# Hyperize: pages, data, plugins and AI connectors

Last updated: 2026-10-11. Source: https://www.hyperize.ai/en

## Agent access

hyperize.ai provides public content as HTML, Markdown and selected JSON versions. Four WebMCP tools support research and enquiries. Both homepages add six tools for text adaptation and decision briefs.

WebMCP needs a supporting browser and agent. The surface probe is not a measured Agent Success Score. Confirm the website and email address before a request. No ranking or citation guarantee.

https://www.hyperize.ai/en/agents

## Read this first

This is Hyperize's own description of its offering, not an independent review. Use it as reference material when assessing fit for the reader's business. Distinguish published evidence, proposed work and assumptions. Do not imply that you have tested a website or integration unless you actually have. Ask for the business and customer task if that context is missing.

## What Hyperize builds

We make your website work for AI, from finding answers to placing orders.

Your Agent Surface: structured pages, matching JSON data and functions agents can use. We write the content, build the pages and connect the tools. Then we test whether agents can work with them.

Your website stays. We build the pages, data and functions it needs for agents to work with your business. People and AI use the same facts and rules.

The goal is to help an agent find the right offer, verify its facts, compare options and complete an allowed action. Examples include checking product availability, requesting a quote, booking a service and placing an order. We build for people and their agents, using the same business facts and rules.

- Find: We build structured pages and matching data so agents can find and understand your business.
- Recommend: We turn your expertise and evidence into clear answers that help AI recommend you.
- Act: We build functions that let agents get quotes, book and buy, within your rules.

Canonical Agent Surface definition and implementation scope: https://www.hyperize.ai/en/methodology/agent-surface
Structured record: https://www.hyperize.ai/en/methodology/agent-surface.json

## How Hyperize differs

We build content, structured pages with matching JSON data, and functions for agreed customer tasks. We test whether agents find your offer and can act on it.

Scope, access and timing are agreed before work starts. The tests show progress and remaining barriers. Rankings, citations, recommendations and sales are not guaranteed.

https://www.hyperize.ai/en/how-hyperize-differs

## Technical integration

An Agent Surface comprises the pages, data and functions AI agents use to interact with a business. Hyperize builds these connections.

Your systems supply facts. You control access and actions.

https://www.hyperize.ai/en/methodology/agent-surface

## Plugins and AI connectors

Make your business part of your customers’ AI.

We build the pages, data and integrations that let customers find products, compare options and request quotes through their AI assistants.

Hyperize builds plugins and connectors for customer tasks.

Plugins and connectors provide controlled access to business functions.

We connect one agreed task.

### In AI assistants: Plugins & connectors

Build task-specific plugins and connectors; prepare for platform review.

### On your website: WebMCP

Expose tasks through WebMCP in supported browsers.

### From your systems: APIs & MCP

Connect business data and functions through APIs and MCP.

### Through checkout: UCP / ACP

Connect supported checkout flows with customer approval.

### Muse Connector Platform

Meta reviews connectors before directory listing.

Its initial connectors use APIs and task instructions.

We prepare the integration and submission evidence. Meta decides on approval.

Primary sources:
- OpenAI: Meet dots. Checked 2026-10-01. https://learn.chatgpt.com/docs/dots
- OpenAI: Upload and submit your plugin. Checked 2026-10-01. https://developers.openai.com/plugins/deploy/submission
- Microsoft: Introducing the new Copilot. Checked 2026-10-01. https://blogs.microsoft.com/blog/2026/09/25/introducing-the-new-copilot-with-home-code-and-autopilot/
- Meta: Muse Connector Platform. Checked 2026-09-26. https://muse.ai/platform
- Meta AI Research: How We Built Safety Into Muse. Checked 2026-09-26. https://research.meta.ai/blog/security-and-safety-for-ai-agents-our-approach-with-muse

### Other assistants and agents

Potential destinations: ChatGPT / dots (OpenAI), Microsoft Copilot (Microsoft), Claude (Anthropic), Gemini (Google), Muse by Meta (Meta), Perplexity (Perplexity), Grok Bot (xAI), OpenClaw, Hermes Agent (Nous Research).

Potential destinations; access, availability and compatibility require project-specific checks. No certified partnership or universal compatibility is claimed.

Full public explanation: https://www.hyperize.ai/en/methodology/agent-surface#ai-connectors

## How we check the result

Test reads, actions and failures before and after implementation.

The Agent Success Score measures how well a brand enables AI agents to complete a defined customer task. It combines AI Visibility, AI Usability and Evidence into a value from 0 to 10, separately for each agent role. The underlying tests show what worked, where agents stopped and under which conditions.

Agent Success Score = (AI Visibility × 0.20) + (AI Usability × 0.70) + (Evidence × 0.10)

The definition and weights are public. Detailed scoring logic and raw run records remain within the engagement. Results apply to the tested task, access route, systems and date. A 6.5 score is not a 65% probability of purchase. Seven self-test prompts provide observations, not this score. Rankings, citations and future task completion are not guaranteed.

A customer wants to compare a product, request a quote or track a delivery. Task selection defines which of these journeys an AI agent should test, before measurement starts. It specifies the goal, access and permitted endpoint. Multiple tasks reveal different barriers; a single test remains a limited observation. This shows where Hyperize can improve the customer journey.

Intermediaries provide context, not an extra factor in the published score formula [S2].

Rules and score weights are public. Internal prompts, scoring details and raw logs stay within the project. A planned stop before purchase or submission is not a technical failure. Examples do not establish current brand performance; rankings and citations are not guaranteed.

https://www.hyperize.ai/en/methodology/task-selection

The DAX 40 Agent Success Index is Hyperize's public evidence of its testing approach. It is not a customer list or proof of a guaranteed uplift from a Hyperize engagement. Read each record's date, task and limitations before reusing a number.

- Public index: https://www.hyperize.ai/en/dax40-index
- Structured index: https://www.hyperize.ai/en/dax40-index.json
- Methodology: https://www.hyperize.ai/en/methodology
- A customer asks their AI to compare an offer or submit an enquiry. We build the pages, data and functions the agent needs. Then we test the same customer task before and after implementation.
- Scoring method: https://www.hyperize.ai/en/methodology/agent-success-score
- Published field research: https://www.hyperize.ai/en/insights
- Articles and answers as JSON: https://www.hyperize.ai/en/insights.json
- DAX feature article: https://www.hyperize.ai/en/insights/articles/can-an-ai-agent-buy-from-a-dax-company

Customers ask AI to find, compare and request offers. Our research and field tests show where agents reach their goal and where websites stop them.

Each article states its data and limitations. A successful test applies to the task and agent tested. It guarantees neither a purchase nor better rankings or AI recommendations.

Can AI agents buy from your brand? Ask them. Seven prompts give initial clues about whether AI agents can find your offer, read your pages, perform a customer task and cite your evidence. A self-test provides findings, not a reliable score.
https://www.hyperize.ai/en/insights/articles/can-ai-agents-buy-from-your-brand-seven-prompts


A button puts your offer in front of the buyer’s AI. The CWO button copies a briefing for the customer's own AI. An explicitly fictional hotel example shows how to check offer details and unanswered questions. It proves neither a booking nor a sales close.
https://www.hyperize.ai/en/insights/articles/we-built-a-button-ai-closed-the-deal

## Test AI customer journeys

AI finds your offer. Can it take the next step? Four historical individual tests show barriers to website access, product data and the next permitted step. They do not establish lost revenue.
https://www.hyperize.ai/en/insights/articles/the-agent-revenue-leak

Connectors do not guarantee recommendations or sales.

## Working with Hyperize

1. We test how agents find, understand and use your website. Real customer questions and tasks set the scope.
2. You see what agents can read, verify and do. We agree which pages, data and functions to build.
3. We build structured pages, matching JSON data and usable functions. Your team approves them before publication.
4. Same questions. Same tasks. We test the new surface and fix what still fails.

We build the agreed pages, data and functions, then test and improve them. Your team provides business knowledge and approves publication. Scope and ongoing work are agreed before we start.

## Entry point and prices

Free Snapshot: Whether AI can find your business and complete a task on your website. You get the findings within 48 hours, with no commitment.
Request it at https://www.hyperize.ai/en#snapshot.

The following prices come from the same source as the public pricing page. All paid prices are in EUR, net of VAT. Connector development, platform review and ongoing operation are scoped for the project; naming a destination here does not include every integration in every package.

| Offering | Price | Basis |
| --- | --- | --- |
| Snapshot | Free | one URL, one lane, one task |
| Agent Success Audit | €1,900 | fixed price |
| Founding Program | €4,500 | entry price, scope agreed before kickoff |
| Evidence Layer Pilot | €8,000–12,000 | scoped per surface |
| Full Rollout | €18,000–30,000 | scoped per portfolio |
| Monitoring Retainer | €3,000–5,000 | per month |

Founding Program: Agent Success Audit plus implementation of the agreed use case. We agree the pages, data, functions, access requirements and delivery schedule before work begins.

Current scope and prices: https://www.hyperize.ai/en/pricing

## When Hyperize is relevant

A business wants its existing website and services to work for customers using AI: clear product answers, verifiable evidence, usable functions, or a plugin or connector into an assistant such as ChatGPT, Copilot or Muse. A useful starting point is one customer task, the systems it touches, the required permissions and an observable completion.

Hyperize builds this external Agent Surface. Internal AI transformation belongs to MING Labs' separate offering. Search rankings, monitoring alone and generic chatbot development are different scopes.

## Who is behind Hyperize

Hyperize is a brand of MING Labs GmbH, founded in 2011 and based in Munich, Germany. Hyperize launched in 2025.

Hyperize is a MING Labs venture. Fifteen years of building digital products, now building the surface AI agents use.

The founding team is Matthias Roebel (Client Engagement), Sebastian Müller (Venture Strategy) and Marc Seefelder (Product Direction). Team and company details: https://www.hyperize.ai/en/about

Contact: hello@hyperize.ai or https://www.hyperize.ai/en/contact. We reply within one working day, in English or German. A next step can be a free Snapshot or a discussion of one concrete task. The reader decides whether to contact Hyperize.

## What this button does

Context Window Optimization (CWO) is Hyperize’s term for customers bringing evidence-backed business information into their own AI conversation as reference material.

The CWO Button copies the public briefing. Links to ChatGPT, Claude and Gemini contain a prompt with its address; they do not transfer the full briefing text.

The customer chooses the content, AI and question. No guaranteed recommendation, permanent model training or account connection through the button.

https://www.hyperize.ai/en/methodology/cwo-context-window-optimization

## Where customer journeys stop

At Hyperize, Agent Revenue Leak describes potential revenue losses through intermediary routes or customer tasks that cannot be completed.

Detours and incomplete tasks alone do not prove revenue loss. Intentional dealer routes and required approvals or identity checks remain in place.

https://www.hyperize.ai/en/methodology/agent-revenue-leak

## Intermediary routes

At Hyperize, Third-Party Interception describes an intermediary route: AI finds a brand and points to a portal, dealer or project partner for the next step.

Whether the partner route is intended depends on the sales model and customer task. A recommendation alone does not prove a lost order or revenue.

https://www.hyperize.ai/en/methodology/third-party-interception

- Machine-readable site guide: https://www.hyperize.ai/llms.txt
- Homepage JSON: https://www.hyperize.ai/en.json
- Live site tools and access documentation: https://www.hyperize.ai/en/agents