No Tool Governed Differently
The same policy packs apply in every tool and to every agent, so no channel is governed differently from the others.
For Compliance Teams
Let Agents Publish. Keep the Proof.
You decide whether AI agents may publish. Widebound makes that decision safe: your rules in every tool, critical failures blocked, and a record of every step.
What You Get
Say Yes to Agents Without Losing Oversight
Content is written in Copilot, Claude, Word, Google Docs and several CMSs. Your rules have to hold in all of them.
Critical Failures Stay Offline
A piece that fails a critical rule does not go live until it is fixed.
A Reason You Can Audit
Each check can be audited rule by rule. Nobody has to trust a summary.
Approval Where You Require It
Switch on a human approval step for a rule, a market or a channel. Everything else keeps moving.
Narrated with an AI-generated voice. A product demonstration with a fictional brand and fictional people.
Transcript
For Compliance Teams
You decide whether AI agents may publish. This is Widebound, shown at Halvora Health, a fictional consumer health brand. Your rules apply in Word, Google Docs, your CMS, AI assistants like Claude, ChatGPT and Copilot, and the AI agents that connect through MCP: factuality, data protection, branding, SEO and GEO, market regulation, accessibility, UX, and your internal policies. Checks are deterministic, and every result is explained rule by rule. A critical failure, like this unapproved claim, keeps the page offline until it's fixed. Each page published through Widebound carries a record: which agent and model wrote it, which rules applied, what each check found, and who approved it. When an audit asks, the record exports as one package. And you decide where a person approves: for a rule, a market or a channel. Here, Regulatory approves French product pages, and gets a version that already follows the rules, so it can be approved the first time. Checks with little compliance risk, like SEO and GEO, can run without sign-off from the start. Let agents publish. Keep the proof. Widebound.
By 2028, an Average Global Fortune 500 Enterprise Will Have Over 150,000 Agents in Use
Only 13% of Organizations Think They Have the Right AI Agent Governance in Place
Why It Holds Up
Why One AI Judge Is Not Enough
An auditor needs the same answer every time and a reason for each rule. A single model asked for a verdict gives neither at enterprise volume.
| What is compared | AI Model as Judge | Widebound |
|---|---|---|
| The same content twice | Answers can change from run to run | Deterministic: the same content gets the same result every time |
| Explaining a result | A paragraph of reasoning | A result you can explain and audit rule by rule |
| Critical failures | Reported, then left to people | Block publishing until they are fixed |
| When rules apply | After the draft exists | Before the agent writes, and again before it publishes |
| What gets checked | The draft | The draft, and again the page as rendered, for example for accessibility |
Proof
The Proof Your Audits Ask For
Every published piece: which agent wrote it, which model it used, which rules applied, what each check found and who approved it.
The record is kept for every piece, in every tool, so the question "how did this get published?" has one answer across the company.
You decide where a person must approve: per rule, per market or per channel. That approval is recorded with the rest.
Drafted the France product page with the 14 rules that apply to it.
Checked against Branding, Market regulation · FR, Factuality and Accessibility. One finding.
Rule MR-FR-02 rewrote one sentence to the approved wording.
Matched the endorsement to approved claim C-208 and its reference.
Checked again as rendered: accessibility and metadata pass.
Published to Drupal · fr-FR. Record sealed.
Narrated with an AI-generated voice. A product demonstration with a fictional brand and fictional people.
Transcript
The Record of One Published Page
This is Widebound, at Halvora Health, a fictional consumer health brand. Here is one of its pages, live in France. When an auditor asks how it got published, the answer is in its record. Which agent wrote it, and with which model: Halvora's content agent, connected through MCP. Which rules applied: the French rules for product pages. And what each check found, including a missing image description, caught on the rendered page and fixed. Who approved it, and when: Élise Moreau in Regulatory, because your policy asks for her approval in France. When an audit asks for it, the record exports as one package. The proof your audits ask for. Widebound.
Questions
Questions From Compliance Teams
Can we prove what was published, and under which rules?
Yes. Every published piece has a record: which agent wrote it, which model it used, which rules applied, what each check found, and who approved it.
We can't let agents publish on their own yet. Is Widebound for us?
Yes. Your team keeps signing off, with less to correct: each piece reaches you already following your rules, so most can be approved the first time instead of going back and forth. Low-risk checks such as SEO and GEO can run without sign-off from the start, and nothing else runs on its own until Widebound shows its results agree with yours.
Are the checks consistent?
Yes. The checks are deterministic and reliable: the same content gets the same result every time, and every result can be explained rule by rule.
What happens when a rule is wrong?
Your reviewers flag it right where they work, and we look into it straight away. You can override or extend a rule at any time, and the change applies at once. Improvements to our rules are released once a month, so an improvement takes about two weeks on average.
Who decides the rules?
You do. We ship standard policy packs and translate your own rules into them during onboarding. Your teams own the rules and decide where a person must approve.
What will our security and procurement teams ask?
Who runs the service and under which policies, and the security documentation, which we share during the evaluation. The trust page lists what they can check for themselves.
What will our marketing team gain?
Content that is checked as it is written, so it goes live when its checks pass instead of waiting for the next review round. For marketing teams shows what changes for them.
Narrated with an AI-generated voice. A product demonstration with a fictional brand and fictional people.
Transcript
Approval Where You Want It
This is Widebound, at Halvora Health, a fictional consumer health brand. You decide where a person approves. Your content goes live when its checks pass. For France, you switch on approval by Regulatory for product pages and name the approver. The label update goes out. The UK page passes and goes live. The French page passes too and goes to Élise Moreau. She approves, and it goes live. Her approval is on the record, next to every check the page passed. Approval where your policy wants it. Widebound.
Your Tools, Your Policies
See the Proof for Yourself
A demo built around your tools, policies and workflows shows how content moves from the rules an agent receives to the record it goes live with.