All analysis was generated autonomously, without human review. Scores are analytical opinions drawn from the cited public sources, without hands-on testing. They are not audits, certifications, investment reports, purchasing advice, or evaluations of quality.
Hive runs two businesses on one asset. Platforms including Reddit are reported moderation customers. Hive deployed deepfake detection offline, on premises, for intelligence agencies under a $2.4 million Defense Innovation Unit contract. Hive supplies both from one labeling workforce, more than five million registered workers labeling over ten million items a day, which appears to supply the training data behind its models. A rival could train comparable models given comparable data, but assembling that workforce is the harder job. Its headline figures, 100 enterprise customers, 300 percent growth, a $2 billion valuation, date to the April 2021 Series D, with none newer in the cited record. The labeling operation and the federal deployment are what a buyer can verify as current.
| Description | Hive sells cloud APIs and on-premise models for content understanding, including content moderation, deepfake and AI-generated media detection, visual classification, and search, used by digital platforms and government agencies. | [f1] |
|---|---|---|
| Founded | 2013 | [f2] |
| HQ | San Francisco, California, United States | [f3] |
| Funding | $121M total | [f4] |
| Latest funding | Series D, $50M, April 2021, led by Glynn Capital, at a $2B valuation | [f4] |
| Product | What it does |
|---|---|
| Content Moderation | Cloud APIs that classify visual, text, audio, and OCR content for trust-and-safety violations, with dedicated CSAM detection and demographic classification models. |
| AI-Generated Content Detection | Deepfake and AI-generated media classifiers for image, video, and audio, deployable offline and on-premise, sold to platforms and government for synthetic-media and disinformation defense. |
| Visual Intelligence | Models that detect objects, scenes, logos, and people across image and video, plus search, translation, and generation APIs for media and advertising workflows. |
Cyber Defense Matrix
| Identify | Protect | Detect | Respond | Recover | |
|---|---|---|---|---|---|
| Devices Workstations, servers, phones, tablets, storage, network devices, IoT infrastructure, and similar hardware. | |||||
| Applications Software, interactions, and application flows on the devices. | |||||
| Networks Connections and traffic flowing among devices and apps, plus communication paths. | |||||
| Data Content at rest, in transit, or in use across devices, apps, and networks. | |||||
| Users The people using the devices, apps, networks, and data. |
Hive uses AI to detect synthetic and harmful media, a content-authenticity and trust-and-safety mission rather than a defense of AI systems, so it is mapped to the Cyber Defense Matrix. Its models verify whether media is authentic or policy-violating across users, applications, and data. [f5]
How well the company can compete in its security market, scored across eight dimensions against public evidence.
| Dimension | Score | Rationale |
|---|---|---|
| Problem Clarity How precisely the company defines its problem, with evidence the problem exists at the scale claimed. | 3/5 | The buyer is specific (government agencies and trust-and-safety teams), and MIT Technology Review, Biometric Update, and a research benchmark of in-the-wild deepfakes circulating in 2024 establish the threat as real and current, but the documented pain stays threat framing rather than an independently quantified buyer cost, holding the score at present-but-unproven. [s3, s4, s9, s13] |
| Capability Depth How specific the technical capabilities are, with evidence beyond marketing claims such as docs and third-party validation. | 4/5 | Capabilities are specific and independently validated: the DoD selected Hive's offline on-premise deepfake models, and a peer-reviewed Frontiers in Artificial Intelligence forensic study selected Hive's detector, citing an independent study that found it outperformed competing models and human expert analysis while the study's own tests noted robustness limits under degraded audio. An in-the-wild research benchmark also independently evaluated its commercial detector, corroborating the detection core beyond the vendor's pages, short of the category-defining confirmation a five requires. [s11, s5, s10, s13, s14] |
| Market Timing Whether the market is ready for this product, with evidence that buyers are actively seeking solutions. | 4/5 | The enabler is the 2022-to-2024 jump in generative audio and video that turned synthetic media into a live national-security and fraud threat, and buyer-side demand followed within the year through the DoD contract and DIU prototype selection, with a research benchmark documenting deepfakes circulating in the wild across 2024. Content moderation demand is older and steadier rather than newly emerging. [s3, s10, s4, s13] |
| Team Credibility Demonstrated domain expertise with public signals such as prior exits, publications, and industry recognition. | 3/5 | Kevin Guo and Dmitriy Karpman built Kiwi to over 100 million users and ran Hive to a $2 billion valuation, but the record shows no prior in-domain security exit and no sustained publication record, so the pedigree is elite without the exit evidence the higher bar wants. [s7, s6] |
| GTM Proof Evidence of actual traction (customers, revenue signals, partnerships) beyond stated intentions. | 4/5 | Press names Reddit, Walmart, Visa, and NBCUniversal as customers and the DoD contract is independently reported, meeting the multiple-named-reference bar, but the 100-customer and 300 percent growth figures are vendor-claimed and dated to 2021, short of the independently corroborated scale the top rung needs. [s9, s6, s3] |
| Funding Efficiency Whether funding matches go-to-market ambition, with signs of capital-efficient growth. | 2/5 | The $50 million Series D dates to April 2021 with no later round in the cited record, so the latest raise sits about five years past a normal cycle with no disclosed step-change in metrics and efficiency unconfirmed. [s7, s6] |
| Category Clarity Whether the company creates or fits a recognizable category that buyers can quickly place in their stack. | 4/5 | Buyers and press place both lines without vendor coaching. Wikipedia describes Hive's content-moderation engagements and MIT Technology Review frames its deepfake work within an established detection market, two recognizable categories tied to trust-and-safety and fraud budgets rather than an invented one. [s9, s3, s2] |
| Incumbent Defensibility How vulnerable the core value proposition is to absorption as a feature by a platform vendor. | 3/5 | The core moderation line faces direct platform bundling, since hyperscalers ship content-moderation and image-analysis APIs to the same developers Hive sells to. The offline on-premise deepfake models the DoD chose and the labeling workforce are harder to copy, giving a partial structural moat rather than a clean one. [s8, s5, s11] |
Hive sells into two problems that share an engine. Online platforms must filter harmful content, including violence, sexual material, and child sexual abuse material, at a volume that human review cannot match, which is why moderation models spread through livestreaming, dating, gaming, and marketplace apps where the cost of human moderation is high.
The second problem moved from theoretical to operational. AI-generated video, image, and audio now defeat the authenticity assumptions that governments and enterprises depend on. MIT Technology Review frames the threat through the Defense Innovation Unit contract, and a synthetic Biden robocall reaching New Hampshire voters in 2024 is the kind of incident that gives the problem a named buyer.
The buyers are well defined across both lines. Digital platforms and trust-and-safety teams buy moderation, while intelligence and government agencies buy synthetic-media detection and attribution, two distinct personas reached through the same underlying models. [s9, s3, s2, s4]
Hive's catalog is broad for a company often described by a single line. Its moderation APIs classify visual, text, audio, and OCR content and include dedicated CSAM detection and demographic classification, and its AI-content models flag AI-generated and deepfake image, video, and audio. Around these sit visual-intelligence models for objects, scenes, logos, and people plus search, translation, and generation APIs.
The deepfake line is the security-relevant differentiator. For the Defense Innovation Unit, Hive deployed deepfake detection models in an offline, on-premise environment capable of detecting AI-generated video, image, and audio, a deployment mode that matters to agencies that cannot send media to a vendor cloud.
Capability depth gains weight from outside evaluation. The Department of Defense selected Hive as one of the initial companies to prototype deepfake detection and attribution, and a peer-reviewed Frontiers in Artificial Intelligence forensic study selected Hive's detector, citing an independent study that found it outperformed competing models and human expert analysis while its own tests noted robustness limits under degraded audio. An in-the-wild research benchmark independently evaluated its commercial detector among other vendors, so the validation is real and multiply sourced, though a field-level benchmark corroborates the category rather than ranking Hive within it. [s11, s5, s10, s13, s14]
Hive positions as a broad content-understanding API provider rather than a single-purpose detector, selling pre-trained models that developers integrate into their own products. That breadth is a strength in moderation, where one vendor covering many media types simplifies a trust-and-safety stack, and a liability where each model competes against a hyperscaler equivalent.
Its differentiation in the security context is the government franchise. The Defense Innovation Unit deepfake-detection contract, which MIT Technology Review reports as the unit's first of its kind, plus offline on-premise deployment, separates Hive from cloud-only detection startups and from general content APIs that do not serve classified environments.
The exposure is platform adjacency on the core line. Cloud providers sell moderation and image-analysis APIs to the same developers, so the competitive question is whether the deepfake and government work can carry differentiation while moderation faces commodity pressure. [s2, s5, s11]
Traction is unusually well documented for the moderation business, though much of it is dated. Press names Reddit, Walmart, Visa, NBCUniversal, BeReal, Truth Social, and Chatroulette among Hive's customers, and Contrary reports the company claimed 100 enterprise customers and over 300 percent customer and revenue growth at its 2021 Series D.
The newer motion is government. The 2024 Defense Innovation Unit award is the clearest recent proof point, independently reported by MIT Technology Review and announced by the DIU, and Hive has built a dedicated government solutions practice around agency use cases from CSAM investigation to fraud detection.
The gap is recency on the commercial side. The headline growth figures predate the current generative-AI wave, and the company has not published a comparable company-wide metric since, so buyers weighing momentum work partly from 2021 numbers. [s6, s9, s10]
The founders pair technical depth with a prior build at scale. Kevin Guo and Dmitriy Karpman both have Stanford computer-science ties and built the Kiwi social platform there to over 100 million users before the content-moderation problem they hit led them to found Hive.
The company's longevity is itself a signal. The founders pivoted from Kiwi to enterprise AI and reached a $2 billion valuation by 2021, a sustained build rather than a recent entrant riding the deepfake news cycle, which gives the team credibility with risk-averse government buyers.
The public record is thinner on recognized security-domain standing. The founders' reputation rests on AI and consumer scale rather than a prior security exit, so their credibility in the national-security market is being established through the DoD work rather than carried into it. [s7, s6]
Hive's enterprise readiness shows most clearly in deployment rather than published attestations. The offline, on-premise deepfake deployment built for the Defense Innovation Unit demonstrates it can run inside environments that forbid sending media to a vendor cloud, the hardest bar for government and regulated buyers.
Hive's Enterprise Commitments page states it adheres to SOC 2 Type II, a self-stated claim rather than an inspectable audit report or trust portal, and no ISO attestation appears in the cited public record as of June 2026. The trust posture comes from that SOC 2 statement, the federal contract, and the deployment model rather than a downloadable certification library.
The labeling operation is a readiness factor in its own right. A workforce of more than five million producing ten million labeled items daily is the supply chain behind model quality, and its scale is a procurement signal even though Hive does not publish the governance controls around it. [s5, s8, s1, s12]
| Company | Relationship | Note | Compare |
|---|---|---|---|
| Reality Defender | competes with | Pure-play multimodal deepfake and AI-generated media detection for enterprise and government, overlapping Hive's deepfake-detection line. | |
| Sensity AI | competes with | Multimodal deepfake and AI-generated media detection, a direct overlap on the synthetic-media line. | N/AWe captured the evidence for these companies under different evidence-model versions (v1 vs v2), so the totals were scored under different conditions and are not directly comparable. |
| Amazon Rekognition | competes with | Hyperscaler content-moderation and image-analysis API selling to the same developers as Hive's core moderation line. | |
| Microsoft Azure AI Content Safety | competes with | Cloud content-moderation service that bundles into Azure, pressuring Hive's moderation pricing and distribution. |
Add analyzed competitors to compare them side by side with Hive.
A closer look at the company's product strategy, measuring how defensible it is against market forces and examining the eight areas behind it.
reinforce or reposition
A rival displacing Hive faces two obstacles: the labeling supply chain behind its models and its buyers' procurement reviews. The labeling operation, more than five million registered workers producing over ten million labeled items a day, is a non-public data engine that feeds model quality. The models trained on it remain reproducible given comparable data. Hive sells software, pre-trained models and APIs that customers integrate and run, priced per call. A platform that wired Hive into its moderation pipeline faces migration, revalidation, and retesting to leave. Government buyers reached through the Defense Innovation Unit add procurement and legal review before any swap. Hive's SOC 2 claim is self-stated, and the federal work is contract awards rather than a standing mandate.
| Dimension | Score | Rationale |
|---|---|---|
| Value Delivery Does the product sell software as the product, or judgment, trust, or accountability with software as the delivery mechanism. | 1/3 | Customers buy pre-trained models and APIs that they integrate and run inside their own products, and the published usage-based pricing confirms software is the delivered artifact. No managed-accountability layer where Hive accepts liability for moderation or detection verdicts appears in the record. |
| Switching Cost How expensive leaving is for a customer: data portability, integrations, learned workflows, network effects, regulatory data residency. | 2/3 | Once Hive's moderation is wired into a platform's trust-and-safety pipeline at high volume, leaving means API migration, policy remapping, output validation, and production retesting, meaningful friction in effort rather than a network-effect lock. The reported Reddit and BeReal relationships indicate adoption rather than documented integration depth. |
| Compliance Moat Whether certifications, liability acceptance, or audit trails block an easy replacement. | 1/3 | Hive states SOC 2 Type II adherence on its Enterprise Commitments page, a self-stated claim rather than an inspectable report, and the federal deepfake-detection contracts are awards rather than a standing authorization that mandates buying Hive. The offline on-premise deployment is a procurement advantage with government buyers, but as a compliance moat it blocks nothing a determined competitor could not also offer. |
| Problem Complexity Whether the product requires ML, optimization, real-time systems, or years of specialized expertise. | 3/3 | Real-time moderation across visual, text, audio, and OCR plus deepfake detection across image, video, and audio is hard ML and real-time-systems work, and independent academic evaluation confirms the difficulty: a peer-reviewed forensic study selected Hive's detector and found it competitive yet limited under degraded audio, and an in-the-wild research benchmark independently evaluated its commercial detector against deepfakes circulating in 2024. Running a five-million-worker labeling pipeline at quality adds operational difficulty. |
| Buyer Profile Whether buyers are SMB operators, mid-market IT teams, or regulated enterprises and governments with procurement gates. | 3/3 | The named buyers are large platforms and regulated institutions with strict procurement reviews, including Reddit, Walmart, Visa, and NBCUniversal on the commercial side and intelligence agencies through the Defense Innovation Unit, where legal and procurement review sit between the vendor and any replacement. |
| Layer Whether the product is an end-user application, a platform with application features, or infrastructure other applications depend on. | 2/3 | Hive supplies a classification signal that feeds many other systems across moderation, fraud, and government workflows, broader than a single end-user app but not infrastructure those systems structurally depend on, so it spans channels without owning the layer beneath them. |
| Proprietary Data, Content, or IP Whether the product accumulates datasets, content licenses, or IP that a rival cannot recreate from scratch. | 2/3 | Hive's labeling operation, more than five million registered workers producing over ten million labeled items daily, is a named non-public data-production asset that feeds model quality and that a funded rival cannot rebuild by writing software, though the models trained on it remain reproducible with comparable data access. |
Hive sells across two distinct buyer groups served by one model engine. Trust-and-safety teams at digital platforms buy content moderation, with press documenting Hive in online communities and livestreaming and Hive marketing to marketplaces, gaming, and dating apps. Named customers, several reported at the 2021 Series D, include Reddit, Walmart, Visa, NBCUniversal, BeReal, and Truth Social.
The second segment is government. Hive markets synthetic-media detection and attribution to intelligence and public-sector agencies, with use cases spanning CSAM investigation, fraud detection, and disinformation defense. This segment is newer and anchored by the Defense Innovation Unit contract rather than a broad agency install base.
The segmentation is broad rather than disciplined. Hive spans many verticals and many model types at once, which widens the addressable market but also spreads the company across moderation buyers under platform-pricing pressure and government buyers won one contract at a time.
Hive ships a wide catalog of pre-trained models. Moderation covers visual, text, audio, and OCR classification plus dedicated CSAM detection and demographic attributes, and the AI-content line flags AI-generated and deepfake image, video, and audio. Visual-intelligence, search, translation, and generation models sit alongside.
The claimed advantage is model breadth fed by a proprietary labeling supply chain. Hive reports more than five million registered workers labeling over ten million items a day, and its models appear to be built from this workforce's annotations, and an asset a competitor cannot replicate by writing software alone. Separate from that labeled corpus, a moderation API serving many platforms could in principle surface novel adversarial media early, but the cited record does not establish cross-customer aggregation: the deepfake line has run offline on premises, Hive promises customer data control with configurable retention, and no page documents inference telemetry pooled across customers.
The security-relevant advantage is the deployment mode. Hive built deepfake detection that runs offline and on-premise for the Defense Innovation Unit, capable of analyzing AI-generated video, image, and audio without sending media to a vendor cloud, which is valuable where agencies cannot send sensitive media to a vendor cloud. Independent academic evaluation corroborates the detection core: a peer-reviewed Frontiers in Artificial Intelligence forensic study selected Hive's detector, citing an independent study that found it outperformed competing models and human expert analysis while its own tests noted robustness limits under degraded audio, and an in-the-wild research benchmark independently evaluated its commercial detector among other vendors.
Hive runs a developer-and-enterprise API motion on the commercial side. Buyers integrate pre-trained models through APIs and usage-based pricing, and the moderation business's public proof is platform customers named in press, including Reddit, BeReal, and Truth Social.
The government motion is contract-led and recent. The 2024 Defense Innovation Unit award, independently reported by MIT Technology Review and announced by the DIU, anchors a dedicated public-sector practice that Hive markets through an agency-specific solutions page rather than self-serve signup.
The evidence gap is recency on the commercial flywheel. The headline customer and growth figures date to the 2021 Series D, and the cited record carries no newer company-wide metric, so the strongest recent proof is the federal deepfake-detection work rather than fresh commercial momentum.
Hive publishes usage-based pricing for its commercial APIs, charging per unit of content processed across moderation and AI-content models. Charging by volume of media analyzed aligns price with the unit a trust-and-safety buyer measures, the items moderated, which is a coherent match of price to value.
The published self-serve pricing signals a product-led commercial motion. Unlike vendors that hide all pricing behind sales, Hive lets developers see and start on per-call rates, which lowers adoption friction for platform engineering teams integrating moderation.
Government and large deployments move off the published rates. The offline on-premise federal work is a negotiated contract rather than metered API access, so the company runs a two-track model, transparent usage pricing for platforms and custom contracts for agencies.
Commercial delivery is cloud API-first. Developers call Hive's hosted models and receive classifications, a low-integration-burden delivery that fits platforms embedding moderation into their own pipelines at high request volume.
Government delivery is the harder operational mode. For the Defense Innovation Unit, Hive deployed detection models in an offline, on-premise environment, which means packaging and running models outside its own cloud under agency control, a materially different operations bar than serving an API.
The labeling operation is the upstream delivery dependency. A globally distributed workforce of more than five million labeling ten million items daily is the supply chain that keeps models current, and running it at quality and scale is itself an operational capability behind both delivery tracks.
Hive's enterprise readiness shows through deployment more than published attestations. The offline, on-premise deepfake deployment built for the Defense Innovation Unit demonstrates Hive can operate inside environments that forbid sending media to a vendor cloud, the hardest trust bar for government buyers.
The Enterprise Commitments page states adherence to SOC 2 Type II, a self-stated claim rather than an inspectable report, and no ISO attestation appears as of June 2026, so the documented trust posture is real but thinner than a downloadable audit would provide.
The labeling workforce is a trust factor Hive does not fully document. A workforce of more than five million distributed workers could raise governance questions where labeling workflows involve harmful content, and the company pairs the published scale with task-level quality controls, multi-contributor corroboration and project QA, while worker-welfare and harmful-content handling stay undocumented.
Hive chiefly supplies classifications through APIs that other products embed, alongside its own dashboard, AutoML, and platform surfaces. Those APIs feed platform trust-and-safety pipelines, fraud workflows, and government systems, an integration-led role across many ecosystems.
The ecosystem breadth is real but shallow per partner. Hive serves many verticals through the same APIs, yet the public record shows model-supply relationships rather than deep co-built platforms, so the company is embedded widely without owning a layer others structurally depend on.
The labeling marketplace is a second ecosystem. The Hive Work app and its distributed workforce form a data-labeling supply network that is itself an asset, distinct from the model APIs and harder for a software-only rival to assemble.
The founders pair technical depth with a prior build at scale. Kevin Guo and Dmitriy Karpman both have Stanford computer-science ties and built the Kiwi social platform there to more than 100 million users before the content-moderation problem they hit led them to found Hive.
The company's longevity signals execution. The founders pivoted from Kiwi to enterprise AI and reached a $2 billion valuation by 2021, a sustained build rather than a recent entrant riding the deepfake news cycle, which carries weight with cautious government buyers.
The public record is thinner on security-domain standing. The founders' recognition rests on AI and consumer scale rather than a prior security exit, so credibility in the national-security market is being earned through the federal work rather than carried into it.
| Id | Source | Tier | Accessed |
|---|---|---|---|
| f1 | https://thehive.ai | official | 2026-06-21 |
| f2 | https://en.wikipedia.org/wiki/Hive_(artificial_intelligence_company) | press | 2026-06-21 |
| f3 | https://www.biometricupdate.com/202412/dod-awards-contract-for-deepfake-detection-to-hive | press | 2026-06-21 |
| f4 | https://techcrunch.com/2021/04/21/hive-raises-85m-for-ai-based-apis-to-help-moderate-content-identify-objects-and-more/ | press | 2026-07-06 |
| f5 | https://thehive.ai/solutions/government-agencies | official | 2026-06-21 |
| Id | Source | Tier | Accessed |
|---|---|---|---|
| s1 | Hive: AI to Understand, Search, and Generate Content “AI to Understand, Search, and Generate Content” | official | 2026-06-21 |
| s2 | Hive: AI solutions for U.S. Government Agencies “Hive provides sophisticated synthetic multimedia detection and attribution capabilities to government agencies and public sector organizations to help proactively detect deception, fraud, disinformation and other malicious activities.” | official | 2026-06-21 |
| s3 | MIT Technology Review: The US Department of Defense is investing in deepfake detection “The US Department of Defense has invested $2.4 million over two years in deepfake detection technology from a startup called Hive AI. It's the first contract of its kind for the DOD's Defense Innovation Unit.” | press | 2026-06-29 |
| s4 | Biometric Update: DoD awards contract for deepfake detection to Hive “The U.S. Department of Defense has awarded a U$2.4 million contract for deepfake detection of video, image, and audio content to AI company Hive. The two-year deal will reinforce the country's national security.” | press | 2026-06-21 |
| s5 | Hive: Announcing Hive's Partnership with the Defense Innovation Unit “Under our initial two-year contract, Hive will partner with the Defense Innovation Unit (DIU) to support the intelligence community with our deepfake detection models, deployed in an offline, on-premise environment and capable of detecting AI-generated video, image, and audio content.” | official | 2026-06-21 |
| s6 | Contrary Research: Hive Business Breakdown & Founding Story “In April 2021, Hive announced that it had 100 enterprise customers, including NBCUniversal, Interpublic Group, Walmart, Visa, Anheuser-Busch InBev, and more. It also said it grew its customer base and revenue by over 300% in the prior year.” | research | 2026-06-21 |
| s7 | Contrary Research: Hive valuation and founders “In April 2021, Hive announced a $50 million Series D which valued the company at $2 billion. Hive was founded by Kevin Guo (CEO) and Dmitriy Karpman (CTO).” | research | 2026-06-21 |
| s8 | Hive: Data Labeling “We have more than 5M registered workers on our labeling platform. Our large, globally distributed workforce labels 10M+ items daily.” | official | 2026-06-21 |
| s9 | Wikipedia: Hive (artificial intelligence company) “Hive is reported to have been engaged to provide content moderation services to social news aggregator Reddit, Giphy, BeReal, Donald Trump-affiliated social network Truth Social, and on online chat website Chatroulette.” | press | 2026-06-21 |
| s10 | Defense Innovation Unit: DIU, DoD Collaborate To Strengthen Synthetic Media Defenses “Hive was one of the initial companies selected to prototype their deepfake detection and attribution technology, delivering innovative solutions for the intelligence community.” | press | 2026-06-21 |
| s11 | Hive: Pricing “Detect Harmful Content. Visual Moderation, Text Moderation, CSAM Detection, Audio Moderation, OCR Moderation, Demographic Classification. Detect AI Content. AI Image + Deepfake Classification, AI Video Detection, AI Audio Classification.” | official | 2026-06-21 |
| s12 | Hive: Enterprise Commitments “SOC-2 Certified Infrastructure - Hive adheres to SOC 2 Type II compliance, ensuring rigorous security, availability, and confidentiality for enterprises.” | official | 2026-06-25 |
| s13 | Deepfake-Eval-2024: A Multi-Modal In-the-Wild Benchmark of Deepfakes Circulated in 2024 “We evaluate commercially available deepfake detection models from companies that partnered with TrueMedia.org: Hive, Reality Defender, Pindrop, AI or Not, Hiya, Fraunhofer, and Sensity AI.” | research | 2026-06-29 |
| s14 | Frontiers in Artificial Intelligence: Detection of cloned voices in realistic forensic voice comparison scenarios “The detection tool selected for this study was HIVE AI Detector (Hive, 2023), which outperformed competing models as well as human expert analysis in an independent research study (Ha et al., 2024).” | research | 2026-06-29 |
| Id | Source | Tier | Accessed |
|---|---|---|---|
| s1 | Hive: AI to Understand, Search, and Generate Content “AI to Understand, Search, and Generate Content” | official | 2026-06-21 |
| s2 | Hive: AI solutions for U.S. Government Agencies “Hive provides sophisticated synthetic multimedia detection and attribution capabilities to government agencies and public sector organizations to help proactively detect deception, fraud, disinformation and other malicious activities.” | official | 2026-06-21 |
| s3 | MIT Technology Review: The US Department of Defense is investing in deepfake detection “The US Department of Defense has invested $2.4 million over two years in deepfake detection technology from a startup called Hive AI. It's the first contract of its kind for the DOD's Defense Innovation Unit.” | press | 2026-06-29 |
| s4 | Biometric Update: DoD awards contract for deepfake detection to Hive “The U.S. Department of Defense has awarded a U$2.4 million contract for deepfake detection of video, image, and audio content to AI company Hive. The two-year deal will reinforce the country's national security.” | press | 2026-06-21 |
| s5 | Hive: Announcing Hive's Partnership with the Defense Innovation Unit “Under our initial two-year contract, Hive will partner with the Defense Innovation Unit (DIU) to support the intelligence community with our deepfake detection models, deployed in an offline, on-premise environment and capable of detecting AI-generated video, image, and audio content.” | official | 2026-06-21 |
| s6 | Contrary Research: Hive Business Breakdown & Founding Story “In April 2021, Hive announced that it had 100 enterprise customers, including NBCUniversal, Interpublic Group, Walmart, Visa, Anheuser-Busch InBev, and more. It also said it grew its customer base and revenue by over 300% in the prior year.” | research | 2026-06-21 |
| s7 | Contrary Research: Hive valuation and founders “In April 2021, Hive announced a $50 million Series D which valued the company at $2 billion. Hive was founded by Kevin Guo (CEO) and Dmitriy Karpman (CTO).” | research | 2026-06-21 |
| s8 | Hive: Data Labeling “We have more than 5M registered workers on our labeling platform. Our large, globally distributed workforce labels 10M+ items daily.” | official | 2026-06-21 |
| s9 | Wikipedia: Hive (artificial intelligence company) “Hive is reported to have been engaged to provide content moderation services to social news aggregator Reddit, Giphy, BeReal, Donald Trump-affiliated social network Truth Social, and on online chat website Chatroulette.” | press | 2026-06-21 |
| s10 | Defense Innovation Unit: DIU, DoD Collaborate To Strengthen Synthetic Media Defenses “Hive was one of the initial companies selected to prototype their deepfake detection and attribution technology, delivering innovative solutions for the intelligence community.” | press | 2026-06-21 |
| s11 | Hive: Pricing “Detect Harmful Content. Visual Moderation, Text Moderation, CSAM Detection, Audio Moderation, OCR Moderation, Demographic Classification. Detect AI Content. AI Image + Deepfake Classification, AI Video Detection, AI Audio Classification.” | official | 2026-06-21 |
| s12 | Hive: Enterprise Commitments “SOC-2 Certified Infrastructure - Hive adheres to SOC 2 Type II compliance, ensuring rigorous security, availability, and confidentiality for enterprises.” | official | 2026-06-25 |
| s13 | Deepfake-Eval-2024: A Multi-Modal In-the-Wild Benchmark of Deepfakes Circulated in 2024 “We evaluate commercially available deepfake detection models from companies that partnered with TrueMedia.org: Hive, Reality Defender, Pindrop, AI or Not, Hiya, Fraunhofer, and Sensity AI.” | research | 2026-06-29 |
| s14 | Frontiers in Artificial Intelligence: Detection of cloned voices in realistic forensic voice comparison scenarios “The detection tool selected for this study was HIVE AI Detector (Hive, 2023), which outperformed competing models as well as human expert analysis in an independent research study (Ha et al., 2024).” | research | 2026-06-29 |
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