Case Studies
Results that speak for themselves.
Every engagement is measured by outcomes. Here's a selection of the transformative results we've delivered for enterprise clients across industries.
AI-Powered Fraud Detection for a Global Investment Bank
Deployed an ensemble machine learning system processing 2M+ daily transactions with sub-100ms inference. The solution replaced a rules-based engine that generated thousands of false positives daily, enabling the fraud team to focus on genuine threats while dramatically reducing customer friction.
Decentralized Zero-Trust Telemetry & Cryptographically Attested Synchrophasor Consensus for Multi-Node Smart Grids
Implementation of a high-throughput, Byzantine Fault-Tolerant (BFT) directed acyclic graph (DAG) ledger running consensus over a hybrid Zero-Knowledge Proof (ZKP) verification layer. Spans 240+ electrical grid distribution nodes, streaming high-frequency synchrophasor PMU telemetry over a custom zero-overhead transport protocol. Features hardware-root-of-trust (HSM/TEE) attestations to prevent man-in-the-middle grid state injection and quantum-resistant lattice cryptography for packet-level verification with zero packet overhead.
Reinforcement Learning for Magnetic Tokamak Control and Plasma Stabilization
We built a deep reinforcement learning agent to run on FPGA boards that adjusts magnetic containment coils in nuclear fusion tokamaks. It predicts plasma disruptions and stabilizes them before they touch the walls, which is a major step to prevent the containment from losing heat and stopping the reaction.
NanoScale Deep-Spatial Anomaly Classification via Multi-Spectral Edge-AI Inferences
Developed a deep convolutional autoencoder ensemble running on custom FPGA accelerators at the sub-nanometer wafer processing edge. Fuses multi-spectral optical data and plasma-sensor telemetry in real-time, preventing micro-crack propagation in silicon structures during lithography.
Generative Off Target Effect Prediction for CRISPR Gene Editing Sequences
Developed a deep transformer model that scans whole genomic sequences to predict where CRISPR cuts might cause unintended mutations. By modeling the chromatin accessibility and sequence context, it flags unsafe edit sites so researchers can design safer gene therapies with way less trial and error in the wet lab.
De-Centralized Cognitive Agent Swarms for Predictive Liquidity Risk Arbitrage
A distributed swarm of autonomous AI agents communicating via semantic vector-embedding channels. Continuously evaluates structural risk across global options and futures derivatives markets, executing localized hedging decisions without human-in-the-loop latency.
On Orbit Autonomous Navigation and Debris Avoidance via Deep RL
We deployed a tiny deep reinforcement learning system on satellite computer hardware to handle navigation and collision avoidance in real time. It calculates thruster burn paths autonomously, which saves fuel and avoids the high latency you get when you have to wait for instructions from ground stations.
Cognitive Document Extraction & Self-Correcting LLM Agent Swarms for Claims Resolution
Deploying a hierarchical multi-agent LLM framework executing self-reflective refinement loops over unstructured medical and insurance documentation. Integrates with existing core legacy databases to extract clinical facts, resolve billing discrepancies, and issue payouts.
Variational Quantum Classical Neural Network for Molecular Ground State Approximations
Created a hybrid neural network solver that helps noisy quantum computers simulate molecular energy states. By offloading the hardest parts of the optimization to classical neural networks, it dramatically reduces the number of quantum gates needed, making molecule design possible on current generation hardware.
Acoustic Emission Machine Learning & Deep Temporal Forecasts for Offshore Drilling Rigs
Applying convolutional recurrent neural networks (CRNN) to high-frequency hydrophone and vibro-acoustic telemetry streams, predicting mechanical failures in subsea blowout preventers up to 72 hours in advance.
Spiking Neural Network on Neuromorphic Hardware for Event Camera Navigating Drones
Developed an asynchronous spiking neural network that runs on brain inspired hardware to guide drones in GPS denied areas. It processes events from custom sensors instantly, which lets the drone fly through tight spaces and avoid obstacles without draining the main battery or using heavy GPUs.
Decentralized Multi Agent RL for Bidirectional EV Fleet Charging Optimization
We built a decentralized multi agent reinforcement learning controller that manages thousands of electric vehicles acting as a virtual power plant. It coordinates when to charge or feed energy back into the grid, balancing local demand spikes and preserving battery health without needing central control.
Cross-Modal Patient Representation Embedding & Synthetic Control Arm Generator
Fusing transformer-based tabular, electronic health record (EHR) text, and genomic sequence embedding vectors using a unified cross-modal transformer. Generates high-fidelity synthetic patient cohorts and identifies optimal clinical study candidates while maintaining privacy through differential privacy guarantees.
Multi-Language Conversational Agent with Edge Speech-to-Text and RLHF Guardrails
An enterprise-wide customer experience engine running custom-finetuned Llama 3 models on private infrastructure, using low-latency speech encoders and semantic safety guardrails to triage complex technical support issues.
Zero Trust Migration for a Fortune 500 Manufacturer
Complete zero-trust architecture migration across 12,000 endpoints and 45 facilities. Micro-segmentation, continuous verification, and automated threat response.
Clinical Data Platform for Multi-Site Research
HIPAA-compliant data mesh architecture enabling real-time clinical data sharing across 30 research sites. Accelerated drug trial enrollment by 40%.
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