Machine Learning Without the Hype and Only the Benefit

TSHR’s machine learning approach prioritizes practical solutions that enhance navigation and decision-making without falling prey to common ML pitfalls like hallucination. Our focus is on providing dependable support tools that maintain accuracy and transparency.

Our Approach

• Continuous Advancement: We stay current with the latest developments in machine learning, rigorously testing each model to ensure it is beneficial and free from pitfalls like hallucination.

• Flexible and Efficient Tech Stack: Our technology stack allows for the seamless integration of new models, making it efficient to update as the field progresses.

• Optimized Model Selection: By selecting a range of models tailored to specific tasks, we ensure high efficiency without demanding extensive computational power, balancing performance and cost.

• Cost-Effective Training and Hosting: We emphasize cost-effective solutions for training and hosting, optimizing resources to keep machine learning accessible and sustainable.

• Synthetic Data Expertise: We specialize in generating high-quality synthetic datasets for model training, reducing the need for organizations to supply large volumes of real data. This approach supports data privacy and enables effective training even when substantive data is unavailable.

Examples

• Semantic Search with Vectorization Models: We use advanced vectorization techniques to create semantic search functions that deliver fast, relevant results across existing databases, enhancing search reliability.

• Fine-Tuning Open Source LLMs: Our expertise includes fine-tuning open-source models like Roberta, LLaMA, and Gemma to meet specific requirements. Using open-source solutions allows organizations to host models internally, maintaining privacy by avoiding the need to send sensitive data through external APIs.

• Multi-Label Classification for Long Documents: TSHR’s multi-label classifiers handle lengthy documents with precision, allowing accurate categorization for complex research and analysis tasks.

• Domain-Specific Language Adaptation: By fine-tuning large language models, we make specialized data accessible to users without domain expertise. This enables users to engage meaningfully with complex information and obtain relevant insights without needing a deep understanding of industry-specific terminology. Such tools have been created for the media and legal industry.