DataInn Technologies

Selected work

Governed systems, measured in production.

Case studies across financial services, healthcare, legal, retail, and industrial operations—each built around a concrete workflow and an accountable outcome.

Measure
Success criteria agreed before the build starts.
Govern
Controls and human decisions designed into the workflow.
Transfer
Code, runbooks, and ownership stay with your team.

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6 case studies

Governed AI agent workflow with verification and human approvalAI Agents

Fortune 500 Financial Services

Enterprise AI Agent Platform

Built a multi-agent system that automated 60% of customer service operations using GPT-4 and custom RAG implementation.

45%
Cost Reduction
3min → 15sec
Response Time
+35%
Customer Satisfaction
AI AgentsGPT-4
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Clinical data signals flowing through evidence and review checkpointsHealthcare AI

Healthcare Research Institute

Medical Diagnosis LLM System

Developed an AI-powered diagnostic assistant using Claude and medical knowledge bases to support clinical decision-making.

94%
Diagnostic Accuracy
60%
Time Savings
50k+
Cases Analyzed
HealthcareClaude
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Legal documents moving through classification, citation, and human reviewLegal Tech

International Law Firm

Legal Document Intelligence

Created an intelligent document analysis system that reduced legal research time by 80% using GPT-4 and semantic search.

-80%
Research Time
10k/day
Document Processing
98%
Accuracy
Legal TechDocument AI
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Personalization signals becoming ranked retail recommendationsRetail AI

Major Retail Chain

E-commerce Personalization Engine

Built a real-time AI personalization system that increased conversion rates by 45% using LLMs and behavior analysis.

+45%
Conversion Rate
+28%
AOV Increase
+52%
Customer Engagement
E-commercePersonalization
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Transaction streams passing through real-time anomaly detectionFinTech

Digital Payment Platform

Real-time Fraud Detection AI

Deployed real-time fraud detection system using ML and graph analytics that prevented $50M in fraudulent transactions.

$50M
Fraud Prevented
98.5%
Detection Accuracy
-60%
False Positives
Fraud DetectionMachine Learning
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