Domain expertise where it counts.
We specialize in four sectors where AI and data create the most durable competitive advantage. Deep domain knowledge means faster time to value.
Turn customer data into margin and loyalty
Retail is a data-intensive, margin-thin business where personalization, inventory accuracy, and demand forecasting directly affect profitability. We work with e-commerce brands and omnichannel retailers to deploy AI that improves customer outcomes and operating efficiency simultaneously.
Key Challenges We Address
- Excess inventory tied up in slow-moving SKUs
- Imprecise demand signals leading to stockouts or overstock
- High customer acquisition cost with weak lifetime value
- Personalization at scale across millions of SKUs
Demand Forecasting
ML models that factor seasonality, promotions, and external signals to accurately predict demand.
Inventory Optimization
Allocation and replenishment algorithms that reduce carrying costs while improving fill rates.
Personalization Engine
Real-time recommendation systems that surface the right product to the right customer at the right moment.
Customer LTV Modeling
Identify high-value customer segments early and model retention levers to extend lifetime value.
Automate product analytics. Answer questions your team never had time to ask.
SaaS companies generate enormous behavioral datasets — but turning event streams into actionable product insight takes weeks of analyst time per question. We build agentic analytics systems that automate the entire workflow: from raw data to root cause identification, narrative, and product recommendations. Your team asks the business question. The AI handles the rest.
Key Challenges We Address
- Analysts spending weeks on reports instead of strategic questions
- Success metric ladder not properly defined or monitored
- No clear root cause when key metrics drop unexpectedly
- Product decisions made on instinct instead of being data-informed
- No way to predict churn or identify at-risk users early
Agentic Analytics Pipelines
Multi-agent systems that take a business question from raw data to root cause identification, narrative, and product recommendation — in minutes, not weeks.
Automated Root Cause Analysis
When a metric anomaly is detected, the system frames the question, explores your data, isolates the driver, and surfaces the answer before your next standup.
Self-Learning Business Context
Analytics that create your metric ladder, extract query patterns and learn from team corrections — getting smarter and more accurate with every question asked.
Churn Prediction & Product Intelligence
Feature adoption scoring, engagement segmentation, and 30–60 day early warning models for at-risk accounts.
From reactive maintenance to predictive operations
Manufacturing margins depend on uptime, quality, and supply chain precision. We deploy AI and ML systems that shift operations from reactive to predictive — reducing unplanned downtime, improving quality yield, and building supply chain resilience.
Key Challenges We Address
- Unplanned equipment downtime disrupting production targets
- Quality defects identified too late in the process
- Supply chain disruptions with little lead time to respond
- Manual inspection processes that don't scale
Predictive Maintenance
Sensor data and ML models that identify failure signatures weeks in advance, preventing costly unplanned outages.
Computer Vision Quality Control
Real-time defect detection on production lines using computer vision — faster and more consistent than manual inspection.
Supply Chain Risk Modeling
Early warning systems for supplier disruptions using multi-source signals and probabilistic forecasting.
Production Optimization
Simulation and optimization models that maximize throughput while minimizing energy and material costs.
From fragmented sensor data to operational intelligence.
Defense and security operations generate enormous volumes of multi-source data — from sensor feeds and surveillance networks to vulnerability scan results across interconnected military systems. We design and build AI systems that fuse these sources into a coherent operating picture, automate the correlation of findings across tools and formats, and surface the threats and anomalies that matter. From maritime domain awareness to AI-enhanced cybersecurity and penetration testing support for DND/CAF practitioners.
Key Challenges We Address
- Sensor data arriving in incompatible formats with no unified operating picture
- Vast surveillance areas that exceed analyst bandwidth at any reasonable scale
- High false-positive rates from threshold-based alert systems
- Course-of-action generation based on multiple, conflicting decision objectives
Maritime Domain Awareness
Unsupervised learning of normal traffic lane distributions from historical GPS and radar data, with automatic flagging of statistically significant deviations for human review.
Multi-Sensor Fusion
Algorithms that combine AIS, GPS, LiDAR, and satellite imagery into a coherent common operating picture — regardless of format, cadence, or source.
Anomaly Detection Systems
Statistical and ML-based detectors that distinguish genuine threats from noise, reducing analyst workload while improving detection precision.
Explainable Decision Support
AI systems that surface ranked, justified recommendations — designed for high-stakes environments where the reasoning matters as much as the answer.