AI, Machine Learning & Deep Learning
1.
Intelligent Look-Alike Audience Engine for Scalable Acquisition
Background
A leading digital marketing team wanted to scale audience reach without inflating budgets or relying on slow, manual ML workflows. They needed a way to discover new high-intent audiences in near real time.
Problem
Traditional look-alike models needed retraining each time a new audience was added. This slowed campaign launches, raised infrastructure costs, and caused inconsistent results – especially with small samples or large consumer datasets.
What We Achieved
We developed a modern, scalable audience-expansion engine that accurately identifies high-value look-alikes – without needing retraining for each new input. This allows real-time similarity scoring and quick decision-making for marketers.
Impact
Targeting Accuracy
86% accuracy, outperforming legacy models
Cost Efficiency
32% reduction in infrastructure costs
Campaign Agility
Instant model availability for faster growth campaigns
A future-ready engine that expands brand reach with precision, speed, and lower cost—unlocking campaign performance at scale.
Who Will Benefit
Scale your marketing with smarter audiences.
2.
Hyper-Personalized Recommendation Engine for Real-Time Consumer Engagement
Background
An omnichannel retailer wanted to deliver highly personalized product recommendations across digital touchpoints, but their existing system barely used their rich customer data.
Problem
Only 10% of customer data was being activated. Models took too long to train, couldn’t handle millions of customer records, and failed to support real-time recommendations – leading to lost conversions.
What We Achieved
We designed a personalization engine to generate billions of digital propensities from signals such as browsing behaviour, product attributes, and Customer 360, and to serve them as real-time propensity scores. This enabled precise next-best recommendations across channels.
Impact
Revenue Growth
19% uplift driven by accurate targeting
Training Efficiency
Reduced training time from 18 hours to 3
Omnichannel Personalization
Unified personalization across web, app, CRM, and marketing channels
A personalization platform that responds quickly to consumer intent – enabling smart journeys and increased conversions.
Value Proposition
A connected, automated manufacturing intelligence framework that enhances speed, accuracy, and operational agility.
Who Will Benefit
Upgrade your plant intelligence—automate manufacturing insights with a modern BI engine. Talk to us.
3.
Enterprise-Scale Record Linkage with AI-Enhanced Identity Resolution
Background
Enterprises often accumulate millions of customer records from multiple touchpoints. Without a unified identity, they lose visibility, spend more on marketing, and fail to deliver seamless experiences.
Problem
Deterministic match rules were rigid, error-prone, and difficult to maintain. The client struggled with low match accuracy, fragmented systems, and high compute cost for large-scale training.
What We Achieved
We introduced an AI-driven identity resolution framework that blends probabilistic ML with contextual reasoning. It works across millions of records, handles ambiguous entries, and creates a single, reliable view of each consumer.
Impact
Matching Performance
300% improvement over the legacy engine
Accuracy
38% higher accuracy than external vendors
Cost Efficiency
48% cost reduction versus the legacy solution
Data Consolidation
Eliminated redundant MDM systems across the enterprise
A governed, scalable identity layer that unlocks personalization, accurate analytics, and strong data governance.
Who Will Benefit
Unify your customer identity with AI—start your modernization journey.
4.
AI-Driven Quality Intelligence from Consumer Complaints
Background
A multi-brand organization received high volumes of consumer complaints but had limited visibility into patterns, root causes, and early warnings across its product portfolio.
Problem
With unstructured feedback spread across systems, teams struggled to detect spikes, trace issues back to batches or suppliers, and respond proactively. This led to delayed action and mounting quality risks.
What We Achieved
We built an AI-powered platform that transforms raw complaint data into actionable insights using NLP, anomaly detection, and statistical analytics—giving teams a real-time pulse on product health.
Impact
Root-Cause Detection
40–60% faster identification of issues
Analysis Efficiency
~70% reduction in manual analysis effort
Supplier Governance
Material-level issue detection for stronger oversight
Quality Monitoring
Earlier detection of quality deviation signals
A proactive quality monitoring system that prevents escalations, protects brand trust, and accelerates corrective action.
Who Will Benefit
Turn consumer feedback into predictive quality intelligence.
5.
AI-Driven Video Language Translation for Global Knowledge Sharing
Background
Enterprises create vast volumes of training, onboarding, and knowledge-sharing videos—but language remains a major barrier for global teams.
Problem
Manual translation was slow, expensive, and unable to handle large-scale content. Multispeaker videos made it harder to produce clear, structured transcripts.
What We Achieved
We developed an automated translation pipeline that converts videos into multiple languages, identifies speakers, and produces time-aligned subtitles—making content instantly accessible across regions.
Impact
Localization Speed
3–5× faster localization than manual workflows
Cost Efficiency
Significant savings by removing vendor transcription
Learning Effectiveness
Improved outcomes and knowledge retention
Knowledge Reuse
Searchable transcripts and automated summaries for scale
A scalable, cost-efficient way to democratize knowledge and boost training adoption.
Who Will Benefit
Scale your training globally—turn every video into multilingual content.
6.
ML-Driven Demand Sensing for High-Accuracy Forecasting
Background
A LATAM planning team needed a smarter way to forecast demand amid volatile market signals and inconsistent historical data.
Problem
Fragmented inputs, ambiguous outliers, and lack of ML-based prediction made forecasting slow, manual, and error-prone. Teams lacked visibility into accuracy, bias, and planner impact.
What We Achieved
We created an end-to-end demand sensing platform with unified data models, automated outlier cleaning, ML-based baseline forecasting, planner override workflows, and real-time accuracy metrics.
Impact
Operational Efficiency
Up to 50% reduction in manual effort
Forecast Visibility
Improved MAPE and FVA across planning cycles
Inventory Management
Better inventory control with fewer stockouts
Future Readiness
Foundation for autonomous forecasting ahead
A forecasting ecosystem that blends ML precision with planner intelligence for faster, smarter decision-making.
Who Will Benefit
Transform how you forecast—let ML guide your next planning cycle.
7.
Multivariate Forecasting for Multi-Retail Channel Growth
Background
A consumer health manufacturer wanted granular forecasting to support channel expansion and SKU-level performance management across multiple retailers.
Problem
Univariate models couldn’t incorporate critical demand drivers like promotions, media, distribution shifts, and inventory constraints—leading to inaccurate forecasts and misaligned supply.
What We Achieved
We deployed a multivariate forecasting engine that integrates dozens of commercial, supply, and promotional signals, supports scenario simulations, and retrains automatically every week.
Impact
Forecast Accuracy
Double-digit improvement in accuracy
Retail Coverage
Expanded to 3 major retailers across a $15B portfolio
Service Levels
Improved service with reduced excess inventory
S&OP Alignment
Stronger alignment across planning teams
A powerful forecasting foundation that adapts to market signals and supports rapid channel growth.
Who Will Benefit
Get forecasting accuracy that grows with your portfolio.
8.
Predictive Product Availability Insights for Revenue Recovery
Background
Retailers often lose sales because stores silently stop selling items that should be performing well. Traditional methods detect such gaps too late.
Problem
Manual analytics could not keep pace with thousands of stores and products. This led to missed revenue from stockouts, distribution gaps, and undetected availability issues.
What We Achieved
We created an ML-driven predictive model that identifies stores with high sales potential but low actual sales—pinpointing the root cause and highlighting recovery opportunities.
Impact
Revenue Uplift
Incremental gains driven by proactive replenishment
Analysis Speed
Cycle time reduced from days to minutes
Retail Collaboration
Stronger partnerships through data-backed insights
Scalability
Rapid onboarding with reusable templates
A predictive revenue engine that surfaces hidden opportunities before they turn into losses.
Who Will Benefit
Reveal revenue hidden in your retail network.
9.
Deep Learning for Market Trend & Momentum Analytics
Background
FMCG brands operate in fast-moving markets where trends shift rapidly. But insights teams often rely on manual, fragmented analyses across channels.
Problem
Trend detection varied by analyst, lacked consistency, and was too slow to guide promotions, pricing decisions, or portfolio adjustments. Cross-category comparison was nearly impossible.
What We Achieved
We built an automated deep-learning framework that identifies momentum shifts, growth drivers, and category trends across omnichannel datasets—delivered through standardized monthly intelligence.
Impact
Productivity
3,500+ analytics hours saved annually
Commercial Decisions
Stronger pricing, promotion, and assortment choices
Scalability
Expanded from 1 to 5 enterprise clients
Strategic ROI
Clearer ROI measurement and faster action
Always-on trend intelligence that replaces manual effort with fast, consistent, AI-driven insights.
Who Will Benefit
Stay ahead of market shifts—unlock AI-powered momentum analytics.