8-week implementation using an agile approach with 2-week sprints.
Weeks 1-2: Discovery and Data Preparation
We analyzed 500,000 historical trade confirmations to understand format variations and edge cases.
Our team worked alongside the bank's reconciliation analysts to document tribal knowledge and
exception-handling procedures that had never been formally captured.
Weeks 3-4: Model Development and Training
We fine-tuned GPT-4 on the bank's historical data and built custom extraction pipelines for
each major confirmation format. The team developed a comprehensive test suite covering 2,000+
scenarios including edge cases and historical exceptions.
Weeks 5-6: Integration and Testing
Integration with the bank's existing trade management systems, data warehouses, and
communication platforms. We ran parallel processing—the AI system processed all trades
alongside the manual process to validate accuracy before go-live.
Weeks 7-8: Deployment and Knowledge Transfer
Phased rollout starting with a single market, then expanding to all 47 markets. Comprehensive
training for the operations team on system monitoring, exception handling, and continuous improvement.
Beyond the quantitative metrics, the transformation delivered significant qualitative benefits:
"Aixagonal transformed a process we thought was impossible to automate. Their deep understanding of both AI technology and financial operations allowed them to build something that actually works in production. The system handles complexity we didn't think machines could understand."
Reducing $420M in excess inventory while cutting stockouts by 73% through predictive analytics and intelligent replenishment.
A major U.S. retailer with 1,200 stores and three distribution centers was drowning in excess inventory while simultaneously experiencing chronic stockouts. With 85,000 SKUs and $420M in excess inventory, they needed a complete overhaul of their demand forecasting and replenishment systems.
Their existing forecasting system relied on simple moving averages and couldn't account for:
The result was a 12% stockout rate on high-demand items while clearance merchandise occupied valuable shelf space. Inventory turns had declined to 4.1x annually, well below the industry average.
We built a comprehensive demand forecasting and inventory optimization platform using ensemble machine learning models that synthesize dozens of data sources into accurate SKU-level predictions.
Our system combines LSTM neural networks for trend analysis, XGBoost for promotional impacts, and Prophet for seasonality detection. Each model specializes in different patterns, with a meta-learner determining optimal weights for each SKU-location combination.
The platform integrates data from:
The transformation delivered immediate and sustained business impact:
The system provides forecasts at multiple horizons (daily, weekly, monthly) enabling better decision-making across merchandising, supply chain, and store operations teams.
"This is the most impactful technology investment we've made in a decade. The system paid for itself in 11 weeks, and the improvements keep compounding. Our merchants can now make data-driven decisions with confidence, and our stores have the right products at the right time."
Reducing ER wait times by 60% and saving $8.4M annually through predictive patient surge forecasting and intelligent staffing.
A major healthcare system with 23 emergency departments across the southeastern U.S. was struggling with unpredictable patient volumes. Average wait times reached 4.2 hours during surge periods, while overnight shifts were frequently overstaffed. Traditional forecasting methods based on historical averages were consistently 30-40% inaccurate.
The challenges were multifaceted:
We developed a predictive patient volume forecasting system that considers dozens of variables affecting ER utilization. The system provides 72-hour forecasts with hourly granularity and automatically generates optimal staffing recommendations.
Our models integrate weather data, disease surveillance, local events, school calendars, holidays, and social determinants of health with historical utilization patterns to predict patient volumes with 91% accuracy at the 24-hour horizon.
The system architecture includes:
The system learns continuously, with automated retraining every week incorporating the latest data and adjusting for changing patterns like seasonal flu, COVID variants, or local demographic shifts.
Deploying an AI system that affects staffing decisions required careful change management. We worked closely with nursing leadership, ED medical directors, and HR to build trust in the system.
Phase 1: Shadow Mode (Weeks 1-4)
The system generated forecasts and recommendations but had no impact on actual staffing.
We compared predictions against actual volumes and existing staffing approaches, building
confidence with quantitative evidence.
Phase 2: Advisory Mode (Weeks 5-8)
Scheduling managers received system recommendations but maintained full control over final
decisions. This phase allowed staff to understand how the system works and when to trust
its recommendations.
Phase 3: Full Deployment (Weeks 9-12)
The system's recommendations became the default starting point for staffing decisions, with
human oversight and override capability. Training programs ensured all stakeholders understood
system capabilities and limitations.
The transformation delivered improvements across multiple dimensions:
"This system has been transformative for our emergency departments. We can now staff appropriately for predicted demand, which means better care for patients and better quality of life for our staff. The accuracy is remarkable—it often predicts surge events we wouldn't have anticipated."
Let's discuss how AI can transform your operations with measurable results.
Start Your Transformation