Compare Ask Sage vs. Liminal

Ask Sage is a robust generative AI platform designed for government, defense, and regulated entities that require the handling of sensitive information within mission-critical workflows. It offers a single multimodal workspace that integrates over 150 different models, including commercial, frontier, and open-source options, and facilitates the generation and analysis of text, code, images, videos, and audio, enabling flexibility without binding teams to a sole provider. Users can input their organizational data just once and leverage it across multiple models for various tasks such as grounded document Q&A, drafting, summarization, knowledge management, policy validation, data analysis, and outputs tailored to specific roles. The platform features Ask Sage Chat, which provides a conversational interface, while Workbook consolidates sources, memos, shared chat histories, and team collaboration into a unified document-oriented space. Furthermore, Agent Builder offers a visual, node-based canvas that allows users to create, preview, reuse, monitor, and orchestrate automated workflows without requiring coding skills, promoting accessibility for all team members. This comprehensive approach streamlines processes and enhances productivity across diverse organizational functions.

Liminal gives regulated organizations a single governed environment for AI. Rather than securing models an organization has already deployed, Liminal supplies the access itself, so protection is applied before anything reaches a provider. Employees work with leading models from Anthropic, OpenAI, Google and others through one interface, with policy-based routing and no per-provider contracts. Adding a new model becomes a policy decision rather than an integration project, and teams are never stuck waiting for procurement to catch up with what the market released. Sensitive terms are identified within each prompt and masked, redacted, flagged or blocked according to rules defined by data category rather than by department. Masked values are restored in the response, which keeps output usable while regulated information stays inside the organization. Employees never have to sanitize inputs by hand, which is what makes the governed path the one they actually choose. Administrators see who used which model, what rules were applied and where violations occurred. That record is generated at the time of the interaction, so audit requests are answered from existing evidence instead of reconstruction after the fact. Activity can be fed into existing security tooling and reviewed alongside other events rather than in isolation. The platform connects to internal systems through MCP, so employees bring organizational context into their work without moving data into tools nobody is watching. Access follows permissions that already exist. Deployments run on single-tenant infrastructure with SSO and role-based permissions, serving banking and credit unions, insurance, healthcare, education, state and local government, and life sciences.