Generative AI - Text
Our expertise in Large Language Models provides the cognitive and reasoning core for our systems, enabling them to understand and process information on a scale.
- Advanced Summarization: We have developed systems for Document Summarization that create outputs in specific, defined formats, including recursive summarization techniques for processing exceptionally large documents.
- Retrieval-Augmented Generation (RAG): Our capabilities include building RAG Chatbots and Document Q&A systems that utilize LLMs, Vector Databases, semantic chunking, text embeddings, and tool use for high-fidelity responses.
- Structured Knowledge: We engineer Knowledge Graph-based Taxonomies and Ontologies, which provide the critical structured reasoning layer for our advanced AI systems.
- Hybrid Document Parsing: We have engineered a sophisticated Heuristic-VLM-OCR Hybrid Parsing system by combining Heuristic, VLM-based, and OCR PDF Parsing methods for unparalleled data extraction accuracy.
- Model Optimization & Evaluation: We perform intensive fine-tuning and quantization of LLMs for efficient deployment and use a comprehensive framework to test text models with metrics like BLEU, ROUGE, and perplexity.
- Operational Readiness: Our organization has proven expertise in the local deployment and operationalization of all models, ensuring robust and secure implementation.
Generative AI - Audio
Creating realistic, controllable acoustic environments enables safe testing, scalable data generation, and rapid development of perception and communication systems.
- Acoustic Environment Recreation & Simulation: Generate realistic 3D soundscapes (speech, machinery, traffic, alarms) to build digital acoustic twins for testing perception, localization, and communication systems safely.
- Synthetic Data & Robustness Training: Create labeled audio scenarios with controllable noise, reverberation, and overlapping speakers to improve detection, monitoring, and speech recognition models at scale.