
Liminal AI is a secure AI enablement and generative-AI governance platform delivering single-pane access to leading models behind a policy engine that protects sensitive data on every prompt and response, sanctions Shadow AI, builds custom multi-model assistants, extends governed AI into Microsoft 365, Google Workspace, Salesforce, Slack and Adobe, and exposes the same controls through an SDK — fitting operating models whose workforce is already using generative AI and whose security, privacy and compliance posture cannot continue absorbing unmanaged Shadow AI exposure. Fibi sources and negotiates Liminal AI on your behalf, at no cost to your business.
Portfolio
A governed control plane across direct chat, in-application AI and developer-embedded AI — with deterministic policy controls on every prompt and response.
Single-pane access to leading generative AI models behind a governed control plane that inspects every prompt and response — fitting operating models whose workforce is already using AI and whose security posture cannot continue treating each consumer AI tool as an unmanaged egress channel.
A policy engine applies redact, mask, warn and pass-through actions on PII, PHI, financial identifiers and internal terminology before prompts reach a model — with rehydration of masked terms in the response — fitting operating models whose regulated data cannot be sent verbatim into third-party LLM endpoints, and whose privacy posture demands a deterministic control plane rather than user-side discipline.
Sanctioned, observable AI usage across web, desktop and embedded applications replaces ad-hoc consumer AI tools — fitting operating models whose security posture has discovered employees pasting sensitive content into unmanaged AI surfaces, and whose governance posture cannot rely on blocking AI as a sustainable strategy.
Build, share and govern custom assistants configured with natural-language instructions, document context and an explicit or auto-selected model — fitting operating models whose use cases demand task-specific assistants, and whose IP and customer-data posture cannot accept consumer assistant tooling that lacks tenant isolation and policy enforcement.
A desktop helper extends the same governed AI surface into the productivity, CRM and collaboration applications already in use — fitting operating models whose adoption strategy demands AI in the flow of work, and whose architecture posture cannot tolerate a separate, unmonitored AI app on every desktop.
A Python, TypeScript and .NET SDK embeds the same prompt and response controls into employee-facing and customer-facing applications — fitting operating models whose AI strategy includes building, not only buying, and whose engineering posture cannot reinvent prompt protection on every project.
Ideal For
Operating models whose customer, transaction and advisory data fall under GLBA and bank-supervisory expectations, and whose AI adoption posture cannot tolerate verbatim regulated-data egress to third-party LLMs.
Operating models whose PHI and claims data fall under HIPAA and state insurance regulation, and whose AI strategy demands deterministic redaction and masking before any model sees the prompt.
Operating models whose student records, research IP and constituent data are governed by FERPA and equivalent rules, and whose AI policy cannot rely on user-side discipline as the primary control.
Operating models whose client confidentiality, IP and matter-bound material cannot be sent into consumer AI tools, and whose governance posture demands tenant-isolated infrastructure and full AI-usage observability.
Why Liminal AI
Structural advantages that justify Liminal AI over consumer AI tools and DIY governance.
A redact / mask / warn / pass-through engine runs on every prompt and every response, with rehydration of masked terms — fitting operating models whose regulated and proprietary data cannot leave the perimeter verbatim, and whose privacy posture demands a deterministic control plane rather than per-user discretion.
Each customer deploys into a dedicated single-tenant VPC with isolated database and data lake in the private subnet, AES-256-at-rest with rotating keys and TLS 1.2+ in transit — fitting operating models whose enterprise security posture demands tenant isolation and auditable cryptography rather than shared-tenant SaaS defaults.
Access to leading models (GPT, Claude, Gemini, Perplexity and others) plus customer-internal LLMs through one engine, with model upgrades absorbed by Liminal as new versions ship — fitting operating models whose AI strategy cannot lock to a single model vendor and whose architecture posture demands forward compatibility as the model market evolves.
Spaces (web), desktop helper across the productivity stack, and an SDK for custom applications — one control plane across direct chat, in-application AI and code-embedded AI — fitting operating models whose AI footprint spans both productivity adoption and product engineering, and whose governance posture cannot tolerate divergent controls per surface.
Why Use Fibi
Your contract is with Liminal AI either way. The difference is the comparison, sourcing and ongoing support layer around it.
| Aspect | Liminal AI Direct | Liminal AI Through Fibi |
|---|---|---|
| Pricing | Standard Liminal AI rates | Volume-negotiated — equal or better |
| Vendor comparison | Liminal AI only | Liminal AI vs other secure-AI and AI-governance providers |
| Quote turnaround | 5–10 business days | 24–72 hours across multiple options |
| Architecture review | Liminal AI solution architects | Independent advisor representing your interests |
| Post-go-live support | Liminal AI support only | Fibi escalation + Liminal AI support |
| Advisory fee | N/A | $0 — provider-funded |
FAQ
Fibi will scope your AI adoption posture, regulated-data exposure, Shadow AI footprint and buy-side / build-side surface against Liminal AI and other secure-AI and AI-governance providers — so you see how Liminal AI compares before signing, with no obligation and no sales pressure.
Compare Liminal AI against other cybersecurity providers