Recommendation Engine Consulting and Implementation

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Empower Customers achieve Business Objectives
using AI Tools
Leverage your data
Mission
Approach
Pick right tools Derive Insights

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Current Context
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Increasing user base Increasing user offerings Short attention span

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Offerings
Recommendation
Engine
Search Engine
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• Achieve last mile user experience
• Build vs Buy
• How your users behave?
• Discover what is relevant for users
• Translate business need to search
behavior

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Benefits
Focus on your Core
• Don’t re-invent the wheel
• Go-to market faster
Continuous Improvement Auto-tune your Algorithms
• Large inventory of algorithms
• Right technique for right dataset
• Quick Iterations
• Controlled roll-out
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Typical Process
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Collect/
Normalize Data
Exploratory Data
Analysis
Initial Candidate
Algorithms
Spot Checking
Production Setup
Tweak Candidate
Algorithms
Deployment and
Integration
Measure & Evaluate
Finalize Algorithm

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CaseStudy-ProductDevelopment
E-Commerce Aggregation Platform
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• Technical Consulting
• Feature Roadmap
• Developed the product
end-to-end
• NLP backed Query
Processor
• Crawled the Web for
Products
• Classification Engine
• Recommendation
• Product catalog from 80+
website
• Infra for crawling and
scrapping
• Live price and stock
checking
Ideation Development Maintenance

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RecommendationEngineforB2CStartup
Increasing User Engagement
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• Improve user
engagement by showing
relevant content
• Modelling the problem in
terms of Data Science
• Built the recommendation
engine in two phases
• Non-NLP and NLP
• Integrate the model with
existing code-base
• Non-NLP algorithm
increased user
engagement by 30%
• NLP method increased
user engagement by 50%
Consulting Build and integrate Out-come

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Clients
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