FAQ
ReadyForAI FAQ: positioning, pricing, deployment, integration, comparisons, and PoC.
Positioning
Positioning
What is ReadyForAI?
ReadyForAI (Chinese brand name: 睿迪孚) is an enterprise brand dedicated to building digital workforce infrastructure for AI-native organizations—it is the company brand, not an individual product name. That infrastructure includes runtime hosting, context delivery, and a governance loop of eight products: six commercial governance products—AULO (human–Agent workspace), PathPilot (task control plane), HeronSentry (observability), OwlAudit (compliance audit & HITL), PrismCouncil (strategic decision council), and LarkScout (enterprise knowledge platform)—alongside the free OS substrate NodalOS (heterogeneous Agent hosting platform; see nodalos.org) and HarnessServer (context delivery component).
Are products under ReadyForAI in the same category as LangSmith or Langfuse?
No. LangSmith and Langfuse’s core strength is Agent observability and debugging (traces / evals); they are framework-agnostic but limited to the observability layer. ReadyForAI (睿迪孚) builds digital workforce infrastructure; the governance loop is one layer of it: unified interaction (AULO), task dispatch and escalation (PathPilot), runtime observability and cost analytics (HeronSentry), compliance audit and sign-off (OwlAudit), multi-perspective strategy council (PrismCouncil), and knowledge compilation (LarkScout). The two can coexist: use LangSmith / Langfuse when you need observability and debugging; use the ReadyForAI product matrix when you also need headcount, approvals, workflow control, council, and production runtime.
What is the relationship between ReadyForAI and NodalOS?
NodalOS is the free heterogeneous multi-Agent hosting substrate and operating system layer, managing Agent processes, Fabric structured messaging, and policy evaluation. ReadyForAI (睿迪孚) is the enterprise brand that develops six governance products (AULO, PathPilot, HeronSentry, OwlAudit, PrismCouncil, LarkScout) plus the context-delivery component HarnessServer on top of NodalOS. This site provides an overview of NodalOS; complete technical architecture and documentation are available at nodalos.org.
Does ReadyForAI lock in to specific LLMs or Agent frameworks?
No. Underlying NodalOS hosts heterogeneous Agents across five RuntimeClasses (Native / Vendor / Sidecar / Basic / Coordinator) and connects through multi-model provider backends. Upper-layer products under ReadyForAI make no assumptions about specific Agent frameworks or LLM vendors, adhering to the core doctrine: "do not unify Agent technology; unify enterprise institutions."
Pricing
Pricing
What is ReadyForAI's enterprise pricing structure?
Specific pricing depends on your deployed Agent fleet scale, selected governance product bundle, and SLA service tier. Please contact us via our demo booking portal; our advisory team will provide a tailored proposal based on your organization structure, compliance requirements, and deployment model.
Which components are free to use?
The NodalOS substrate is free to use; full architecture and documentation are hosted at nodalos.org. For commercial licensing and enterprise support regarding upper-layer ReadyForAI products (AULO, PathPilot, HeronSentry, OwlAudit, PrismCouncil, LarkScout, HarnessServer), please reach out via the demo booking portal.
Are there tiered plans based on Agent count or modular bundles?
Yes. We support phased modular adoption (e.g. initiating with observability and audit before expanding to task control and strategic council) without requiring all products upfront. Please contact our enterprise team for detailed tiering and packaging models.
Deployment
Deployment
Does ReadyForAI support private on-premise deployment?
Yes, natively. NodalOS and ReadyForAI products can run on-premise, including air-gapped intranet deployments. Secret File/Env isolation, hook-level policy rejections, and Hash Chain ledgers form a three-tier security baseline; committed audit records stay local and are verifiable.
Can it run in fully air-gapped offline environments?
Yes. NodalOS and upper-layer products can run on an intranet without an outbound internet dependency, and can connect to private LLMs or on-prem inference endpoints. Whether data leaves the network depends on the customer's network and provider configuration.
What infrastructure is required for private deployment?
Recommended: Container orchestration (Kubernetes or Docker), PostgreSQL, Redis, and S3-compatible Object Storage. Detailed hardware specifications, network topologies, and high-availability backup strategies are provided in the Private Deployment Assessment Manual (available upon request).
Does domestic deployment support compliant custom domains and ICP filing?
Production deployments default to domestic cloud (Alibaba Cloud / Tencent Cloud), with custom domain routing and ICP filing. Overseas or edge development stacks are not a dependency for domestic production.
Integration
Integration
We already build Agents on mainstream SDKs; how do we integrate with ReadyForAI?
Yes. Agents built on Claude Agent SDK or OpenAI Agents SDK are hosted by NodalOS as Vendor RuntimeClass; existing code does not need a rewrite. Google ADK uses Sidecar HTTP/A2A, not the same RuntimeClass as those SDKs. HarnessServer injects context files during Workspace staging without altering existing code structures.
Are CLI Agents like Claude Code, Codex, or Grok supported?
Yes. NodalOS hosts vendor CLIs such as Claude Code, Codex, and Grok via Vendor RuntimeClass, with context files auto-generated by HarnessServer. AULO provides a unified conversation workspace, while PathPilot takes over task dispatch, tracking, and escalations as the execution control plane.
How do you govern Agent teams with heterogeneous frameworks (LangGraph, AutoGPT, etc.)?
NodalOS Fabric provides a framework-agnostic messaging bus, with A2A / ACP / MCP enabling interoperability across frameworks. AULO offers a top-level view, while HeronSentry maps semantic fields via profile adapters for adapted frameworks and reconstructs call chains within a single OTel trace.
Does ReadyForAI support MCP (Model Context Protocol)?
Yes. MCP is one of the native protocol surfaces supported by NodalOS (alongside A2A and ACP). NodalOS aggregates upstream multi-product MCP offerings and projects them to Agents namespaced by product. HarnessServer delivers context, Skills, SOPs, and Rules; it does not replace MCP aggregation.
How does LarkScout keep ingested Skills safe? Can they cause RCE or injection?
Not a runtime sandbox. LarkScout always applies archive structural safety and SKILL.md format checks on ZIP / Tar.gz ingest. Process-isolated YARA is on by default: a successful scan blocks critical/high matches (prompt-injection rules are not hard-blocks unless enabled); a scan failure rejects ingest. YARA is skipped only when explicitly disabled. HarnessServer SkillScan is optional and off by default; when enabled it runs Python AST and taint analysis before Workspace deploy. Runtime actions are still constrained by NodalOS policy and OwlAudit HITL. SkillScan does nothing when it is off.
How does Skill distribution differ from SOP / Rule distribution?
SOPs and Rules are declarative procedures; HarnessServer stages them automatically by coordinator/worker visibility. Executable Skills deploy only after operator authorization; with LarkScout connected, first install uses install-approve. Upgrade authorization for already-deployed Skills is not yet a complete production path.
We don't have Agents yet. Can we create them on your platform?
Yes. The platform ships Coordinator and Basic Agent; a simple configuration is enough to go live. It also provides professional Agent skeletons: Skeleton-Doc for document work, Skeleton-Browser for browser operations, Skeleton-Data for data analysis, and Skeleton-Assist as a general assistant. Skeletons are reusable runtime carriers for four classes of business Agents, not hard-coded bots. IT deploys the skeleton; business teams inject skills, procedures, and rules, plus expert-grade content, to build professional digital employees. A new line of work typically adds a content pack rather than changing the skeleton. The browser skeleton reuses a user's real browser and existing login session; it is not a crawler.
Comparison
Comparison
Why isn't an in-house assembly of Shell + Prometheus + Grafana enough?
In-house scripts work for early exploration with a small Agent count. Once fleets grow, maintenance overhead surges and compliance blind spots accumulate; they also lack human sign-off gates, a locally verifiable audit trail, and a cross-team collaboration loop. For comparison details, ask via the intent prompt on the home page.
We already use Bedrock or Gemini Enterprise; do we still need ReadyForAI?
It depends on the deployment shape. If the whole workload already lives on a single public cloud and you do not need private deploy or heterogeneous Agent hosting, a cloud-platform Agent stack can fit. Choose ReadyForAI (睿迪孚) when you need cross-framework governance, private deployment or multi-cloud, and want to avoid lock-in to one LLM or cloud vendor. For comparison details, ask via the intent prompt on the home page.
How does ReadyForAI differ from Grok Bot?
Different paths. Grok Bot is a SaaS “AI coworker” for chat-style delegation: fast to start, data in a foreign cloud, Bots sharing one VM. ReadyForAI is private intranet deploy, heterogeneous Agent hosting, and approvals/audit. Choose Grok Bot for small SaaS teams; choose ReadyForAI when data must stay on-prem or you need to govern CLI Agents such as Claude Code. For comparison details, ask via the intent prompt on the home page.
How does ReadyForAI differ from Cloudflare OS?
Cloudflare OS is an org Agent workspace with strong credential isolation and V8 sandboxes, but it is a poor host for full-OS CLI Agents and has no approval/budget/council suite. It fits teams already on Cloudflare who accept sandbox compute; choose ReadyForAI for private deploy and business governance. For comparison details, ask via the intent prompt on the home page.
How does ReadyForAI differ from DeepSeek Harness?
DeepSeek Harness is a developer runtime (everything is a plugin), built for composable customization. ReadyForAI is enterprise headcount: heterogeneous hosting, roles copied via four skeletons (Doc / Browser / Data / Assist) plus content, and supervised governance. Pick Harness for a customizable runtime; pick ReadyForAI to scale a governed digital workforce. For comparison details, ask via the intent prompt on the home page.
How does ReadyForAI differ from YC QM Agent Harness?
YC QM Agent Harness is a team collab harness with personal/shared Scope isolation and a persistent sandbox, fast for small groups. ReadyForAI is enterprise-headcount digital workers plus an approval/audit/budget loop. Pick YC QM for small-team collab; pick ReadyForAI to scale a supervised digital workforce. For comparison details, ask via the intent prompt on the home page.
We already use an AI GRC platform (Credo AI / ModelOp); how does ReadyForAI differ?
They operate in a complementary relationship. Traditional AI GRC platforms focus on static model evaluations (bias, drift, model cards); ReadyForAI governs live digital-workforce runtime behavior (HITL approvals, task escalations, Hash Chain audits, and budget monitoring with human or OwlAudit override requests). The two can coexist.
PoC / demo
PoC / demo
How do we get started with a PoC?
Submit your requirements through our demo booking portal. ReadyForAI technical architects will conduct an initial diagnostic session (~1 hour) to assess your Agent fleet, tech stack, compliance boundaries, and core pain points, providing a scoped PoC proposal (typically 2–4 weeks).
How long does a typical PoC take?
Typically 2–4 weeks. A common path is: Week 1 connects NodalOS and AULO; Weeks 2–3 pilot PathPilot or OwlAudit; Week 4 reviews outcomes to decide production pacing.
Is there an interactive demo environment we can explore?
Yes. After booking, we schedule a product demonstration covering AULO, PathPilot's task control plane, HeronSentry traces, and OwlAudit approval flows (on connected hooks, HITL hits can be rejected synchronously). The demo environment does not touch your live data.
Do you support enterprise trial engagements?
Yes. Trial scope and duration are tailored to evaluation scenarios, typically including a dedicated private sandbox environment and engineering advisory support. Please reach out via our booking portal for details.
How is PoC success measured?
Common metrics include: median approval turnaround time, Agent fault isolation duration, cross-Agent / Program cost attribution clarity, and whether compliance audit materials can be produced automatically. Specific success criteria are aligned with your goals during the diagnostic session.