What you actually get from DuckDuckGoose
DuckDuckGoose is a Delft, Netherlands based company founded in 2020 that builds deepfake detection tools for identity verification and fraud workflows. Its product line includes DeepDetector for image and video manipulation detection, Waver for real time audio deepfake and voice clone detection across 16-plus languages, and Phocus, a no-code web workspace for fraud analysts and compliance reviewers to check media manually with an exportable audit trail. The company positions itself for KYC onboarding, document verification, morphing attack detection, insurance claims, and media forensics, with drop-in SDKs and sub-second API responses meant to slot into existing verification flows. It publishes benchmark accuracy figures (roughly 96 to 98 percent across DFDC, FaceForensics++, and Celeb-DF v2) and cites customers including bunq and Banco Daycoval.
DuckDuckGoose pricing
DuckDuckGoose does not publish a public rate card. No published pricing. Site has no working pricing page; access is via booked demo and enterprise contract.
Source: www.duckduckgoose.ai/, read 2026-09-07.
No public rate card. Any number you see quoted for DuckDuckGoose on a comparison site is a third-party report, not a vendor price. Ask for the contract minimum and the termination clause on the first call.
Strengths
- Publishes specific, dated benchmark accuracy numbers (DFDC, FaceForensics++, Celeb-DF v2) rather than vague marketing claims
- Covers all three media types (image, video, audio) with separate purpose-built products
- Explainable output: forensic layer breakdown (face localization, heatmap, noise residual, temporal/embedding/frequency signals) instead of a black-box score
- Named enterprise deployments in identity verification and banking (bunq, Banco Daycoval) with public case studies
Weaknesses
- No published pricing anywhere on the site; the /pricing URL redirects to the homepage
- Enterprise, API-first product aimed at fraud and compliance teams, not a consumer-facing tool for individuals
- Small company (roughly 14 employees per third-party data, seed-stage funding of about 1.4M euros), which is a viability consideration for long-term enterprise contracts
- Requires a booked demo to see the product in action; no self-serve trial visible on the site
Best for
- Identity verification and KYC platforms wanting a deepfake detection layer added to onboarding
- Fraud, compliance, and legal teams needing explainable, court-ready manipulation evidence
- Insurance and fintech companies screening claims or applications for synthetic identity fraud
Not ideal for
- Individuals or small businesses wanting a low-cost or self-serve consumer tool
- Teams needing an immediate published price to budget against without a sales call
- One-off checks of a single image or clip outside of an ongoing verification pipeline
DuckDuckGoose is built for volume. Reputation damage is a tail-risk problem
DuckDuckGoose handles throughput well, and for the operational job of processing a lot of items consistently it is a sound choice. High-volume tooling is genuinely hard to build.
The mismatch is statistical. Volume tools optimise the average: more reviews, more mentions, more coverage, better aggregate numbers. Reputation risk does not live in the average. It lives in the tail, in the single post, the single article, the single thread that behaves differently from the other ten thousand. Optimising the mean is close to useless against a distribution where one observation carries most of the damage.
A summariser does not average a thousand data points. It reaches for the outlier.
This is why businesses with healthy dashboards still get blindsided. Aggregate health and tail exposure are close to independent. A system tuned to raise your average buries the one signal that mattered inside the noise it was built to produce. The tool was not wrong. It was answering a different question.
Where this lands in 2026
AI answers amplify the tail specifically. A model summarising a subject does not average a thousand data points into a fair picture. It picks the most quotable, most linked, most specific claim it can find, and that is the outlier. The one bad thing is exactly what a summariser reaches for.
That is why this review sits on the site of a firm that competes with DuckDuckGoose. The argument is not that DuckDuckGoose is a bad product. Its category was designed against a version of your problem that no longer describes the whole surface. The part it was never built to cover is the part growing fastest.
A detection score is evidence, not a remedy. The work that ends a deepfake is the takedown chain: platform reports, hosting complaints, registrar escalation, and the paper trail that survives a re-upload.
Key facts
- Founded in 2020, headquartered in Delft, Netherlands
- Raised roughly 1.4M USD (about 1.3M EUR) in pre-seed funding as of June 2024, per Crunchbase and Tracxn
- Product suite includes DeepDetector (image/video), Waver (audio), and Phocus (manual review workspace)
- Claims about 96 percent detection accuracy on real-world deepfakes with a 0.01 percent false positive rate
DuckDuckGoose covers one lane. We cover the surface.
You get deepfake & synthetic-media removal as one protocol inside a measured engagement, not as a standalone subscription. Your exposure is measured first against a Bayesian baseline, then the counter-strike work runs, then your surface stays monitored across search, review platforms, and the AI answer layer.
Sources checked
- www.duckduckgoose.ai/
- www.duckduckgoose.ai/pricing
- tracxn.com/d/companies/duckduckgoose/__FlmhWR32zj_gnEvs4mEG_gTUAFwHu_lhoErzU_M
- www.crunchbase.com/organization/duckduckgoose
We read the vendor's own pages for every price on this review and record the date we read them. Where a figure comes from a third party we say so. Method: review methodology.