Hyde partners with enterprises to convert their proprietary data and domain expertise into specialized reasoning engines that operate as private, high‑performance AI agents. Their platform delivers superior accuracy, lower inference costs, and zero data leakage by building small, owned models that improve over time, enabling use cases such as regulatory report generation, demand‑forecasting agents, and code‑generation assistants. Clients report up to 4× gains in sales conversion and multi‑percentage improvements in forecasting accuracy.
Funding
Funding not disclosed
Founders
Product
Problem
Enterprises often rely on generic large language models that lack deep domain knowledge, leading to lower accuracy, higher inference costs, and risks of exposing proprietary data when handling specialized tasks such as regulatory reporting or demand forecasting.
Solution
Hyde partners with organizations to transform their own data and subject‑matter expertise into dedicated reasoning engines. These specialized AI agents are trained on proprietary datasets, delivering higher accuracy and contextual understanding for niche use cases while keeping intellectual property in‑house. The platform continuously refines the models as they are used, reducing inference latency and cost by up to tenfold compared with generalist alternatives. Hyde’s approach eliminates dependence on external model providers, offering a defensible competitive edge and predictable long‑term ownership expenses. Clients can deploy the resulting agents as white‑label products or internal tools, benefiting from near‑100 % accuracy in tasks such as SEC report generation or demand‑forecasting improvements of over ten percentage points.
Target Audience
Primary customers are large enterprises in regulated industries, retail, finance, and engineering that require high‑accuracy, domain‑specific AI agents while protecting their data and reducing operational costs.
Features
- Custom reasoning engine creation using a client’s proprietary data and expertise
- Continuous performance improvement through usage‑driven fine‑tuning
- Up to 10× lower inference cost and latency at scale
- Zero data leakage guarantees with on‑premise or private‑cloud deployment options
- Specialized models for regulatory reporting, time‑series forecasting, code generation, and sensor‑data analysis
- White‑label distribution capability for revenue‑generating AI products