A neutral engine that measures and optimizes to realized retained profit across Taboola, Outbrain, Meta and the rest — the one thing none of them can build, because each only sees its own traffic and only the front-end conversion.
That is the actual AppLovin analogy. AppLovin never won by buying publishers. It won by owning a proprietary, self-improving signal no competitor had — and renting an engine on top of it. Our equivalent signal is realized downstream profit, measured the same way across every channel.
AppLovin = AXON (the buyer) + MAX (the signal) → Ours = the cross-channel buyer + the realized-profit signal.
Face B makes Face A smarter than anyone's buying. Face A generates the data that makes Face B impossible to copy. That loop is the company.
Programmatically buys native + paid-social + open-web for DTC advertisers and bids to realized retained profit — rebills, refunds, chargebacks netted out — instead of front-end conversions. Revenue and spend volume live here.
Proves true incremental profit per channel via continuous geo-holdouts and real backend truth (Checkout Champ / Shopify / Stripe). This is the moat — and the multiple.
AppLovin = AXON (buyer) + MAX (signal) | Ours = the buyer + the realized-profit signal
The two reasons the honest red-team killed the bootstrapped version — both were constraints of being broke and alone. With money and the network, they invert.
No tool today combines true cross-channel incrementality + real backend retained-LTV + closed-loop automated bidding. Dashboards exist. Experiment tools exist. Nobody closes the loop.
One schema per advertiser: creative → spend → click → session → checkout → rebill 2/3 → refund → chargeback → LTV. Network APIs + analytics + backend via server-side CAPI.
Continuous geo-holdouts yield incremental retained-profit-per-dollar by channel, campaign, audience, creative. A "True ROAS" advertisers trust — because it matches their bank deposits.
Per-opportunity model predicting realized retained LTV — not p(click). Auto-bids and reallocates budget across every network via their existing Target-ROAS APIs. We ride their auctions; we don't build supply.
We productize our own marketing edge: generative + performance-tested creative tied to the same realized-profit loop, so the system learns which angles drive retained customers. Engineers who never bought a click can't fake this.
We can light up 30–50 anchor DTC advertisers fast. Cold-start solved. Benchmark breadth solved. Logos that pull the rest in.
Our ~10 brands give live, full-funnel, realized-profit data to train on before a single external customer signs.
We buy media and live or die on ROAS. The product is built by the people who need it to work — it out-performs tools built by engineers who never bought a click.
We subsidize onboarding to acquire data, out-hire on ML, and move faster than any bootstrapped competitor.
No channel conflict. Everyone shares with the layer that makes their existing spend more profitable.
Networks have data but no neutrality. Tools have neutrality but no closed loop. Agencies have neither data science nor scale. We have all three.
We raise against milestones, not vibes. Every round is priced on demonstrated lift and spend-managed growth — so the multiple narrative stays clean.
Build the spine + measurement on our ~10 brands. Run holdouts. Produce a defensible True ROAS vs. network self-report.
8–12 anchor advertisers from your network. Subsidized, white-glove — our marketers run it for them.
Self-serve + managed tiers, onboarding automation, hardened channel integrations. Land 50–150 advertisers.
Become the default profit layer for DTC. Expand channels, geos, verticals. Selective M&A of point tools — data + talent, not supply.
Money is available — so we deploy it against proof, not ahead of it.
Funded from Acentecom cash. No dilution. Build the proof.
On the Phase 0 proof: platform + bidder, design partners, key ML hires.
On Phase 1/2 traction: scale GTM, channels, geos, subsidize the network land-grab.
At Phase 3: category capture + selective M&A.
Exactly like AppLovin: an advertiser funds a budget, our engine spends it buying impressions across native, paid-social and the open web, and they see the CPM each creative set paid. We sit in the flow of funds — we buy media, our realized-profit engine wins the cheap, high-retention impressions, and the spread between what we pay for media and the budget they fund is our revenue. That's a principal ad-platform model with ~75%+ software-like gross margin — not an agency cut of someone else's spend.
1 · Fund a budget. Advertiser deposits spend into the platform — one balance, all channels.
2 · We buy impressions per creative set. The engine bids to realized retained profit and reports the CPM each creative set actually paid.
3 · We keep the spread. Win impressions below the value we deliver → the gap is high-margin platform revenue that compounds as the model sharpens.
of gross profit (the spread) is roughly what an ad-tech multiple needs to clear $10B — which means single-digit $billions of budget flowing through the platform at AppLovin-class margins, with >120% net spend retention as each advertiser scales. A realistic ceiling for the default DTC profit layer, given the size of native + paid-social DTC budgets.
Stand up the data spine on our own brands — one unified schema across every channel + the Checkout Champ backend.
Run the first continuous geo-holdout on our biggest brand; produce the first True-ROAS vs. network self-report.
Make the #1 hire — the ML / bidding lead.
Pick the 8–12 design-partner advertisers from your network; draft the subsidized offer.
Package the Phase 0 proof into the Series A narrative.