LE
LucenEdge
LucenEdge
Research-grade machine learning engineered for production. Our ML team brings academic rigor to business problems — custom architectures, rigorous validation, and deployment pipelines that keep models performing long after launch.
120+
Models in production
96%
Peak model accuracy
12ms
Avg inference latency
Purpose-built models trained on your data — classification, regression, clustering, and generative.
Transfer learning and fine-tuning of foundation models (LLMs, vision transformers) on your domain.
Managed data annotation pipelines with quality control for supervised learning at any scale.
CI/CD for ML — automated retraining, model versioning, A/B testing, and drift monitoring.
Neural network architectures for vision, language, audio, and tabular data problems.
Demand forecasting, anomaly detection, and predictive maintenance for temporal data.
Unsupervised and semi-supervised models that flag fraud, equipment failure, and data quality issues.
Semantic search, document Q&A, summarization, and information extraction from unstructured text.
Define the ML problem type, success metrics, and minimum viable dataset requirements.
Data audit, feature engineering, train/val/test splitting, and augmentation strategies.
Rapid model experimentation with tracked runs, hyperparameter search, and ablation studies.
Model optimization (quantization, distillation), containerization, and inference API setup.
Data drift detection, performance monitoring, and automated retraining triggers.
LogiStream was over-ordering by 23% each quarter due to relying on Excel-based demand forecasting. Excess inventory was costing $2.1M annually in storage and write-offs.
We built a gradient boosting + LSTM ensemble model trained on 4 years of their sales, weather, and events data. Forecast accuracy improved from 61% to 94%, eliminating the inventory waste.
Tell us about your project and we'll have a solution architect reach out within 48 hours.
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