Case Studies that prove intelligence delivers measurable impact.
From document AI to quantum optimization, explore how we've helped enterprises achieve breakthrough results with AI, Cloud, and Data solutions.
Intelligent Document Processing for a Global Insurer
LLM-driven data extraction and compliance tagging across 18M+ files.
Accuracy
Cost Reduction
SLA Time
Built a Retrieval-Augmented Generation (RAG) pipeline using OpenAI + private vector DB for claims documents.
Used domain-adapted LLM to extract clauses, coverage limits, and compliance tags from PDFs and scanned text.
Deployed via Azure Functions with event-driven chunking and secure blob storage.
Achieved 94% accuracy vs. 78% baseline using traditional regex NLP models.
Reduced manual review cost by 60% and SLA time from 7 days → 14 hours.
Multi-Cloud FinOps AI for Retail Chain
Real-time cloud optimization engine delivering 38% cost savings.
Monthly Savings
Cost Reduction
Projects Optimized
Integrated cost telemetry from AWS, Azure, and GCP using unified APIs.
Trained XGBoost-based cost predictor to forecast utilization spikes and recommend auto-scaling thresholds.
Implemented LLM-based anomaly detection agent to explain sudden billing deviations in natural language.
Deployed using Kubernetes + Prometheus + Grafana dashboards.
Result: Average monthly compute savings of $420K across 48 projects.
Agentic AI for Supply Chain Automation
Autonomous agents reducing delays in a multi-country logistics network.
Faster Shipping
Real-time Routing
Agent Types
Designed multi-agent system (Planner → Executor → Critic) architecture to manage order routing and warehouse coordination.
Agents connected via message bus (Kafka) for async event passing and state recovery.
Added LLM-based policy agent for interpreting dynamic vendor SLAs.
System self-optimized shipping time by 22%, rerouting in real-time under disruptions.
Deployed on Google Cloud Run + Pub/Sub stack for scalability.
Predictive Maintenance using Edge-to-Cloud ML
Preventing equipment downtime in large manufacturing setups.
Downtime Reduced
Alert Latency
ROI Timeline
Built IoT ingestion pipelines using AWS IoT Core → Kinesis → SageMaker.
Modeled sensor data with LSTM sequence predictors detecting early-stage vibration anomalies.
Created lightweight ONNX-optimized edge models for real-time alerts with < 200 ms latency.
Integration with cloud-based digital twin dashboards for predictive visualization.
Reduced unplanned maintenance events by 37% within 3 months.
Private Healthcare LLM with PHI Protection
On-premises GenAI platform for clinical document reasoning.
Faster Summaries
Data Breaches
HIPAA Compliant
Hosted LLM fine-tuned on de-identified clinical text inside HIPAA-secure private VPC.
Added context window filtering + role-based prompt access for PHI safety.
Integrated Retrieval layer (Weaviate + embeddings) for contextual retrieval of guidelines.
Achieved zero data exfiltration under DLP scans; compliance verified via internal audit.
Enabled clinicians to summarize case histories 4× faster.
Quantum-Inspired Optimization for Financial Portfolios
Hybrid classical–quantum model achieving faster portfolio balancing.
Faster Convergence
Better Sharpe Ratio
Pilot Partner
Developed Quantum Approximate Optimization Algorithm (QAOA) simulator with Python Qiskit.
Integrated with classical PyTorch reinforcement learner for continuous portfolio updates.
Benchmarked against classical solvers: achieved 1.8× faster convergence and 12% better Sharpe ratio.
Deployed hybrid workflow using Azure Quantum + local GPU cluster.
Prototype now in pilot with investment analytics partner.
NLP-Driven Legal Intelligence Platform
Automated clause extraction and risk classification from contracts.
F1 Score
Effort Reduced
Contracts Trained
Built NER and classification pipeline using BERT + Spacy custom entities (Obligation, Termination, Jurisdiction).
Trained model on 30K annotated contracts using weak supervision.
Implemented context-aware clause summarizer via fine-tuned Llama-2-13B model.
Accuracy: 93.6% F1 vs. 82% baseline.
Result: Reduced manual legal review effort by 70%.
Intelligent Cloud Migration Advisor
AI-guided recommendation engine for migrating legacy workloads.
Blueprints Generated
Classification Accuracy
Assessment Time
Analyzed 20 TB of legacy metadata using Python-based classifier + embedding search.
Auto-classified workloads as lift-and-shift, refactor, or rebuild.
Integrated LLM reasoning layer to explain recommendations in natural language.
Produced 1,200+ migration blueprints with 98% accuracy vs. manual classification.
Delivered via Danalitic Migration Assistant™, cutting assessment time from 5 weeks → 3 days.
DataOps & Metadata Intelligence Framework
Unified data discovery and governance through automation.
Files Classified
False Positive Rate
Automated
Created metadata scanning engine integrating with AWS S3, GCS, and Azure Blob.
Added ML models for sensitivity detection, data lineage, and PII scoring.
Connected with Elasticsearch + Neo4j for fast graph traversal and visual lineage.
Results: 40M files auto-classified with < 2% false positive rate.
Deployed via containerized microservices orchestrated on Kubernetes.
Smart Retail Analytics with Generative Insights
Real-time consumer trend discovery powered by LLMs + vector search.
Better Targeting
Automated Dashboard
Platform Built
Integrated streaming analytics (Kafka → BigQuery) with AI summarizer agent.
Generated marketing insights in plain English using LLM-based summarization.
Enabled auto-segmentation of customers based on RFM + sentiment embeddings.
Improved campaign targeting accuracy by 46%.
Fully automated dashboard built using LangChain + Streamlit.