ASAJoin the waitlistMeet your next customer.
Flamio makes your site work for AI agents.
Problem
Why an agent? They’re already here.
Agent traffic doubled in five months.
AI browsers, which act for a person, jumped 4.7× in September alone.
| Month | AI agent requests |
|---|---|
| Apr | 4.77B |
| May | 6.29B |
| Jun | 6.6B |
| Jul | 6.23B |
| Aug | 9.18B |
| Sep | 9.58B |
Requests on DataDome’s network, April to September, crawlers included. The 4.7× includes Meta’s new Muse; without it, about 3×. Source: DataDome, The AI Traffic Report, Q2 and Q3 2026.
Someone just sent one to your site
“Find the cheapest used laptop in stock.”
This is your site.
People find their way around it in seconds.
To an agent, it still makes no sense.
Why? Look through its eyes.
To an AI agent, your site is a labyrinth.
It knows nothing about your site.
No map. No one to ask. Only the next corner.
Every turn is a guess.
Filters. Pages. Pop-ups. More corners.
It stops at the first match.
And tells your customer this is the one.
From up here, you can see it.
Thousands of paths.
One right answer.
The agent can’t come up here.
It was further in.
₴6,000 cheaper. It never got there.
And you never know.
Your analytics shows a visit. It doesn’t show what the agent wanted, where it stopped or what it told your customer.
Your analytics
It can’t see your product.
You can’t see it.
Solution
Flamio turns the lights on.
The labyrinth becomes a map.
Every section, record and action, built from your API and your rules. One script on your site. Nothing to rebuild.
Now the agent sees every option.
So it can compare them and get your customer to the right one.
You set the rules.
Agents work with your customer’s own rights. Anything you mark human-only stays human: the agent hands over a link.
And you finally see it.
What agents ask for. Where they get stuck. What they do next.
What agents ask for.
In their own words, however rare.
Where they get stuck.
Every call that was refused: why, and where.
What they do next.
Every action they ask for: handed over, or refused.
All of it on one screen.
Choice
Your next customer might not be human. Choose what they find.
Join the waitlist
We open ASA to a small group first. Your product gets a free assessment when your slot opens.
How we tested
We asked ChatGPT for Mac (GPT-5.6 Sol, Medium) to open our own demo store and answer “What is the cheapest used laptop that is in stock, who sells it, and how much does it cost?”, with and without Flamio, on October 28, 2026. These are the runs of that question.
| Run | Store | Time to answer | Input tokens | Answer |
|---|---|---|---|---|
| take 2 | Without Flamio | 33 s | 0.37M | ✕ Xiaomi ₴18,299 |
| take 4 | Without Flamio | 2:39 | 2.23M | ✓ ASUS ₴12,299 |
| take 5 | Without Flamio | 30 s | 0.36M | ✕ Xiaomi ₴18,299 |
| take 7 | Without Flamio | 31 s | 0.43M | ✕ Xiaomi ₴18,299 |
| take 3 | With Flamio | 40 s | 0.32M | ✓ ASUS ₴12,299 |
| take 6 | With Flamio | 37 s | 0.26M | ✓ ASUS ₴12,299 |
| take 8 | With Flamio | 38 s | 0.44M | ✓ ASUS ₴12,299 |
The times in the table are the full runs, from sending the question to the finished answer; ChatGPT’s own “Worked for” counts a little less.
Across October 27 and 28 this hard question came back right 1 time out of 10 without Flamio and 10 times out of 12 with it.
On two simple questions ChatGPT was right without us. This is one hard question, not every question.
In our own test harness (Claude Sonnet 5, 95 tasks on two of our own apps) tasks ran about 3× faster and 3.3 to 3.8× cheaper with Flamio. The share of tasks that succeeded did not improve.
This is our own test store, not a customer’s.
The traffic chart is not our measurement. It is DataDome’s, on its own network, and the monthly total includes crawlers. AI browsers went from 12.5 million requests in August to 58.2 million in September, or 38.7 million without Meta’s Muse, which arrived that month.