Pricing that scales with you.

An AI asset is a reusable unit of business intelligence your company builds and controls.

That can include: AI agents · workflows · prompts · decision rules · company context · approval logic · connected data · model orchestration · reusable business methodologies

Unlike a one-time AI answer, an AI asset can be reused, improved, governed, shared, and scaled across the company.

The goal is to create a new class of AI assets that can compound institutional capability and exponentially increase the value created from your company's knowledge and operating intelligence.

Because your company stops recreating intelligence from scratch.

A workflow created once can be reused thousands of times. It can be improved with new knowledge, connected to more systems, shared across teams, and powered by better models as they become available.

Over time: one workflow becomes many. Individual expertise becomes reusable infrastructure. Company knowledge becomes executable. AI experimentation becomes owned operating capability.

PN describes this as Compounding Equity — the work invested in building AI increases the institutional intelligence and IP retained by the company.

AI models are the compute layer.

Your company's decision logic, workflows, methodologies, context, and operating intelligence are the valuable layer built on top.

Foundation models will continuously change and improve. PN is designed so your AI assets remain independent of those changes.

As the PN ownership thesis puts it: Models are commodities. Your decision logic is not.

Use the best available compute. Own the intelligence your company builds on top of it.

You can change the model without rebuilding the business process.

PN workflows and agents are model-agnostic, so your workflow logic, company context, approvals, orchestration, and methodology can stay intact while the underlying model changes.

Models improve. Your AI assets remain and continue compounding.

Those are excellent AI models. PN solves a different problem.

Instead of leaving company intelligence spread across separate AI products and conversations, PN provides an operating layer where that intelligence can become: reusable, governed, connected to company data, shared across teams, model-independent, owned by the organization.

The difference is between using AI and building an owned AI capability.

Yes. Open Know Now, choose the supported model you want, and start chatting.

When building workflows, you can also choose which model should power each workflow or individual step.

Yes. A workflow can use different models for different jobs.

For example: Research → Model A, Analysis → Model B, Final output → Model C.

This lets you use the right intelligence for each task rather than forcing every process through one model.

Yes. Your workflows are not permanently tied to the model you selected when you created them.

As newer or better models become available, you can change the underlying model while keeping the workflow itself.

It means your business users do not need to think about input tokens, output tokens, context windows, or model-by-model API bills every time they use AI.

PN provides a predictable subscription with managed AI usage included.

For exceptionally high-volume automated workloads, capacity is discussed before any commercial change.

You can choose whichever structure best matches how your organization operates.

Scale by Users — Pay based on the number of PN users and create unlimited workflows. Best for teams that continuously build, experiment, chat, and create new AI use cases.

Scale by Workflows — Pay based on the number of workflows and get unlimited users. Best for larger organizations deploying a defined portfolio of AI workflows broadly.

Same PN OS. Two ways to scale.

Annual plans include larger subscription discounts as you scale: Launch — 10%, Team — 15%, Business — 18%, Enterprise — 20%.

Annual plans also include Dedicated Support + Forward Deployed Engineer support, with value ranging from up to $20K/year to $100K+/year, depending on the plan.

FDE stands for Forward Deployed Engineer.

FDE support gives your organization hands-on access to PN expertise to accelerate implementation. This can include: identifying high-value AI use cases, designing workflows, building and refining agents, connecting systems and data, improving workflow performance, supporting deployment and adoption.

The objective is to help you move from "we should use AI here" to a working AI asset much faster.

Yes. PN supports multi-step and multi-agent workflows where different agents, models, data sources, and approval steps can work together toward a business outcome.

Yes. PN supports Human-in-the-Loop (HITL) checkpoints.

You can require a person to review, approve, or validate an output before the workflow continues.

This is particularly useful for high-impact financial, operational, customer-facing, or regulated work.

PN includes a complete Content Generation Suite for turning AI work into finished business deliverables.

You can create: PowerPoint presentations, executive briefs, research reports, strategy reports, marketing content, one-pagers, business documents, Excel-based outputs.

Generate, edit, refine, and export without moving between multiple AI products.

PN supports business-ready outputs including PowerPoint (.pptx), Word (.docx), Excel (.xlsx), and PDF.

You can submit unlimited new connector requests.

The goal is not to force your company to work around a fixed integration catalog. As your technology stack evolves, PN can continue expanding the systems your AI assets can work with.

Yes. Company documents, data, applications, and organizational context can be connected to PN so that chats and workflows operate with information specific to your business.

That context can then become part of the reusable intelligence behind your AI assets.

Yes. Enterprise deployments can include: Private VPC, dedicated infrastructure, on-premises deployment, SSO / RBAC, advanced governance, custom security requirements, enterprise SLAs.

Yes. PN provides centralized administration and governance so organizations can control users, permissions, model access, workflows, data connections, and deployment policies.

Business teams can build within those guardrails without requiring engineering to build every AI use case for them.

The goal is to evaluate PN using real business work, not a scripted demo.

Days 1–10 — Configure — Identify priority use cases, connect relevant information, and configure workflows.

Days 11–25 — Operate — Use PN with real users and real business scenarios.

Days 26–30 — Measure — Review quality, time saved, operational improvements, and potential ROI before deciding how to scale.

PN is built for organizations that want to move beyond individual AI experimentation and make AI part of how the company operates.

It is particularly useful when you want to: build reusable AI workflows, create AI assets, connect AI to company knowledge, give business teams no-code AI capabilities, use multiple leading models, govern AI centrally, deploy AI across teams, retain ownership of the intelligence you build.

PN is not built around one model, one chatbot, or one narrow automation tool.

It gives your company an operating layer to use AI, build with AI, govern AI, and own what you create with AI.

The core idea is simple: Build AI Assets. Exponentially Increase Your Company's Value.

Models will change. Your AI assets should compound. Rent the compute. Own the brain.