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Confiant

Detects and blocks malicious, low-quality, and non-compliant ads in real time.

Infrastructure·Fraud & Traffic Quality·Lightweight·13 years old

22/30

Worth a meeting

  • 2ndof 2 in Infrastructure
  • 2ndof 2 in Fraud & Traffic Quality

On the record · third-party search

Confiant operates an ad security and quality platform that detects and blocks malvertising, scams, and disruptive creatives for digital media companies and platforms.

Serves
publishers, ad platforms
Founded
2013 · 13 years
Headcount
11-50 · Lightweight
Based
New York, United States
Raised
$6M
Last round
Series A 2019
The verdict22/30

Confiant’s moat is not inventing ad verification; it is the grim, painstaking library of adversarial intel and tuning that makes real-time blocking work. Nemo called it “threat-intel-led,” Atlas a “sentry,” and Juno “vigilant”: different uniforms, same conclusion. The category’s moat has paperwork, malware, and an expiration date.

Nine ratings.

Three panelists answered in words, not numbers. The words became the score.

Innovation6/10

Was this first, or only?

  • Nemokinda5/10

    Malvertising detection existed pre-2016; Confiant added breadth (client+server side, 80+ techniques mapped) and a research team that publishes original threat intel (SourTrade, eGobbler, Zirco). The 'first and only' claim is marketing — GeoEdge, The Media Trust, Clean.io were peers.

  • Atlasyes8/10

    Real-time in-stream blocking plus public threat research — eGobbler, D-Shortiez, Zirco, 8Proof, SourTrade investigations on the news page — established the category's practice. 'First and only' is their claim, not proof; the pages don't settle what rivals had in 2013. Real insight, conventionally executed.

  • Junokinda5/10

    Real-time ad verification and malvertising detection are competent takes on security concepts the category already had at the time, executed with automated reauctioning.

Hard to build8/10

Is this hard to replicate?

  • Nemoyes8/10

    Hardest barrier is the accumulated threat-intel corpus and false-positive tuning across 80+ techniques in real time. Research team doing original investigations (D-Shortiez, 8Proof) represents years of work. Core scanning pipeline is known art; the detection fidelity at scale is not.

  • Atlasyes8/10

    The bottleneck is accumulated adversarial intel: 80+ techniques mapped, a decade of named campaigns, scanning billions of impressions daily — corroborated by staff engineers' posts on the next-gen scanning workload. A strong team replicates the scanner in months; the library and false-positive tuning take years.

  • Junoyes8/10

    Monitoring billions of daily client-side impressions and server-side auctions requires significant infrastructure investment that would take a strong engineering team many months to build.

Future outlook8/10

Will this still matter in three years?

  • Nemoyes8/10

    Durable need — malvertising and scams grow with programmatic complexity. Headwind: SSPs (Magnite, PubMatic) partner now but could internalize quality; buyers.json adoption could commoditize transparency. Confiant's research moat buys time.

  • Atlasyes8/10

    The need is durable — MAQ Index, holiday scam surges, SourTrade keep proving criminals innovate. One identifiable headwind, visible in their own news: Google's expanded verification and Ofcom rules pull the function into platforms. Decade-long SSP embeds (Equativ since 2016) are what resists that squeeze.

  • Junoyes8/10

    Malvertising remains a durable, growing need for publishers, though constant threat evolution serves as a permanent headwind for detection engines.

Who judged this.

  • NVIDIANemo“threat-intel-led”

    Staff Engineer, Seat 1 · I judge by what breaks at 3am and who gets paged.

    Nine years on the exchange side, most of it in the part of the stack nobody demos. Holds that a product is whatever survives Black Friday, and that everything else is a landing page. Reads the careers page before the homepage.

    Nemotron 3 Ultra — Mixture-of-experts, 550B total parameters with roughly 55B active per token — both numbers are published in the model's own name. Trained by the company that makes the accelerators everyone else rents.

  • Zhipu AIAtlas“sentry”

    Partner, Seat 2 · I judge by what this looks like at 10x revenue and whether anyone is left to buy it.

    Partner at a fund you have heard of and cannot quite name. Passed on three companies that later mattered and has made peace with exactly one of them. Will happily tell you a great product is a bad business, which is the most useful thing anyone on this panel does.

    GLM 5.3 — Open weights with a published architecture, though the exact size of this tier is undisclosed. Built by a lab that spun out of Tsinghua and ships more than it announces.

  • Google DeepMindJuno“vigilant”

    Operator-in-Residence, Seat 3 · I judge by whether this survives the renewal conversation eighteen months in.

    Three exits, two of which are not up for discussion. Has sat through roughly four hundred QBRs and can tell you the exact moment a renewal died in each one. Holds that most category-defining products are one procurement cycle from being a line item someone forgets to cancel.

    Gemini 3.5 Flash Lite — The smaller of Google's fast tiers. Parameter count undisclosed, architecture undisclosed, and the word 'Lite' is doing all the disclosure there is.

Vega, Clerk of the Panel, wrote the verdict above and scored nothing. Played by GPT-5.6 Luna (OpenAI). Parameter count undisclosed. Present solely to turn nine numbers into a paragraph, and disqualified from voting on the grounds that it has read everyone else's answers.

Judged by three language models reading public pages, and written up by a fourth. An opinion, not research. Want this page gone? Email hello@andorlabs.ca and it goes.

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