Real Results Across Industries

The Challenge

High-pressure fracking pumps used in onshore oil & gas are subject to sudden catastrophic failures causing production shutdowns, unplanned downtime and costly repairs for onshore oilfield service companies. Threshold based alarms generate alert fatigue and limit operational trust. Traditional maintenance schedules tend to be either too frequent, reactive, wasteful or sometimes infrequent and risky.

Our Solution

Developed a Machine Learning ML-based system to predict failures days in advance, enabling proactive maintenance. Engineered features from streaming high-frequency sensor telemetry analyzed (in real-time) by bespoke Machine Learning (ML) model ensemble trained on normal operating patterns, while incorporating domain expertise. Retroactively backtested for validation. Explainable alerts (labeled by severity, persistence and anomaly frequency) with contributing factors, trend visualization and root-cause insights. End-to-end custom designed, developed and production-deployed on Google Cloud Platform (GCP).

Results Delivered

• 2-4 days advance warning before failures
• 30-40% reduction in unplanned downtime, with attractive ROI and improved safety
• 89% of failures detected (recall), acceptable alert volume (precision) - tuned thresholds
• Severity-based tiered alert generation with actionable precision
• Proactive and optimized maintenance scheduling
• Explainable AI, root cause analysis

Technologies

The Challenge

Heavy industrial machinery with high-frequency sensor telemetry have static threshold-based monitoring, which misses early performance drift and context-dependent anomalies. Normal operational variability is difficult to distinguish from abnormal behavior. Limited visibility into true operating state and early degradation.

Our Solution

Trained ML models to predict expected sensor behavior in near real-time, given current operating conditions and classify machine operating modes – compared with observed behavior. Deviation patterns (magnitude, variance changes, trends, drifts) across sensors and derived features were quantified, visualized and analyzed.

Results Delivered

• 25–40% reduction in nuisance alerts
• 1–3 weeks advance detection of abnormal operating behavior
• Early visibility into degradation and operating states
• Improved asset utilization and operational confidence

Technologies

The Challenge

As the organization scaled, internal knowledge became increasingly scattered and fragmented across system siloes and varied document platforms; Security risks, leakage and compliance challenges from public LLM tools.

Our Solution

Unified a large volume of documents with cloud-native RAG-based enterprise-grade private LLM, which can be searched within a fully-secure environment – isolated from public LLM access (via APIs) to eliminate exfiltration risk, role-based access control (RBAC), scalable architecture.

Results Delivered

• 45% reduction in research tasks time
• 82% reduction in time to find information (45 min → 8 min)
• Zero data leakage — processed in client's secure environment
• Institutional knowledge preserved efficiently, safeguarding knowledge loss from retirements and turnover

Technologies

The Challenge

98% of the human genome is non-coding, yet governs gene regulation, disease susceptibility, development and evolution. Massive volumes of high-throughput sequencing data with limited interpretability and functional insight. Translating raw sequencing data into functional understanding required new computational approaches. Extracting actionable biological insight from large-scale DNA sequencing and epigenomic assays remains a major challenge in biomedical research.

Our Solution

Designed and implemented a machine learning framework for functional annotation of the human genome, enabling researchers to characterize novel epigenomic modifications, protein-binding, gene-expressions and regulatory elements control regions at scale. Built suite of ML models for genome-wide functional annotation. Established a reusable ML foundation for large-scale genomic and epigenomic analysis.

Results Delivered

• Resulted in high-impact publications in Nature, Cell, BioInformatics Journal, Oxford Univ. Press
• Transformed raw sequencing data into biologically meaningful annotations that could be reused across experiments and research groups
• Scalable pipelines for high-throughput sequencing
• Adopted by multiple research laboratories as a core genomic analysis platform

Technologies

The Challenge

Beverage styles drive consumer expectations, shelf placement, and pricing strategy. Style boundaries are fuzzy and overlapping. Manual, intuition-driven style categorization is subjective, slows innovation and market alignment. R&D team needed a way to predict outcomes for new recipes.

Our Solution

Built a machine learning-based multi-class classification pipeline across a large number of recipes and formulations. Mapped continuous probability landscape (ingredients, flavor, recipe-level variables to multiple beverage style categories) revealing style boundaries and market white space.

Results Delivered

• ~91% (top-3), 80% (top-1) style prediction accuracy; faster product experimentation and R&D
• Identified top-5 features that drive 74% of style prediction
• Quantitative style mapping revealed positioning opportunities and insightful assessment; Style boundaries enabled precise product development
• Interpretability – via feature importance ranking and probability distributions – drove adoption
• 30–40% reduction in recipe iteration cycles during experimentation
• Apply ML in market & business strategy and product optimization in Consumer Products (CPG) – giving producers a data-driven tool for product-recipe refinement, competitive differentiation, and targeted market placement
• Aligns product innovation with commercial strategy

Technologies

The Challenge

Bio-pharma company developing novel biomarkers – high-throughput assay generating complex, high-dimensional data showing clinical progress. With limited in-house computational expertise for novel assay analytics posed challenges with FDA submission timelines.

Our Solution

Developed end-to-end, ML-driven, high-dimensional, analytics pipeline from raw high-throughput assay output to clinical-grade biomarker signatures – with advanced feature extraction and signal discovery. Applied stability selection and regularized ML to identify robust, reproducible markers – designed for regulatory and clinical workflows.

Results Delivered

• 81% prediction accuracy for treatment response
• Accelerated readiness for FDA clinical trials
• Reduced analytical bottlenecks
• Improved regulatory confidence in biomarker signals

Technologies