01 / PRODUCTS & DEMOS

Built for practical impact.

Turning advances in grounded perception and efficient AI into practical tools for industrial inspection, multimodal reasoning, and model development.

01

Industrial Visual Inspection

Lightweight anomaly detection and reliable visual classification for industrial quality inspection, with alternatives to computationally expensive models.

02

Compact Multimodal Intelligence

Efficient vision-language models that combine classification and reasoning for specialized applications and resource-constrained environments.

03

Efficient Model Development

Automated evaluation, checkpoint selection, and numerical optimization to improve model reliability while reducing development and post-training costs.

Compact VLM Classification & Reasoning

Large vision-language models can be computationally expensive to deploy, particularly for specialized visual understanding tasks that require both accurate classification and interpretable reasoning. We develop compact VLM architectures with task-specific classification and reasoning capabilities, combining efficient multimodal representations with specialized post-training strategies. In our evaluation, an 8B model achieved an F1 score of 0.863, outperforming a larger 31B model (0.834) on 1,500 held-out expert-labeled examples.

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Lightweight Adaptive Anomaly Detection

Industrial defect detection requires reliable inspection with minimal computational overhead. We combine normalized cross-correlation (NCC) template matching with statistical KDE and image-adaptive fusion to provide a lightweight alternative to deep neural networks. On ceramic debris detection, our method achieved 95.2% F1, compared with 99.3% for ResNet-50, demonstrating competitive accuracy without neural-network training.

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Automated Model Evaluation & Checkpoint Selection

Selecting reliable model checkpoints from extensive post-training experiments is costly and difficult when evaluation depends on inconsistent or expensive manual judgments. We develop an automated evaluation framework that progressively combines pointwise scoring, listwise ranking, and pairwise comparison to assess model quality and identify promising checkpoints. The framework supports systematic, repeatable model selection and reduces the need for exhaustive manual comparisons.

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Newton-Based Optimization

Hyperparameter optimization and post-training can require many expensive evaluations, slowing model development and increasing computational costs. We explore Newton-inspired numerical optimization methods that use local objective information to guide efficient parameter updates and search. In a reported XGBoost experiment, the approach required 8 optimization iterations versus 320 grid-search evaluations to reach competitive performance. The framework also targets efficient optimization in VLM post-training and classical machine learning.

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