Success Stories

AI That Works in the Real World

We let results speak. Here are six detailed case studies showing exactly how AriloTec's AI solutions have transformed businesses across industries.

Generative AILegal Tech

AI-Powered Contract Intelligence Platform Processing 10,000+ Documents Monthly

The Challenge

LegalEdge's team of 200+ lawyers spent 60% of their time reviewing, summarizing, and extracting key clauses from contracts — work that was repetitive, error-prone, and expensive.

Our Solution

We built a RAG-based contract intelligence platform powered by GPT-4 with custom fine-tuning on legal language. The system extracts obligations, dates, risks, and anomalies with legal-grade accuracy, and generates executive summaries automatically.

LegalEdge CorpLondon, UK

GPT-4LangChainRAGNext.jsPostgreSQL

Key Results

92%

Time reduction in contract review

99.1%

Extraction accuracy on key clauses

$1.8M

Annual savings in legal hours

3 weeks

From kickoff to production

Machine LearningBanking

Real-Time Fraud Detection ML System Monitoring 2 Million Transactions Daily

The Challenge

FinancePro was losing $6M+ annually to fraudulent transactions. Their rule-based system had a 35% false positive rate, frustrating legitimate customers and overwhelming the fraud operations team.

Our Solution

We designed and deployed an ensemble ML fraud detection system combining gradient boosting, neural networks, and graph-based anomaly detection. The model processes transactions in under 12ms, enabling real-time blocking.

FinancePro BankSingapore

XGBoostPyTorchKafkaRedisAWS SageMaker

Key Results

67%

Reduction in fraud losses

89%

Reduction in false positives

$4.2M

Fraud prevented in Year 1

12ms

Real-time inference latency

Computer VisionManufacturing

AI Quality Control System Replacing 40 Manual Inspectors on Automotive Assembly Line

The Challenge

AutoPrecision's manual QC process had an 8% defect escape rate — costing millions in recalls and rework. 40 inspectors worked around the clock, yet quality remained inconsistent.

Our Solution

We installed a multi-camera computer vision system using YOLOv9 with custom-trained defect detection models. The system inspects every unit in real-time, classifying 47 defect types with sub-30ms inference on edge hardware.

AutoPrecision IndiaPune, India

YOLOv9OpenCVTensorRTNVIDIA JetsonPython

Key Results

99.4%

Defect detection accuracy

0.2%

Defect escape rate (from 8%)

65%

QC cost reduction

24/7

Continuous automated inspection

AI AgentsProcurement

Multi-Agent Procurement Automation Handling €50M in Purchase Orders Autonomously

The Challenge

GlobalEdge's procurement team processed 2,000+ purchase orders monthly — a labor-intensive process involving vendor lookup, price negotiation, approval routing, and ERP data entry.

Our Solution

We architected a LangGraph-based multi-agent system with specialized agents for vendor lookup, price comparison, compliance checking, and approval orchestration — integrated directly with their SAP S/4HANA instance.

GlobalEdge EnterpriseBarcelona, Spain

LangGraphClaude APISAP APIPythonFastAPI

Key Results

80%

Purchase orders processed autonomously

74%

Reduction in processing time

4 months

ROI achievement timeline

99.8%

Compliance accuracy

Predictive AnalyticsHealthcare

Patient Risk Stratification AI Deployed Across 12 Hospitals, Preventing 1,400+ Adverse Events

The Challenge

HealthBridge struggled to identify high-risk patients proactively. Clinicians relied on intuition and fragmented data — resulting in preventable readmissions and adverse events.

Our Solution

We built a HIPAA-compliant patient risk ML platform that ingests EHR, lab, medication, and vitals data to score patient risk in real-time. Integrated with nurse call systems, it surfaces alerts 48 hours before predicted deterioration.

HealthBridge NetworkSingapore

PyTorchFHIR APIsAWS HealthLakeFastAPIReact

Key Results

1,400+

Adverse events prevented

34%

Reduction in ICU readmissions

$8.2M

Annual cost avoidance

Hospitals on the platform

NLP & AutomationInsurance

AI Claims Processing Platform Reducing Settlement Time from 14 Days to 6 Hours

The Challenge

ShieldMore's claims process required manual document review, fraud assessment, policy matching, and approval — taking an average of 14 business days and costing significant operational overhead.

Our Solution

We built an end-to-end claims AI platform using NLP for document understanding, ML for fraud scoring, and workflow AI for automated approval routing. Integrated with their legacy claims management system via API.

ShieldMore InsuranceMumbai, India

BERTspaCyXGBoostFastAPIAzure

Key Results

6 hours

Average claims settlement (from 14 days)

55%

Operational cost reduction

94%

Customer CSAT improvement

31%

Fraud detection improvement

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