This document summarizes an AI company that offers recommendation engines and search engines to help customers achieve business objectives using AI tools. It describes the company's mission to leverage customer data to derive insights and pick the right AI tools. Case studies describe building an ecommerce aggregation platform and recommendation engine for a B2C startup that increased user engagement. The typical process involves collecting and analyzing data, testing algorithms, deployment, and evaluation.

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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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