Case studies
In-depth stories from our work
A closer look at how we approach and deliver production AI systems — the problems, the architecture, and the results.
Structurally-Aware Predictive Maintenance for Autonomous Bot Fleets
A predictive-maintenance system for fleets of autonomous bots navigating structural rails, built to detect track defects before they cause physical grid failures — by mapping physical reality into the data model instead of relying on naive global thresholds.
Read case study →A Deterministic Multi-Agent System for FinTech Compliance
A multi-agent KYC and transaction-compliance system for a highly regulated financial environment — engineered as a deterministic "software cage" around LLMs, so every decision is traceable to hard facts and legally defensible.
Read case study →Edge Engineering for a Real-Time Smartwatch Telemetry Fleet
A life-critical health-monitoring system processing high-velocity telemetry (PPG, IMU, fall detection) from a fleet of battery-constrained smartwatches. Making it work meant bending the software to the hardware — from the wire protocol and on-device encryption to reliably commanding devices that are asleep 99% of the time.
Read case study →Production MLOps for a Dual-Head Vision Model (Detection + Segmentation)
Training a dual-head vision model is the easy part. We built the production MLOps ecosystem — on a massive Azure Databricks cluster — to tune it across competing objectives, scale distributed trials, visually debug it, and keep the training data legally clean for commercial use. The same system also powers real-time detection and anonymization of people in the video feed.
Read case study →Classifying Documents into 30,000+ Categories (Beyond Prompt Engineering)
Asking an LLM to zero-shot classify a document into one of 30,000+ categories simply fails. We replaced the naive prompt approach with a multi-stage pipeline — vector search, algorithmic guardrails, and tightly-constrained LLM reasoning — plus a human-in-the-loop app, turning a high-cardinality problem into a reliable, continuously-improving system.
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