Terno provides an enterprise‑grade AI data‑scientist platform that lets organizations query their own data securely and receive accurate, execution‑grounded insights. The system runs inside a private cloud or on‑premises, ensuring no raw data leaves the network and allowing full control over LLM models, inference, and compliance logging.
Funding
Funding not disclosed
Founders
Product
Problem
Organizations with mission‑critical data need to extract actionable insights quickly, but traditional analytics tools either require data to be moved to external services or rely on large language models that can hallucinate and expose sensitive information.
Solution
Terno offers an enterprise‑grade AI data‑science platform that runs entirely within a private cloud or on‑premises, ensuring that raw data never leaves the organization’s network. Users interact with their databases through natural‑language queries, while the system translates these into deterministic, rule‑based executions that enforce fine‑grained table, column, and row‑level access controls. The platform supports self‑hosted large language models, giving full control over model selection, inference logging, and compliance. Answers are grounded in actual query results rather than generated text, eliminating hallucinations and providing reliable, execution‑backed insights. A deterministic security layer guarantees zero data leakage and prevents policy bypass, making the solution suitable for highly regulated and security‑sensitive workloads.
Target Audience
Primary customers are large enterprises, government agencies, and financial institutions that handle sensitive, mission‑critical data and require secure, compliant analytics capabilities.
Features
- Private deployment option that runs inside the customer’s cloud or on‑premises infrastructure
- Deterministic rule‑based query transpilation to enforce strict data access policies
- Fine‑grained access control at table, column, and row levels across all analytics workflows
- Support for self‑hosted large language models with full control over inference, logging, and compliance
- Hallucination‑free analytics: responses are derived from actual query execution results
- Secure handling of metadata only; raw data values are never shared with external LLM services