As AI live search agents reshape how consumers discover products and brands, traditional publishers face a sharp decline in organic web traffic. While some digital publishers look to cloud licensing or pay-per-crawl models to recoup losses, Blankspace is offering an alternative: monetising AI traffic through native, agent-targeted advertising infrastructure.
Blankspace co-founders Alexander Taylor and Harry Luff adtech veterans with a combined 20 years in programmatic advertising, including time at Picnic, are building tools to turn live search agents into a new high-intent revenue channel.
New Digital Age sat down with Taylor to discuss how live search agents process web content, how native ad insertions influence AI search results without compromising editorial integrity, and why the company is raising its pre-seed funding to build a “billion-dollar advertising channel.”
Tell us about the origin of Blankspace and the problem you set out to solve for publishers?
We went through Antler’s incubator back in September last year and secured funding at the beginning of January.
As we assessed the major pain points across the industry, it quickly became apparent that the massive decline in organic traffic, driven by users turning to LLMs for product and brand discovery, is severely impacting the publisher ecosystem.
When looking at the existing solutions, leaders like Tollbit were focusing on content licensing, while Cloudflare announced pay-per-model approaches. We were sceptical about the actual monetary value 99% of the publishing industry could realistically generate just by licensing their content files to AI companies.
Between September and January, we took time to map out a monetisation model that could genuinely scale. We settled on building advertising infrastructure specifically tailored for live search agents.
How does this infrastructure actually work on a publisher’s page?
While AI model training is important, our primary focus is on live search agents. These agents visit pages in real time based on active user intent.
We’ve built infrastructure that allows a publisher to dynamically add a brand placement specifically for the agent to consume.
Put simply: if an article features a “top 10” list, we dynamically insert an 11th placement behind the scenes, invisible to humans, but structured precisely for the search agent to crawl.
Which verticals are you focusing on initially, and why?
We’ve focused heavily on three core sectors: automotive, consumer electronics, and sportswear.
There are two main reasons for this. First, these are high-consideration purchase decisions where consumers require detailed specs and validation before buying, as opposed to pure impulse or aesthetic purchases, so we see very high levels of AI search intent behind them.
Second, coming from a programmatic background, we observed that brands’ affiliate publisher partners were losing organic traffic, causing affiliate revenues to drop. Brands recognised that publisher content held immense value inside LLM ecosystems, but they had no way to track audience volumes or interactions.
Adding an 11th product to a top 10 list raises questions about editorial credibility. How do you address the balance between commercial interests and editorial integrity?
Publishers always balance commercial revenue against editorial independence. Where we’ve seen immediate success is by amplifying existing advertorial or sponsored content across a publisher’s site.
We enable publishers to sell sponsored content at a higher rate because they can demonstrate its value within the LLM ecosystem.
Secondly, we amplify that piece of content across related articles on their site so search agents pick it up. The longer-term vision is allowing brands to buy into per-agent requests dynamically across our publisher network.
How scalable is this approach, and how far can you take it beyond traditional publishers?
The publishing market is huge, and while we’ve started with key verticals, we intend to expand rapidly.
We are constantly identifying sources of truth. the specific authorities LLMs and AI assistants trust. In the future, we plan to work with content creators, podcast hosts, and newsletter publishers as they build authority within the LLM ecosystem.
But for now, our priority is supporting traditional publishers who are feeling the immediate loss of organic search traffic.
The Sole Supplier has seen live search agent traffic grow 282% since we started working together, and seen ChatGPT referral traffic grown 5x.
On the demand side, who is buying this inventory today?
Demand is primarily coming through media agencies, as well as leverage from publishers’ direct sales teams.
It functions similarly to audience extension, but operating entirely within agentic search space.
Agencies are watching the rapid rise of Generative Engine Optimization (GEO) and Answer Engine Optimization (AEO) platforms closely.
Agencies see a major shift occurring and want strategic access to AI visibility. We see source material across the open web as a vital new ad channel for agencies to buy against.
Do you expect pushback from LLM platforms that want to retrieve pure, unmanipulated editorial data?
A massive portion of the open internet is already commercially funded via affiliate models or direct sponsorships. It is exceptionally difficult for an LLM to cleanly filter out every commercial signal.
We’ve seen companies like Perplexity discuss deranking publishers who aggressively block scrapers, but our focus is on adding genuine contextual value.
Live search agents have limited context windows and short attention spans, much like humans. They seek specific, relevant answers to satisfy a prompt. If you attempt to spam an agent with irrelevant promotional text, it simply ignores it.
Interestingly, across the publishers we work with, paid and sponsored articles often perform nearly three times better in LLM citations than standard organic content. That’s because sponsored pieces tend to feature clear verdicts, structured facts, and opinions that LLMs look for to validate their answers.
What success metrics or campaign results are you seeing so far?
Our focus has been proving that you can measurably influence agents at the point of request. Currently, 98% of the ads we serve are successfully picked up by agents, driving an average 3x brand uplift in LLM responses during testing.
We track creative relevance closely.
What does an “ad creative” actually look like to an AI search agent?
To the human eye, it’s very plain, it’s essentially a structured text file, rendered via HTML. Visual aesthetics don’t matter to an LLM; the data structure does.
We model the user’s intent behind why an agent landed on that specific page, mapping out “grounding queries” to understand the prompt that triggered the visit.
From there, we serve a concise snippet of verified brand facts, pricing, and product details from a structured database. It seamlessly matches the tone and context of the surrounding article so the agent can digest it efficiently.
How is Blankspace structured as an organisation, and what are your broader ambitions?
Right now, Harry and I operate in a truly AI-native way, it’s the two of us backed by hundreds of specialised AI agents running automated workflows.
After being backed by Antler, our next round of funding will allow us to scale our human engineering team tenfold and rapidly expand our publisher supply base.
Our goal is to build the next billion-dollar advertising channel for agencies. If you look at how Google built an empire around human search queries, we believe the exact same economic opportunity exists as agents take over web discovery.
