Case Studies
AI Products That Deliver Results
Real AI systems solving real business problems. See how we've transformed Travel, Energy, and Healthcare with intelligent products.
Travel & Hospitality
AI-Powered Intelligent Itinerary Planner
Premium Online Travel Platform • Series B Startup
The Challenge
Travel booking platforms face a critical problem: customers spend hours manually building itineraries from 1000s of options. This leads to: • High cart abandonment (60%+ don't complete bookings) • Poor customer experience (too many choices paralyze users) • Low average order value (customers pick cheapest, not best) • No personalization (generic recommendations)
Our Solution
We built an Agentic AI system that: 1. Understands user preferences from past trips + implicit signals 2. Uses autonomous agents to plan multi-day itineraries 3. Integrates real-time availability + pricing APIs 4. Generates personalized travel plans in <2 seconds 5. Adapts to budget, travel style, and constraints Tech Stack: • Agentic AI (task decomposition, planning) • LLM for natural language understanding • RAG for travel knowledge base (attractions, restaurants) • Real-time APIs (flights, hotels, activities) • Vector DB for semantic search
TIMELINE
16 weeks
TEAM
3 AI engineers + 1 product manager
Results & Impact
Cart completion
40%→78%
+95%
AOV
₹15,000→₹24,000
+60%
Plan generation
15 min manual→2 sec AI
450x faster
User satisfaction
6.2/10→9.1/10
+47%
Monthly bookings
50K+
Customer churn
35%→12%
-66%
Energy & Utilities
Predictive Grid Analytics & Anomaly Detection
Large Regional Power Utility • Fortune 1000
The Challenge
Grid operators face unpredictable demand, equipment failures, and inefficient planning: • Demand forecasting 30-40% inaccurate (15% on average) • Catastrophic equipment failures go undetected until breakdown • Transformer overloads cause blackouts in peak hours • Manual load balancing leads to wasted capacity • Annual losses from preventable failures: ₹2Cr+
Our Solution
We built an ML-powered analytics platform: 1. Time-series forecasting models (LSTM + Transformer architectures) 2. Anomaly detection for equipment health 3. Predictive maintenance alerts (predict failures 2-4 weeks early) 4. Real-time load balancing recommendations 5. Integration with SCADA systems for live data Tech Stack: • PyTorch for deep learning models • Time-series forecasting (LSTM, Prophet) • Anomaly detection (Isolation Forest + neural nets) • Real-time streaming (Kafka) • Cloud data warehouse (BigQuery)
TIMELINE
20 weeks
TEAM
2 ML engineers + 1 DevOps + infrastructure team
Results & Impact
Forecast accuracy
75%→98%
+23%
Equipment failure detection
Post-mortem→2-4 weeks prior
Predictive
Blackout prevention
12/year→2/year
-83%
Wasted capacity
22%→8%
-64%
Annual loss prevention
₹2.1 Cr
Maintenance cost savings
₹1.8 Cr/year
E-Commerce & B2B
AI-Powered B2B E-Commerce Platform
Premium Food & Beverage Supplier • 500+ B2B clients, Pan-India
The Challenge
B2B food suppliers struggle with inefficient ordering and pricing: • Manual quote generation (1-2 days turnaround, 40% errors) • Static pricing (no bulk discounts, location-based tiers) • Limited visibility (distributors can't track orders/inventory) • Paper-based contracts and compliance • High operational costs in sales team • No personalization (one-size-fits-all approach)
Our Solution
We built a full-stack B2B E-commerce platform with AI: 1. AI-powered dynamic pricing engine (location, volume, history) 2. Instant quote generation (NLP → pricing rules) 3. Buyer portal (order tracking, payment, compliance docs) 4. Inventory management with demand forecasting 5. Seller dashboard (analytics, customer insights) 6. Compliance automation (GST invoices, delivery docs) Tech Stack: • Next.js + React for responsive UI • Node.js backend with Express • PostgreSQL for transactional data • Redis for caching & real-time updates • ML models for demand forecasting • Razorpay for payments
TIMELINE
22 weeks
TEAM
3 full-stack devs + 1 PM + 1 DevOps
Results & Impact
Quote generation
24-48 hours→< 2 minutes
720x faster
Quote accuracy
60%→99%
+65%
Active B2B buyers
500+
Monthly orders
5,000+
Revenue per buyer
₹50k/year→₹125k/year
+150%
Sales team effort
Full-time→20% automation
80% reduction
Healthcare
AI Medical Documentation & Structured Data Extraction
Multi-Specialty Hospital Chain • 500+ beds, 200+ doctors
The Challenge
Doctors spend 2-3 hours daily on documentation, leading to: • Burnout from paperwork (documentation kills productivity) • Diagnosis delays (time spent on notes, not patient care) • Manual transcription errors (patient safety risk) • Incomplete records (time pressure = poor documentation) • Billing delays (revenue impact ₹50L+/year) • Compliance violations (HIPAA, FHIR standards)
Our Solution
We built a RAG + LLM system for instant medical documentation: 1. Doctor dictates during/after consultation (voice input) 2. AI converts to structured medical records in real-time 3. RAG system pulls relevant medical history, guidelines 4. Auto-populates chief complaint, findings, assessment 5. Generates billing codes (CPT) automatically 6. HIPAA-compliant, audit logs for compliance Tech Stack: • Speech-to-text (OpenAI Whisper + fine-tuning) • LLM for clinical documentation (GPT-4 + medical fine-tuning) • RAG for patient history + medical knowledge base • Vector DB for semantic search on past notes • FHIR-compliant output format • AWS for HIPAA compliance
TIMELINE
18 weeks
TEAM
2 LLM engineers + 1 healthcare domain expert + compliance
Results & Impact
Documentation time
2.5 hours/day→20 min/day
-92%
Doctor productivity
Baseline→+3 hours/day
+3 hrs/day
Patients seen/doctor
18/day→25/day
+39%
Documentation errors
12%→<1%
-92%
Billing delay
7 days avg→24 hours
-71%
Revenue recovery
₹65L/year
Technologies Behind These Products
🤖
Agentic AI
Used in: Travel case
📚
RAG Systems
Used in: Travel, Healthcare
🧠
LLM Fine-tuning
Used in: Healthcare
⚡
Time-series ML
Used in: Energy case
🔍
Anomaly Detection
Used in: Energy case
🎙️
Speech-to-Text
Used in: Healthcare
📊
Data Pipelines
Used in: Energy case
🌐
API Integrations
Used in: All cases
Your Product Could Be Next
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