AskSLM provides a fully on‑premise Small Language Model platform that lets regulated enterprises train, certify, and run specialized AI models within their own infrastructure, ensuring data never leaves the organization. The solution combines a C++‑optimized inference engine, encrypted model lifecycles in Trusted Execution Environments, and a vendor‑driven marketplace for auditable, domain‑specific models, delivering real‑time inference, cost reductions, and compliance‑ready AI deployment.
Funding
Funding not disclosed
Founders
Product
Problem
Regulated organizations such as legal firms, hospitals, banks, and government agencies cannot safely use cloud‑based large language models because data must remain on‑premise to meet privacy, compliance, and audit requirements, and existing solutions lack vendor accountability and cost predictability.
Solution
AskSLM delivers a fully on‑premise Small Language Model (SLM) platform that lets enterprises train, certify, and run specialized AI models within their own infrastructure. The solution combines a C++‑optimized inference engine, a transparent training pipeline, and hardware‑bound encrypted execution to ensure data never leaves the organization. A built‑in marketplace lets vetted vendors and internal teams publish domain‑specific models, while trusted execution environments provide provenance‑tracked training data and vendor‑backed accuracy guarantees. Multi‑model concurrency and real‑time inference run on standard hardware, eliminating cloud latency, reducing operating costs, and enabling straightforward regulatory audits.
Target Audience
Primary customers are large enterprises and agencies in regulated sectors—legal, healthcare, finance, and government—that require on‑premise AI with full auditability and data control.
Features
- C++‑optimized on‑premise inference engine supporting real‑time, multi‑model execution on standard servers
- No‑code training interface for domain experts to fine‑tune models without ML engineering
- Encrypted model lifecycle and Trusted Execution Environment for provable data sovereignty
- Vendor‑driven marketplace with curated legal, medical, financial, and compliance models, protected by a trust‑broker architecture
- Transparent, auditable training pipeline that tracks data provenance and supports vendor accountability
- 5–20× cost reduction compared to cloud LLM API usage through local execution and subscription‑based licensing