Small, Fast, and Knowledgeable with Your Domain Expertise.
Tackle complex industrial challenges. Without vendor lock-in.
Leverage AI in your specific industrial context with much lower computational resources requirements than LLMs.
Create efficient, robust, and domain-specific, knowledge-based AI models for industrial-grade applications.
Democratize industrial AI and contribute to a project focused on building and sharing specialized AI models.
Apply your domain knowledge to tackle problems, with the reliability of AI solutions in an industrial setting.
Supports industrial-strength requirements of trust, safety, reliability, and scalability inherent to the industrial sector.
100-1000x faster and more efficient than LLMs, SSAs are cost-effective for Industrial AI where time and resources are critical.
OpenSSA enables knowledge query and inferencing/predictive capabilities based on your domain knowledge.
OpenSSA lets you train, fine-tune, and do whatever you want with your own SSAs. Your knowledge, your choice.
SSAs are designed to work in problem-solving "teams". Multi-SSA collaboration is a first-class design feature, not an afterthought.
Each SSA is expert in its domain. With causal reasoning, and retrieval knowledge, SSAs provide a trustable source of domain expertise.
SSMs comprise three key components: a front-end Small Language Model (SLM),
an adapter layer in the middle, and a wide range of back-end domain-knowledge sources.
SLM is a small, efficient, domain-specific model. It forms the frontend of an SSM. It can be compacted from a larger model.
Adapters (eg. LlamaIndex) provide the interface between the SLM and the domain-knowledge backends.
Support for a wide range of text files, documents, PDFs, databases, code, knowledge graphs, models, other SSMs, etc.
SSMs communicate in both unstructured (natural language) and structured APIs, catering to a variety of real-world industrial systems. The composable nature of SSMs allows for easy combination of domain-knowledge sources from multiple models.