We predict how people respond to your content.
Upload an idea, a mockup, a rough cut, or a finished ad. Choose your audience, and see how they'll react, second by second: what grabs attention, what they feel, and what they do next. In minutes.
Grounded in real neuroscience
Backed & supported by
Test an idea or a GenAI draft before you build it.
Compare mockups, animatics, or cuts before you spend.
Understand why a campaign worked, or didn't.
See how a new audience or market will respond.
How it works
From upload to answer in three steps.
Upload your content, choose your audience, and read how they respond.
Upload your content
Drop in your video, or an image or text. It can be a rough idea, a mockup, or a finished ad. Add competitors if you want them in the picture.
Choose your audience
Pick who you're trying to reach. We build them from 100,000 profiles that notice, feel, and react the way real people do.
See how they respond
Watch a simulated audience react, second by second: what grabs attention, what they feel, and what they do at the end. You get the best, expected, and worst case.
First, we read your content
We break your video down, frame by frame.
Before we predict anything, we look at what's actually in your content.
Scenes
We split the video into scenes and read its pacing.
On screen
We detect the logos, products, people, and text in every shot.
Sound
We read the audio: music, voice, and where they land.
Meaning
We read the tone and message of each moment.
This is the fingerprint we use to predict how your audience reacts.
Why you can trust it
Grounded in real brain data, not opinions.
Our model was trained in the lab on real brain and eye-tracking data from hundreds of people. It reads what your content does to attention and emotion, not what people say in a survey.
When the stakes are high
Confirm it with real viewers.
Recruit a panel from your exact audience. They watch with eye-tracking, and you see how real people actually responded, right next to the prediction, including which logos and moments held their eye.
Real attention, second by second
How a real audience's focus rose and fell across your video.
Where their eyes actually went
Which logos, products, and moments held the gaze, and which got skipped.
What they said, in their words
Verbatim reactions from people in your exact target audience.
Use it from Claude, ChatGPT, or any tool your team already lives in.
- ·Connects through MCP, with no new workflow to learn.
- ·Ask in plain language; the simulation runs behind the scenes.
- ·Answers land in the same thread as the rest of your work.
While planning
You
“Simulate a Go Viral scenario on Instagram: US Gen-Z women, $5,000 budget, Creative A.”
North AI
Running 100 scenario paths…
Instagram peaks day 4. Budget lifts reach 3.2× in week 1. Organic sustains if early saves >8%.
Boost on day 1 and 5, not continuously.
Mid-strategy
You
“How does our audience hold attention versus Competitor X on TikTok?”
North AI
Competitor X spikes harder in the first 12h but drops 2× faster than your baseline. Their audience chases trends; yours is more loyal.
You win on week-2 retention.
In a meeting
You
“We have 3 ebooks. Which resonates best with 25–34s on LinkedIn versus Reddit?”
North AI
Ebook 2 wins on sustained attention for that group.
LinkedIn: Ebook 1 gets more shares. Reddit: Ebook 3 matches the tone.
Different content for each platform.
After the campaign
You
“Creative B had lower initial engagement, but the sim predicts more total views. How?”
North AI
Creative A spiked early: paid drove it, and paid ramps fast then decays fast.
Creative B started slower but held attention across more segments, sustaining organic spread without extra budget.
Creative B wins past week 1. Creative A is the launch spike.
What the simulation tells you
Six signals you can act on before you spend.
F01
Paid vs organic separation
Paid reach ramps fast, peaks high, and decays quickly. Organic spreads slower but sustains. The simulation models both independently, so you see where budget ends and content takes over.
F02
How long attention lasts
Every platform fades at its own speed: TikTok content drops in hours, YouTube sustains for weeks. See exactly when attention drops and which format holds longest.
F03
Audience breakdown
Who responds, by age, gender, and behaviour type. Passive scrollers, active sharers, and power users carry different inertia and thresholds, calibrated from demographic data.
F04
Competitor & bot dynamics
Competitor activity competes for the same attention. Fake engagement is modelled separately, so you can tell real signal from inflated early metrics.
F05
Cross-platform spread
Each platform has its own network model. Content moves through algorithmic feeds on TikTok, subscriber networks on YouTube, and group cascades on WhatsApp. The simulation tracks every path.
F06
Range of outcomes
We run every scenario hundreds of times. You get the best, expected, and worst case, not a single number, so you know the downside before you commit budget.
The technology behind your offering
Objective neuroscience
AI models trained on neuroscience data predict how audiences respond: attention, engagement, and emotional response, frame by frame. Validate with real viewers when the stakes are high. No opinions, no bias, just brain data.
Entertainment & media DNA
Our team comes from Sony, NBC Universal, and Warner Bros. We've worked with brands, studios, and agencies at every scale. We speak your language.
Backed by leading institutions
Backed by Innovate UK, the University of Manchester, and the Alan Turing Institute. Supported by AWS, NVIDIA, and BridgeAI. Investors include Praetura Ventures and SFC Capital. Patent filed.
Inside the product