70 valid · 30 fraud
Momentiv x Engrammatic x Fou Analytics
Remove fraud before it becomes auction load.
Fou Analytics is Engrammatic's fraud-data partner. Fou supplies observed fraud labels. Engrammatic turns those labels into a real-time bidstream filter. Momentiv receives a smaller, cleaner opportunity stream while keeping complete publisher-auction control.
Fou-labeled fraud filtering for Momentiv · product concept2:41 · slow narration · English captions
The promise
Turn 100 requests into 73 better decisions.
Assume 30% of incoming traffic is fraudulent. If Engrammatic removes 90% of that fraud while retaining valid traffic, 27 bad requests never consume the full auction. This is not a reporting upgrade. It changes the economics of every downstream decision.
70 valid · 3 fraud
Modeled result: 27% fewer full-auction decisions, 90% less fraud exposure, about 4% fraud remaining in the accepted stream, and nearly 96% clean opportunity share.
Scenario model
Put Momentiv's baseline into the model.
The 90% removal target stays fixed. Move the baseline-fraud slider to see the implied auction-QPS relief, remaining fraud rate, clean share, and budget-density headroom.
requests reach auction per 100
full-auction QPS
fraud in accepted stream
clean share of auctioned traffic
budget-density headroom if spend holds
MODEL: kept = 1 − (baseline fraud × 90%). Assumes valid traffic is retained. Production precision, recall, false positives, buyer demand, and CPM response must be measured.
Clear ownership
Three roles. One clean handoff.
Fou provides observed fraud truth. Engrammatic learns client-specific patterns and acts at line rate. Momentiv decides how clean opportunities are priced, packaged, routed, and allocated.
Fraud-data partner
Observed human, bot, fraud, and unknown labels from delivered media provide the learning evidence.
Real-time filter
Client-specific binary memory compares each request with learned patterns before it reaches the full auction.
Publisher authority
Momentiv retains policy, allocation, floors, demand control, measurement, and activation authority.
PRODUCT BOUNDARYEngrammatic filters against learned fraud patterns. It does not replace Momentiv's auction or claim that an unobserved request was directly certified by Fou.
Momentiv impact
Cleaner traffic compounds across the platform.
At the 30% baseline scenario, Momentiv removes 27% of full-auction work while increasing clean-opportunity concentration by about 37%. The benefit reaches infrastructure, agents, packaging, and publisher economics.
01
Auction infrastructure
About 27% fewer full decisions and downstream logs create capacity headroom without asking the auction to reason over obvious waste.
02
Seller and revenue
Nearly 96% clean share supports stronger packages, more credible forecasts, and up to 37% more budget density if buyer spend holds.
03
Operations
Ninety percent less fraud exposure means fewer incidents, investigations, make-goods, and clawback risks entering the operating loop.
04
Content intelligence
Programming, ad-load, and floor decisions learn from human attention rather than an inventory count inflated by fraud.
Controlled proof
One publisher. One production truth.
The promise is deliberately falsifiable. Replay establishes the baseline; shadow mode tests accuracy and latency; a small live slice proceeds only under Momentiv control.
PILOT SEQUENCE
01 · REPLAYHistorical publisher traffic.
02 · SHADOWScore live; do not act.
03 · ACTIVATEControlled 5–10% slice.
04 · GRADECompare production outcomes.
fraud recallvalid retentionp95 latencyauction QPSyieldclawbacks1M+ EPS target
Product fit is based on Momentiv's public platform description. Fou's role is based on its public measurement methodology. The 90% removal rate and derived business outcomes are modeled pilot targets, not historical Momentiv results or guarantees.