Enterprises deploying generative AI face difficulty guaranteeing that model outputs respect safety, regulatory, and brand policies across multiple languages and deployment contexts. Without real‑time governance, outputs can drift, trigger compliance violations, or damage brand reputation. Existing solutions are often cloud‑only, opaque, and require extensive engineering effort to integrate.
Funding
Funding not disclosed
Founders
Product
Problem
Enterprises deploying generative AI face difficulty guaranteeing that model outputs respect safety, regulatory, and brand policies across multiple languages and deployment contexts. Without real‑time governance, outputs can drift, trigger compliance violations, or damage brand reputation. Existing solutions are often cloud‑only, opaque, and require extensive engineering effort to integrate.
Solution
Principled Intelligence delivers open‑source, multilingual small language models (ScopeGuard) and a composable agents framework (Orbitals) that embed corporate policy enforcement directly into AI pipelines. The models run on‑premise or in any private cloud, providing full data sovereignty and sub‑100 ms latency on commodity hardware. Continuous red‑team testing and adversarial probing harden the models before and after launch, while dynamic guardrails evaluate inputs and outputs in real time. Agents act as filters, supervisors, adversarial probes, and monitors, exposing policy breaches and escalating exceptions through configurable APIs. A unified dashboard supplies live safety scores, incident logs, and multilingual compliance reports, enabling rapid remediation without altering the underlying generative model.
Target Audience
Primary customers are large enterprises in regulated, multilingual sectors—such as finance, healthcare, telecommunications, and global tech firms—that require robust AI governance for mission‑critical generative AI applications.
Features
- ScopeGuard SLMs supporting 5+ languages with culturally‑aware evaluation suites and <100 ms inference latency
- On‑premise and cloud‑agnostic deployment packages that preserve data sovereignty and minimize infrastructure cost
- Continuous red‑team and adversarial testing harnesses that automatically surface regressions and policy violations
- Orbitals library of composable agents (Guard, Supervisor, Adversarial, Monitor) that can be inserted into any workflow via lightweight SDKs
- Real‑time policy evaluation engine with dynamic rule updates and multi‑policy scoring dashboards
- API‑first integration layer compatible with existing LLM stacks, toolkits, and enterprise orchestration platforms
- Automated incident escalation and rational‑explanation generation for human review
- Open‑source licensing model with optional enterprise support and custom integration services