Case Studies

Challenge

This top-20 global bank was processing over 50 million daily transactions through a legacy rules-based fraud detection system plagued by a 12% false positive rate. Legitimate customers were being flagged and blocked, costing the bank customer trust and millions in lost revenue. Meanwhile, increasingly sophisticated fraud rings were exploiting pattern gaps, resulting in $350M+ in annual fraud losses across credit card, wire transfer, and digital payment channels.

Solution

Shailka designed and deployed a multi-layered AI fraud detection platform that operates in real time across all transaction channels. We built an ensemble of deep learning models — including graph neural networks for detecting organized fraud rings and transformer-based sequence models for behavioral anomaly detection. The system ingests 200+ transaction features, device fingerprints, geolocation signals, and historical behavioral patterns to score every transaction in under 50ms. A continuous learning pipeline retrains models weekly on new fraud patterns, while an explainability layer provides compliance-ready reasoning for every flagged transaction.

Key Results

Technologies

Challenge

This Fortune 50 pharmaceutical company faced an unsustainable drug discovery pipeline — averaging 5.2 years from target identification to IND filing, with a 92% failure rate in preclinical stages. Their medicinal chemistry teams were manually screening thousands of molecular candidates, and computational chemistry tools were siloed across research divisions. R&D costs had ballooned to $4.8B annually with declining productivity, putting pressure on the entire therapeutic pipeline across oncology, immunology, and rare disease programs.

Solution

Shailka built an end-to-end AI-powered drug discovery platform combining generative molecular design with physics-based simulation. We developed custom large language models trained on proprietary molecular datasets (500M+ compounds) to generate novel drug candidates optimized for target binding affinity, ADMET properties, and synthetic accessibility. The platform integrates molecular dynamics simulations for binding pose prediction, a reinforcement learning module for multi-objective optimization, and an automated retrosynthesis planner. We also built a federated learning framework allowing cross-division collaboration without exposing proprietary compound libraries.

Key Results

Technologies

Challenge

This Fortune 100 omnichannel retailer with 4,500+ stores and 100M+ active customers was losing market share to digital-native competitors. Their existing recommendation system used collaborative filtering with batch-processed data, resulting in stale, generic suggestions that failed to account for real-time browsing behavior, seasonal trends, or cross-channel interactions. Cart abandonment sat at 74%, email click-through rates had dropped to 1.8%, and average revenue per customer had plateaued for three consecutive years.

Solution

Shailka designed and deployed a real-time hyper-personalization platform that unifies customer signals across web, mobile app, email, in-store POS, and loyalty program interactions. The system uses a deep learning recommendation engine combining collaborative filtering, content-based features, and sequential behavior modeling via transformer architectures. It processes clickstream data in real time through an event streaming pipeline, updating customer preference vectors within 200ms of each interaction. We built personalized pricing, dynamic email content generation, and an AI-powered visual search feature that lets customers photograph products to find similar items. A multi-armed bandit framework continuously optimizes placement and ranking strategies across all touchpoints.

Key Results

Technologies

Challenge

This national energy provider manages a grid serving 28M households across a territory with rapidly growing renewable energy sources — solar and wind now comprising 38% of capacity. The intermittent nature of renewables was causing dangerous frequency fluctuations, with 340+ grid instability events in the prior year. Legacy SCADA systems couldn't forecast renewable generation accurately, and manual load balancing was resulting in $420M annually in wasted energy through curtailment and emergency fossil-fuel spinning reserves. Regulatory mandates required 50% renewable integration within 3 years.

Solution

Shailka built a comprehensive AI-powered grid management platform anchored by a real-time digital twin of the entire national grid. The digital twin ingests data from 85,000+ IoT sensors, smart meters, weather stations, and satellite imagery to model grid state with sub-second granularity. We developed ensemble forecasting models combining gradient-boosted trees, LSTMs, and physics-informed neural networks to predict renewable generation, demand patterns, and equipment failure probabilities up to 72 hours ahead. An AI-powered dispatch optimization engine uses mixed-integer linear programming with reinforcement learning to balance supply and demand across 200+ generation assets. The platform also includes automated demand response orchestration, dynamic pricing recommendations, and a predictive maintenance system for transformers and transmission lines.

Key Results

Technologies

Challenge

This national postal service, one of the country's largest employers with 120,000+ staff and 12,000 branches, was struggling with decades-old legacy systems — over 45 fragmented applications running on mainframes and proprietary middleware. Service quality had declined steadily: average parcel delivery took 5.2 days (vs. 1.5 for competitors), customer complaints had increased 60% year-over-year, and operational costs were growing at 8% annually despite flat volumes. The organization needed to serve 45M citizens with a modern digital-first experience while managing a workforce transition and maintaining uninterrupted service delivery during migration.

Solution

Shailka executed a complete digital transformation spanning customer-facing services, logistics optimization, and back-office operations. We migrated 45 legacy applications to a cloud-native microservices architecture on Kubernetes, implementing a strangler-fig migration pattern to ensure zero service interruption. Key deliverables included: an AI-powered route optimization engine that dynamically plans 35,000+ daily delivery routes based on real-time traffic, weather, and parcel volume data; a conversational AI customer service platform handling 70% of inquiries without human escalation; a computer vision system for automated parcel sorting and damage detection; a workforce management AI that optimizes shift scheduling across 12,000 branches; and a citizen-facing mobile app and web portal with real-time tracking, digital identity verification, and self-service capabilities.

Key Results

Technologies

Challenge

This global automotive manufacturer operating 14 production plants across 6 countries was experiencing an 8% defect rate on its flagship vehicle line, translating to $500M+ in annual losses from scrap, rework, warranty claims, and recalls. The quality inspection process relied on manual visual checks at end-of-line stations, catching defects too late in the process. Unplanned equipment downtime averaged 340 hours per plant annually, disrupting just-in-time supply chains and requiring expensive emergency repairs. With 2,400+ robots and 15,000+ IoT-connected machines, the data existed but remained siloed in disparate historian databases with no unified analytics layer.

Solution

Shailka deployed an integrated AI manufacturing intelligence platform across all 14 production plants. For quality control, we installed 1,200+ high-resolution camera systems with edge-deployed computer vision models that inspect every component at 12 critical stages of the assembly process — detecting surface defects, dimensional deviations, weld quality issues, and paint imperfections at the micron level. For predictive maintenance, we built a unified IoT data platform ingesting vibration, temperature, pressure, and acoustic data from 15,000+ machines, using temporal convolutional networks and survival analysis models to predict equipment failures 48-72 hours in advance. A digital twin of each production line enables simulation-based optimization of line speeds, robot trajectories, and process parameters. The platform includes a real-time production dashboard used by plant managers and a mobile alert system for maintenance crews.

Key Results

Technologies

Challenge

This tier-1 global telecom serving 200M+ subscribers across 12 countries was plagued by network reliability issues — averaging 1,200+ critical incidents per month with a mean time to resolution (MTTR) of 4.5 hours. Their network operations center (NOC) employed 800+ engineers working in reactive mode, manually triaging alarms from 500,000+ network elements across 4G, 5G, fiber, and satellite infrastructure. Customer churn attributable to network quality had reached 8.2% annually, and NPS scores were declining quarter-over-quarter. The complexity of multi-vendor, multi-technology network made root-cause analysis extremely difficult, with engineers spending 60% of incident time just identifying the source.

Solution

Shailka built an AIOps platform that transformed the NOC from reactive to predictive and self-healing. The platform ingests and correlates data from 500,000+ network elements in real time — including performance metrics, configuration data, alarm logs, weather data, and subscriber usage patterns. We developed a causal AI engine that automatically performs root-cause analysis by building dynamic causal graphs of network dependencies, reducing triage time from hours to minutes. A predictive failure detection system using temporal graph neural networks identifies potential outages 2-6 hours before they impact subscribers. We implemented automated remediation playbooks for the 50 most common incident types, enabling the system to self-heal 65% of incidents without human intervention. The platform also includes an AI-powered capacity planning module that forecasts demand growth at cell-site granularity and recommends infrastructure investments.

Key Results

Technologies

Challenge

This top-10 global insurer processing 8M+ claims annually across auto, property, health, and liability lines was drowning in manual processes. Average claims processing took 15.3 days, with adjusters spending 70% of their time on document review, data entry, and compliance checks rather than decision-making. Fraudulent claims were estimated at 12% of total volume ($1.8B annually), but the existing rules-based fraud detection system only caught 22% of confirmed fraud. Customer satisfaction scores for the claims experience were at 3.1/5, driving policyholder attrition. The company had 12 different legacy claims systems acquired through mergers, with no unified data model.

Solution

Shailka built a unified intelligent claims processing platform that automates the entire lifecycle from first notice of loss (FNOL) to settlement. The platform features: a multi-modal document processing engine using fine-tuned LLMs to extract, classify, and validate information from claim forms, medical records, police reports, invoices, and photographs — processing 200+ document types across 8 languages. An AI triage engine that scores incoming claims by complexity, fraud risk, and estimated value to route them optimally — auto-approving straightforward claims and flagging high-risk ones for senior adjusters. A generative AI-powered fraud detection system that analyzes claim narratives, cross-references historical patterns, social network connections, and external databases to identify organized fraud rings and staged incidents. A computer vision module for auto/property damage assessment that estimates repair costs from photographs with 92% accuracy. We unified the 12 legacy systems into a single cloud-native platform with a modern API layer.

Key Results

Technologies

Challenge

This Fortune 500 diversified conglomerate with $45B in annual revenue operated 3,200+ applications across 8 business units, running on aging on-premises infrastructure in 6 data centers. Infrastructure costs were growing at 15% annually while performance and reliability declined — average application availability was 99.2% (vs. 99.99% target), and deploying new features took 4-6 months through a waterfall release process. The technology debt was estimated at $2.1B, with 40% of applications running on unsupported operating systems or middleware. A failed previous migration attempt had stalled at 200 applications over 18 months, creating organizational skepticism about cloud transformation.

Solution

Shailka designed and executed a comprehensive cloud migration program using an AI-powered application assessment and migration factory approach. We built a custom AI tool that automatically analyzed all 3,200+ applications — scanning source code, infrastructure dependencies, data flows, and performance profiles to classify each application into the optimal migration strategy (rehost, replatform, refactor, rebuild, or retire). This reduced assessment time from 40 hours per application to 2 hours. We established a migration factory with automated CI/CD pipelines, infrastructure-as-code templates, and automated testing frameworks that could migrate 50-80 applications per sprint. For the 400+ applications requiring modernization, we decomposed monoliths into microservices using domain-driven design, containerized workloads on Kubernetes, and implemented event-driven architectures. We built a multi-cloud landing zone spanning AWS and Azure with unified governance, cost management, and security controls. A dedicated cloud enablement program trained 2,000+ internal engineers on cloud-native development practices.

Key Results

Technologies

Challenge

This international governance organization coordinating AI policy across 15 member countries faced a critical gap: each nation was deploying AI in public services — criminal justice, social welfare, immigration, healthcare — without consistent standards for fairness, transparency, or accountability. Documented cases of biased AI in criminal sentencing, discriminatory welfare eligibility algorithms, and opaque immigration screening models had eroded public trust and triggered citizen lawsuits across 6 member states. There was no common framework for AI impact assessments, no standardized bias testing methodology, and no cross-border mechanism for AI incident reporting. Public trust in government AI services had dropped to 31% across member nations.

Solution

Shailka developed a comprehensive, implementable ethical AI governance framework spanning policy, technology, and organizational dimensions. On the policy side, we created a tiered AI risk classification system (4 risk levels with corresponding governance requirements), mandatory AI impact assessment templates, transparency and explainability standards calibrated by risk level, and cross-border incident reporting protocols. On the technology side, we built an open-source AI Fairness Toolkit that includes: automated bias detection across 15 fairness metrics (demographic parity, equalized odds, calibration, etc.) for any ML model; counterfactual fairness testing that measures how predictions change when protected attributes are altered; a model explainability suite generating human-readable explanations for AI decisions using SHAP, LIME, and counterfactual explanations; and a continuous monitoring dashboard that tracks fairness metrics in production and alerts when drift occurs. We also designed organizational governance structures including AI ethics boards, citizen appeal mechanisms, and mandatory AI literacy programs for public officials. The framework was validated through pilot deployments in criminal justice, healthcare, and social welfare AI systems across 4 countries.

Key Results

Technologies