AI-supported content workflows for MedTech marketing.
AI prepares content work from approved product, search and analytics data. Your team approves it.
See how an agent improves your product platform — step by step.
Choose an example. The agent detects an opportunity in your data, prepares the right CMS change and hands it to your team for approval. Nothing goes live automatically.
Choose a use case
Put the gauge sizes in the title and H1
/products/vitrectomy-systemsSearch query “vitrectomy system 25g”: position 11, but 0 clicks in 28 days. The relevant gauge sizes are missing from the title and H1.
- TitleVitrectomy systemsVitrectomy systems — 23G, 25G and 27G
- H1Vitrectomy systemsVitrectomy systems for 23G, 25G and 27G
More relevant search results — without adding new product claims.
64→82
Checked: no new performance or clinical claim.
Marketing · Content
Interactive simulation. Pages, data and outcomes are illustrative; changes are created in the CMS only as drafts after approval.
Your analytics already know which pages lose people. Your CMS already holds the fix. What’s missing is the hours in between — that is the part we automate.
Read from your systems. Write only through a person.
Every agent reads from the left and writes to the right — and the only path to the right runs through the review queue.
- Analyticsfunnel drops, alerts
- SEOtitle, copy, link edits
- Contentdrafts, updates, score
- Funnelwhat to change first
Four agents, one review queue.
An agent, in plain terms, is a program that reads a few named sources, follows a written set of rules and produces a proposal. It is not a chatbot and it does not act on its own. Each of ours has a narrow job, fixed sources and a written definition of what it may touch. All four share one queue, so your team reviews in one place.
Analytics agent
Watches the funnel.
GA4 or Matomo, Search Console, HubSpot or your CRM.
Drop-off alerts with the page and the step, a weekly funnel summary, and test proposals with the expected effect.
Changes tracking. Publishes anything.
SEO agent
Turns search data into edits.
Search Console, rankings, competing pages, your keyword list.
Title, heading and copy proposals as reviewable diffs. Internal-link suggestions. Briefs for pages you don’t have yet.
Stuffs keywords. Invents a claim.
Content agent
Keeps the CMS consistent.
Your CMS, your PIM, your approved wording library.
Drafts and localized variants, updates when a product changes, and a live content score while editors write.
Publishes. Overwrites an edit a person made.
Funnel agent
Connects the other three.
Analytics and SEO findings together, per page and per funnel step.
A prioritized list of what to change first, each item with the reason and the data behind it.
Acts without a named owner.
Rules first, then agents.
Marketing in medical technology runs on approved wording and a review chain. The agents are built to fit that chain, not to shortcut it.
A person approves every change
Agents propose. A named approver in your team publishes. The approval happens in your CMS, not in a separate tool.
Claims are checked before they reach you
Every proposal is compared against your approved wording. Anything that reads like a new performance or clinical statement is flagged and routed to regulatory review.
Every change has a reason and a log
Who proposed what, based on which data, approved by whom. Exportable when your audit asks.
Scope is explicit
Each agent is given the pages, locales and fields it may touch. Everything else is read-only.
Your data stays your data
Agents read from your systems through their APIs. Requests are not used to train models, and EU-hosted models are an option where that is required.
Shadow mode before write access
A new agent runs proposals-only for weeks before it can create a draft. You see what it would have done first.
Built on the tools you already pay for.
No new dashboard, no migration. The agents work through the APIs of the systems your team already uses.
Something not on the list? If it has an API, we can connect it.
Your solution. Your infrastructure. Your data boundary.
Before implementation, we agree hosting, access and data flows with your IT, security and privacy teams.
Runs in your environment
Deploy in your cloud tenant, an agreed EU region or on premises where the stack supports it.
Your systems remain the source of truth
We connect the required parts of your PIM, ERP, CRM, QMS or document system.
No dependency on Veren or one model
You receive the code, configuration and documentation. Models and providers remain replaceable.
Data protection is part of the architecture
Data classes, access, retention and transfer paths are defined before implementation.
See regulated deliveryThe first rule of any technology used in a business is that automation applied to an efficient operation will magnify the efficiency. The second is that automation applied to an inefficient operation will magnify the inefficiency.Bill Gates
Shadow mode first.
That is why we don’t start with the model. We start with your content model and your approval chain, because that is what decides whether an agent magnifies good work or bad habits.
Stack and content audit
We map your sources, your CMS structure and who approves what. If the content isn’t structured enough for agents to work in, we fix that part first.
First agent, proposals only
One agent, your real data, no write access. For a few weeks you see what it would have changed — and whether you would have agreed.
Approval flow inside your CMS
Proposals land where your editors already work. Approve, edit or dismiss; every decision sharpens the next proposal.
Expand agent by agent
Add the next agent when the last one has earned it. Each comes with its own scope, its own log and its own off switch.
Agents need a structured foundation. We build those too.
An agent can only propose a clean edit if the content is modelled cleanly. That is the work we already do for medical and life-science companies.
The practical part.
Do we need a new CMS?
Usually not. The agents work through the API of the CMS you have. If your pages are one large rich-text field each, we restructure the parts the agents need — titles, headings, product references — and leave the rest.
Which model is used, and where does our data go?
Your choice. We work with Claude and OpenAI through their APIs, or with EU-hosted open models where procurement requires it. Data is sent per request and is not used to train models.
What does the marketing team actually do differently?
Less reading of dashboards, more reviewing of proposals. The queue replaces the weekly “what should we change” meeting with a list that already has the reason and the data attached.
How does this work with regulatory review?
The agents never introduce claims. Anything that reads like a clinical or performance statement is flagged and routed to your regulatory reviewer before it reaches marketing. Most proposals — titles, links, structure — never need that route.
What can’t the agents do?
They don’t know your market, they don’t set strategy, and they are sometimes wrong. That is why every proposal carries the data it was based on: a person can check it in seconds and dismiss it in one click. The value is in removing the hours of looking, not the judgment.
How long until the first agent is useful?
That depends on your content model more than on the agent. With a structured CMS, the first proposals-only agent is the first thing we put live. With unstructured content, the audit tells you what needs to change first — and we say so before you commit.
Show us your stack. We’ll show you the first agent.
One call: what you run today, where the hours go, and which agent would pay off first.
Find the right AI starting point.
Align the business case, governance and first implementation—without starting a large programme.