How Agents Publish Content You Can Defend

What Widebound Is

This is Widebound. It keeps the content you write with AI within your nine policy packs, from factuality to your own internal policies.

An Example: Halvora Health

Meet Halvora Health, a fictional consumer health brand. You lead content for its allergy tablet. Every page, email and listing goes through brand, legal, regulatory and accessibility review, and SEO and GEO checks. And the rules change from market to market. On Monday, a label update lands. Forty pages across six markets have to change. AI drafts them in minutes, but many come back from review with the same comments as last time.

Three Steps: Context, Checks, Proof

Widebound works in three steps. Context: before anyone writes, people and AI get the rules that apply. Checks: the draft is checked against those rules, with the same result every time, explained rule by rule. Critical issues block publishing. Proof: what goes live carries a record of the rules it passed and who approved it. It works where your teams write: in Word, in Google Docs, in your CMS, in AI assistants like Claude, ChatGPT and Copilot, and with AI agents that connect to it. And it brings in results from the SEO, GEO and accessibility tools you already use. Here's Word.

In Word, Then on the Website

You open Widebound, and the US rules are right next to the page. Copilot drafts the new section. Widebound checks it against your approved claims, the Drug Facts label and your brand guide. "Non-drowsy" isn't an approved claim, so it offers the approved wording. One click. The missing "use as directed" line goes in. The page passes. Then you send it to your website. It arrives there exactly as approved, linked to the Word file it came from, with its checks and changes on the record. When audit asks, it's one export. You decide where people still sign off, like medical review for new claims.

Where People Still Sign Off

The rest first proves it agrees with your reviewers, then runs on its own. And reviewer feedback isn't lost in the document. Widebound uses it to improve the context drafts start from and the wording it suggests, and, where a check is missing, to extend the checks. Because it's one set of rules,

One Set of Rules in Every Channel

the same check runs on the spring email Copilot drafts. "Non-drowsy" is caught there too, before the email goes out.

The Short Version

Your rules, where you write. Proof, when you publish. Widebound. Making enterprise ContentOps autonomous.

The Rules Before the First Word

This is Widebound. You lead content at Halvora Health, a fictional consumer health brand. The French product page needs a new section. Your agent takes the brief through MCP. Before it writes, it gets the rules that apply here: your brand guide, the French legal and regulatory rules, this product's approved claims, accessibility, and SEO and GEO. It also sees what other agents produced and approved, like this morning's US page. So it drafts within them, with the claim approved in France. Without that context, it would have carried over the US wording, which is not approved in France. With it, the first draft passes the French checks. The right rules, before the first word. Widebound.

A Critical Failure Stopped and Fixed

This is Widebound, at Halvora Health, a fictional consumer health brand. You decide whether its agents may publish. It checks this US product page against its nine policy packs, from factuality to Halvora's own policies. Every result is explained, rule by rule. This one is critical: "non-drowsy" is not an approved claim. So the page can't be published until it's fixed. Checks are deterministic: run it again, and the result is the same. Widebound offers the approved wording. Apply it, add the missing required statement, and the page passes. The fix is on the page's record. Where your policy asks for a person's approval, that comes next. Every result explained, rule by rule. Widebound.