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, which renamed itself Deploy in October 2026, sells online platforms and government agencies AI models that flag harmful content and detect AI-generated or deepfake media. Hive also hosts open source language models. Founded in 2017, it raised a Series D led by Glynn Capital in 2021, when TechCrunch named Reddit, Walmart and Visa among its roughly 100 customers. Deploy says it has more than 250 employees, has raised over $200 million and serves hundreds of organizations. The Defense Innovation Unit and a Homeland Security cybercrime center awarded Hive detection contracts of $2.4 million and $150,000. Its position is stronger in detection, which federal agencies have bought and outside researchers have tested, than in moderation, where Amazon and Microsoft sell rival services.
| Description | Hive, renamed Deploy in October 2026, builds AI models for content moderation, AI-generated and deepfake media detection, and intellectual property protection, sold through APIs and offline deployments, and hosts open source language models for developers and enterprises. | [f1] |
|---|---|---|
| Founded | 2017 | [f2] |
| HQ | San Francisco, California, United States | [f3] |
| Funding | $121M total | [f2] |
| Latest funding | Series D, $50M, April 2021, led by Glynn Capital, at a $2B valuation | [f2] |
| 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 and translation APIs for media and advertising workflows. |
| Open Source Frontier Models | Hosted inference for open source frontier language models, priced per million tokens, added when Hive became Deploy in October 2026. |
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. [f4]
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. | 4/5 | Hive's buyers are trust-and-safety teams screening user content and agencies checking media for synthetic manipulation, including child-exploitation investigators. MIT Technology Review reports that the DHS filing behind Hive's contract cites a 1,325% rise in generative-AI incidents in 2024, from National Center for Missing and Exploited Children data. The Defense Innovation Unit describes the synthetic-media threat it chose Hive to counter. [s14, s9, s2, s25] |
| Capability Depth How specific the technical capabilities are, with evidence beyond marketing claims such as docs and third-party validation. | 4/5 | Hive documents what its detection models return, confidence scores, the likely generating model where available and timestamps for video and audio, and offers offline or private-cloud deployment. Outside researchers have tested it by name. A 2024 University of Chicago study called it the clear winner among five automated detectors, at 98.03% accuracy on human and AI-generated art, and weaker against adversarial perturbations. A Frontiers in Artificial Intelligence study measured 0.86 precision on cloned voices, with degradation under noise. [s23, s17, s14, s13, s3] |
| Market Timing Whether the market is ready for this product, with evidence that buyers are actively seeking solutions. | 4/5 | Generative video, image and voice tools have made synthetic media a child-safety and fraud problem. Two kinds of buyer-side signal fall within about a year: a regulatory driver, the TAKE IT DOWN Act's duty on platforms to remove reported nonconsensual intimate images within 48 hours of a valid request, in force from May 19, 2026 (Inside Privacy), and government procurement, the DHS Cyber Crimes Center's AI-detection contract, described in a filing posted September 19 and reported by MIT Technology Review on September 26, 2025. [s25, s14, s3] |
| 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, a Q&A app that started as a Stanford class project, to more than 100 million users before founding Hive, and Hive announced a $2 billion valuation in 2021, per Contrary Research. That is one verifiable prior build, in consumer social media, and the reviewed sources describe no earlier security product or exit by either founder. [s6, s7] |
| GTM Proof Evidence of actual traction (customers, revenue signals, partnerships) beyond stated intentions. | 4/5 | TechCrunch's 2021 funding report named Reddit, Walmart, Visa and NBCUniversal among about 100 customers, figures Contrary Research says Hive announced. Two federal contracts are independently reported: the DoD's $2.4 million deepfake-detection award (MIT Technology Review, Biometric Update, DIU) and a $150,000 DHS Cyber Crimes Center contract (MIT Technology Review). Hive's current scale claims, hundreds of organizations sending billions of items a day, are its own figures. [s7, s6, s3, s4, s9, s14, s20] |
| Funding Efficiency Whether funding matches go-to-market ambition, with signs of capital-efficient growth. | 3/5 | TechCrunch reported in April 2021 that Hive had raised $121 million, had about 100 customers and had grown more than 300% in a year, and Deploy said in October 2026 that it has raised more than $200 million, naming no later round. Hive has kept shipping, including Hive Detect for Enterprises in January 2026, and won a $2.4 million DoD contract in December 2024, but it discloses no revenue, margin or growth figure newer than 2021, so the return on its capital is unconfirmed. [s7, s20, s15, s3] |
| Category Clarity Whether the company creates or fits a recognizable category that buyers can quickly place in their stack. | 4/5 | Independent sources place Hive in two established categories. MIT Technology Review covers it as a deepfake-detection vendor the DIU chose from 36 companies, and Contrary Research compares its image and video analysis with AWS and Microsoft Azure offerings. The October 2026 Deploy repositioning adds LLM inference. No independent coverage of Deploy in that category appears in the cited sources. [s3, s9, s6, s20] |
| Incumbent Defensibility How vulnerable the core value proposition is to absorption as a feature by a platform vendor. | 3/5 | Amazon and Microsoft both sell content-moderation services on their clouds, and Contrary Research says AWS and Azure offerings overlap Hive's image and video analysis, so the moderation line faces cloud providers' own products. The deepfake line has more friction: offline, on-premise deployment for the DoD and named academic evaluations in which Hive led other detectors. No reviewed source documents either as a barrier that Amazon or Microsoft could not reproduce with detectors of their own. [s18, s19, s6, s5, s17] |
Hive sells into two problems from one model catalog. Its moderation models help platforms enforce content policies across images, video, text and audio. Since May 19, 2026, the TAKE IT DOWN Act, which Hive endorsed, has required covered platforms that receive a valid request to remove the nonconsensual intimate image, and make reasonable efforts to remove known identical copies, within 48 hours, a legal deadline on the kind of company that buys moderation.
The second problem is synthetic media. MIT Technology Review reports that the DHS filing for Hive's contract cites a 1,325% rise in generative-AI incidents in 2024, from National Center for Missing and Exploited Children data. The Defense Innovation Unit describes synthetic media as a threat the DoD needs to identify and counter.
The buyers differ by line. Trust-and-safety teams at digital platforms buy moderation, while intelligence agencies and child-exploitation investigators buy detection of AI-generated media. In October 2026 Hive renamed itself Deploy and added a third pitch, low-cost hosting of open source frontier language models. [s20, s25, s14, s9, s2]
Hive's model catalog spans content moderation, AI-generated media detection, intellectual property protection and ad intelligence. Its moderation models analyze images, video, text and audio, and its pricing page lists a child sexual abuse material detection model built on Thorn's technology. Its detection models return confidence scores, the likely generating model where available, and timestamps for video and audio.
The deployment options matter to government buyers. For the Defense Innovation Unit, Hive deployed deepfake detection in an offline, on-premise environment. Its detection models are offered for offline or private-cloud use.
Outside researchers have tested the detectors by name. A 2024 University of Chicago study called Hive the clear winner among five automated detectors at 98.03% accuracy on human and AI-generated art, while finding it weaker against adversarial perturbations. A Frontiers in Artificial Intelligence forensic study measured 0.86 precision on cloned voices and found performance degraded under babble noise and signal saturation.
The newest line is general inference. Deploy hosts open source frontier language models, priced per million tokens, alongside a multimodal model that it says replaces its other models for many customers. [s22, s20, s10, s23, s5, s16, s17, s14, s13, s24]
Hive positions as a broad model supplier rather than a single-purpose detector. Deploy offers moderation across images, video, text and audio alongside hosted language models.
The moderation line overlaps cloud providers' own services. Amazon Rekognition Content Moderation and Azure AI Content Safety both classify harmful images or text, and Contrary Research says AWS and Azure offerings overlap Hive in image and video analysis.
The deepfake line competes with specialist detectors. An arXiv benchmark of commercial deepfake detectors evaluated Hive alongside Reality Defender, Pindrop and Sensity AI, though it anonymized each vendor's results. Hive's distinguishing evidence there is its offline deployment for the DoD and the named University of Chicago and Frontiers evaluations. [s20, s18, s19, s6, s12, s5, s17, s13]
Hive's named commercial customers come mostly from 2021 reporting. TechCrunch's funding report that year named Reddit, Yubo, Chatroulette, Tango, NBCUniversal, Interpublic Group, Walmart, Visa and Anheuser-Busch InBev among about 100 customers, with growth above 300% in the prior year, figures Contrary Research says Hive announced.
Government work is the newest independently reported traction. The DoD put $2.4 million over two years into Hive's deepfake detection. The DIU chose Hive from 36 entrants. MIT Technology Review reported on September 26, 2025 that the DHS Cyber Crimes Center had awarded Hive a $150,000 contract for a three-month trial of its AI-detection software.
The company's current claims are larger and its own. Deploy says hundreds of organizations send billions of pieces of content a day through its APIs, and Hive's own homepage says hundreds of companies trust it. [s7, s6, s3, s9, s14, s20, s1]
Hive's founders built at consumer scale before founding the company. Kevin Guo and Dmitriy Karpman started Kiwi, a mobile Q&A app, as a Stanford class project and grew it to more than 100 million users. Kiwi's own moderation needs led them to build the models that became Hive, which TechCrunch dates to 2017.
Guo remains chief executive under the Deploy name. MIT Technology Review quotes him as chief executive on the DoD and DHS contracts, and the October 2026 rebrand announcement quotes him as CEO of Deploy.
The founders are known for AI and consumer products. The reviewed sources describe no earlier security product or exit by either founder. [s6, s7, s3, s14, s20]
Hive's readiness for regulated buyers shows most clearly in deployment. Under its Defense Innovation Unit contract, Hive says it provided five detection models for an offline, on-premise environment, and it offers its detection models for private-cloud use.
Deploy states on its enterprise commitments page that it adheres to SOC 2 Type II, that submitted content stays under customer control, and that customers can set retention rules. These are the company's own statements, with no audit report among the reviewed sources.
The labeling operation is a procurement question of its own. Hive says more than 5 million registered workers label over 10 million items daily, and it publishes quality controls: each task goes to multiple contributors for independent corroboration and each project is checked before delivery. The reviewed sources do not describe worker-welfare or data-handling rules for that workforce. [s5, s23, s11, s8]
| Company | Relationship | Note | Compare |
|---|---|---|---|
| Reality Defender | competes with | An arXiv benchmark of commercial deepfake detectors evaluated Reality Defender alongside Hive. | |
| Sensity AI | competes with | An arXiv benchmark of commercial deepfake detectors evaluated Sensity AI alongside Hive on the synthetic-media detection line. | |
| Amazon Rekognition | competes with | Contrary Research names AWS offerings that overlap Hive's image and video analysis, and Amazon sells content moderation through Rekognition. | |
| Microsoft Azure AI Content Safety | competes with | Contrary Research names Azure offerings that overlap Hive's image and video analysis, and Microsoft sells harmful-content detection through Azure AI Content Safety. |
Add analyzed competitors to compare them side by side with Deploy.
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
Hive, founded in 2017 and renamed Deploy in October 2026, sells online platforms and government agencies AI models that flag harmful content and detect AI-generated media. It trains them on data labeled on its own platform, which it says has more than 5 million registered workers. University of Chicago researchers found Hive's detector the clear winner among five at spotting AI-generated art, and weaker against images altered to fool it. A platform leaving Hive may have to replace and retest the API calls in its review workflow, while an agency running Hive offline may have to install and validate a replacement. A rival would have to match Hive's offline deployment and its outside test results, which Hive has to defend by updating its models as AI tools change.
| 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 hosted inference that they call from their own products, and Deploy prices them per request or per million tokens. Hive also sells a managed data collection and annotation service, a human-staffed offering beside the model catalog, and the reviewed sources do not show it as the main thing customers pay for. |
| Switching Cost How expensive leaving is for a customer: data portability, integrations, learned workflows, network effects, regulatory data residency. | 2/3 | A platform that wired Hive's moderation or detection API into its review pipeline would have to repoint and retest that integration, and agencies running the offline deployment would have to install and validate a replacement. These are integration and validation costs, and the cited record documents no customer data or configuration that cannot be transferred, or measured migration effort that would raise them. |
| Compliance Moat Whether certifications, liability acceptance, or audit trails block an easy replacement. | 1/3 | Deploy states SOC 2 Type II adherence, an attestation funded rivals can also obtain. The federal deepfake-detection work consists of contract awards, a DIU prototype and a DHS trial, rather than an authorization only Hive holds, so a competing vendor could qualify for the same procurements. |
| Problem Complexity Whether the product requires ML, optimization, real-time systems, or years of specialized expertise. | 3/3 | Hive builds classifiers across images, video, text and audio and detectors for AI-generated media. University of Chicago researchers found Hive led other detectors on human and AI-generated art but was weaker against adversarial perturbations. Researchers publishing in Frontiers found its audio detection degraded under noise. Running a multi-million-worker labeling pipeline adds operational difficulty. |
| Buyer Profile Whether buyers are SMB operators, mid-market IT teams, or regulated enterprises and governments with procurement gates. | 3/3 | Hive builds and sells its detection line for government agencies, and two have bought it: the DoD's Defense Innovation Unit and the DHS Cyber Crimes Center. TechCrunch's 2021 report named large enterprises such as Walmart and Visa among its customers, while Deploy's APIs are also open to individual developers. |
| Layer Whether the product is an end-user application, a platform with application features, or infrastructure other applications depend on. | 2/3 | Hive's established products return classification scores that the customer's own pipeline acts on, a signal feeding moderation, fraud and government workflows. Deploy offers hosted language-model inference, which it describes as production-ready inference infrastructure. No customer running applications on it appears in the reviewed sources. |
| Proprietary Data, Content, or IP Whether the product accumulates datasets, content licenses, or IP that a rival cannot recreate from scratch. | 2/3 | Hive retains a training corpus built by its own labeling workforce: Contrary Research says data its contributors source and annotate trains and refines Hive's models, and Hive reports more than 5 million registered workers labeling over 10 million items a day. The corpus accrues to Hive, and no cited source shows it published, though a well-funded rival could assemble labeled data of its own with time. |
Hive sells to two buyer groups from one model catalog. Trust-and-safety teams at digital platforms buy moderation, and TechCrunch's 2021 funding report named Reddit, Yubo, Chatroulette, Tango, Walmart, Visa and NBCUniversal among about 100 customers. Hive's government page markets synthetic-media detection and attribution to agencies, including investigation of child sexual abuse material and fraud detection.
The government segment has two independently reported buyers. The DoD's Defense Innovation Unit chose Hive from 36 entrants for a $2.4 million, two-year deepfake-detection contract. The DHS Cyber Crimes Center, which investigates child exploitation, awarded it a $150,000 contract for software that identifies AI-generated content.
The October 2026 rename to Deploy adds a third audience. Deploy now sells hosted open source language models to developers and enterprises, and its enterprise commitments page says individuals, developers and hobbyists may also use its APIs. The company spans many verticals and model types at once, which widens its market and spreads its focus.
Hive ships a wide catalog of pre-trained models for content moderation, AI-generated media detection, intellectual property protection and ad intelligence. Its detection models return confidence scores, the likely generating model where available, and timestamps for video and audio, and Deploy says it regularly upgrades them to cover new generators.
The claimed advantage is model quality fed by a large labeling operation. Contrary Research describes Hive's models as built with training data sourced and annotated by a distributed workforce. Hive says that workforce exceeds 5 million registered workers labeling over 10 million items a day.
Independent evaluations support the detection core and also mark its limits. A 2024 University of Chicago study called Hive the clear winner among five automated detectors on AI-generated art, and found it weaker against adversarial perturbations. A Frontiers in Artificial Intelligence forensic study measured 0.86 precision under ideal conditions on voices cloned with commercial algorithms, with performance degraded by babble noise and signal saturation. Ben Zhao, a University of Chicago professor who evaluated Hive's technology, told MIT Technology Review that it is far from foolproof and not hard to circumvent.
Hive's commercial motion is developer- and enterprise-led. Buyers integrate pre-trained models through APIs with usage-based pricing, and a no-code application, Hive Detect for Enterprises, launched in January 2026 for teams that verify media without using an API.
The government motion runs through contracts. The Defense Innovation Unit award and the 2025 DHS Cyber Crimes Center trial are the two federal deals in the reviewed sources. MIT Technology Review reported both. Hive markets to agencies through a dedicated solutions page.
The customer count and growth rate date to 2021, while Deploy's current claims, hundreds of organizations sending billions of items a day, are its own figures.
Deploy prices usage, posting rates for entry tiers. Visual Moderation lists $3.00 per 1,000 requests with a 100-request daily limit, text moderation is cheaper per request, and hosted language models are priced per million tokens.
Detection and larger accounts are sales-quoted. Deploy lists the AI-generated image and deepfake classifier under Contact Sales and offers custom pricing to fit an organization's usage.
Government deployments sit outside the posted rates. The offline, on-premise federal deployment was a contract award rather than metered API access, so posted per-request prices serve entry-level platform use while larger customers and agencies negotiate.
Commercial delivery is cloud API first. Developers call hosted models and receive classifications, and Hive says it serves real-time responses to billions of API calls a month.
Government delivery is a harder operating mode. For the Defense Innovation Unit, Hive deployed detection models in an offline, on-premise environment, and its detection models are also offered through NVIDIA NIM and for private-cloud use.
Two upstream operations support both tracks. The labeling platform supplies annotated training data, and Deploy says it has invested in operating its own secure datacenter infrastructure to scale with customer demand. Deploy hosts open source language models.
Hive's readiness for regulated buyers shows most clearly in deployment. Under its Defense Innovation Unit contract, Hive says it provided five detection models for an offline, on-premise environment, and it offers its detection models for private-cloud use where data privacy standards require it.
Deploy states that it adheres to SOC 2 Type II, that submitted content stays under customer control, and that customers can set retention rules. These are the company's own statements, with no audit report among the reviewed sources.
The labeling workforce brings its own governance questions. Hive publishes quality controls, sending each task to multiple contributors for independent corroboration and checking each project before delivery. No worker-welfare or data-handling rules for that workforce appear in the reviewed sources.
Hive mostly supplies classifications that other products embed. Its models feed platform moderation pipelines, fraud workflows and government systems, and it also sells its own applications, Hive Detect for Enterprises among them.
Customers can also build on its platform. Deploy lets them fine-tune their own classification or language models and release those models for inference on Deploy, and Hive offers its detection models through NVIDIA NIM for private-cloud deployment.
The rename to Deploy pushes toward a platform role. Deploy now hosts open source frontier language models beside its own, and on its about page it describes itself as providing infrastructure to run them, a position it announced on October 26, 2026.
Hive's founders built at consumer scale before founding the company. Kevin Guo and Dmitriy Karpman started Kiwi, a mobile Q&A app, as a Stanford class project and grew it to more than 100 million users, and Kiwi's moderation needs led them to build the models behind Hive.
Guo leads the company under its new name. MIT Technology Review quotes him as chief executive on the federal contracts, and the October 2026 rebrand announcement quotes him as CEO of Deploy. Deploy says it has more than 250 employees across San Francisco, Seattle, New York and Delhi.
The founders are known for AI and consumer products. The reviewed sources describe no earlier security product or exit by either founder.
| Id | Source | Tier | Accessed |
|---|---|---|---|
| f1 | https://deploy.com/about-us | official | 2026-10-31 |
| f2 | https://techcrunch.com/2021/04/21/hive-raises-85m-for-ai-based-apis-to-help-moderate-content-identify-objects-and-more/ | press | 2026-10-31 |
| f3 | https://www.biometricupdate.com/202412/dod-awards-contract-for-deepfake-detection-to-hive | press | 2026-10-31 |
| f4 | https://thehive.ai/solutions/government-agencies | official | 2026-10-31 |
| Id | Source | Tier | Accessed |
|---|---|---|---|
| s1 | Hive: homepage “Hive is trusted by hundreds of the world’s largest, fastest-growing, and most innovative companies” | official | 2026-10-31 |
| s2 | Hive: solutions page 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 | 2026-10-31 |
| 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.” | press | 2026-10-31 |
| 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.” | press | 2026-10-31 |
| s5 | Hive: blog post announcing its Defense Innovation Unit partnership “We are honored to join forces with the Department of Defense in this critical mission.” | official | 2026-10-31 |
| s6 | Contrary Research: Hive business breakdown “The company offers a portfolio of deep learning models built with training data sourced and annotated by a distributed workforce of contributors.” | research | 2026-10-31 |
| s7 | TechCrunch: Hive raises $85M for AI-based APIs to help moderate content, identify objects and more “The company has now raised $121 million.” | press | 2026-10-31 |
| s8 | Hive: data labeling service page “We have more than 5M registered workers on our labeling platform. Our large, globally distributed workforce labels 10M+ items daily.” | official | 2026-10-31 |
| s9 | Defense Innovation Unit: DIU and DoD collaborate to strengthen synthetic media detection capabilities “Hive was one of the initial companies selected to prototype their deepfake detection and attribution technology, delivering innovative solutions to enhance the DoD's ability to identify and counter synthetic media threats with precision and speed.” | regulatory | 2026-10-31 |
| s10 | Deploy: pricing page (thehive.ai/pricing redirects here) “Flexible, usage-based plans made to suit your needs.” | official | 2026-10-31 |
| s11 | Deploy: enterprise commitments page “Deploy adheres to SOC 2 Type II compliance, ensuring rigorous security, availability, and confidentiality for enterprises.” | official | 2026-10-31 |
| s12 | arXiv preprint: 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 | 2026-10-31 |
| s13 | Frontiers in Artificial Intelligence: Detection of cloned voices in realistic forensic voice comparison scenarios “Our findings indicate that HIVE achieves strong performance (precision = 0.86) on commercial voice cloning algorithms in ideal conditions and demonstrates high accuracy in differentiating original from cloned voices.” | research | 2026-10-31 |
| s14 | MIT Technology Review: US investigators are using AI to detect child abuse images made by AI “The Department of Homeland Security’s Cyber Crimes Center, which investigates child exploitation across international borders, has awarded a $150,000 contract to San Francisco–based Hive AI for its software, which can identify whether a piece of content was AI-generated.” | press | 2026-10-31 |
| s15 | Hive: blog post introducing Hive Detect for Enterprises “Today, we are excited to launch Hive Detect for Enterprises, a new way for organizations to access Hive’s best-in-class AI-generated and deepfake detection models in a team-ready user experience.” | official | 2026-10-31 |
| s16 | Hive: blog post announcing its integration with NVIDIA NIM “The first Hive models to be made available with NVIDIA NIM are our AI-generated content detection models, which allow customers to identify AI-generated images, video, and audio.” | official | 2026-10-31 |
| s17 | arXiv preprint: Organic or Diffused: Can We Distinguish Human Art from AI-generated Images? “Hive is the clear winner among all five detectors, with a
98.03% accuracy, 0% FPR (i.e., it never misclassifies human
artworks), and 3.17% FNR (i.e., it rarely misclassifies AI-generated
images).” | research | 2026-10-31 |
| s18 | Amazon Web Services: Amazon Rekognition Content Moderation page, a competitor's page “Amazon Rekognition Content Moderation automates and streamlines your image and video moderation workflows using machine learning (ML), without requiring ML experience.” | official | 2026-10-31 |
| s19 | Microsoft Azure: Azure AI Content Safety page, a competitor's page “Detect and block violence, hate, sexual, and self-harm content. Configure severity thresholds for your specific use case, and adhere to your responsible AI policies.” | official | 2026-10-31 |
| s20 | Deploy (formerly Hive): blog post Hive is now Deploy “Today, we are thrilled to announce that Hive is now Deploy.” | official | 2026-10-31 |
| s21 | Deploy: homepage “Intelligence Infrastructure for Every Ambition | Deploy” | official | 2026-10-31 |
| s22 | Deploy: about page “Deploy also develops specialized AI models for content understanding, including best-in-class classifiers and detectors that solve urgent challenges across content moderation, AI detection, intellectual property protection and ad intelligence.” | official | 2026-10-31 |
| s23 | Deploy: AI-generated and deepfake content detection product page “Deploy's AI Detection models return confidence scores for whether media is AI-generated and, when available, attribution for likely model behind it. For video and audio content, timestamps are returned.” | official | 2026-10-31 |
| s24 | Deploy: open source frontier models product page “The latest open source frontier LLMs, now live on Deploy” | official | 2026-10-31 |
| s25 | Inside Privacy (Covington): The TAKE IT DOWN Act's notice and removal requirements enter into effect “Covered platforms that receive valid requests must remove the visual depictions and identical copies within 48 hours.” | press | 2026-10-31 |
| Id | Source | Tier | Accessed |
|---|---|---|---|
| s1 | Hive: homepage “Hive is trusted by hundreds of the world’s largest, fastest-growing, and most innovative companies” | official | 2026-10-31 |
| s2 | Hive: solutions page 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 | 2026-10-31 |
| 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.” | press | 2026-10-31 |
| 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.” | press | 2026-10-31 |
| s5 | Hive: blog post announcing its Defense Innovation Unit partnership “We are honored to join forces with the Department of Defense in this critical mission.” | official | 2026-10-31 |
| s6 | Contrary Research: Hive business breakdown “The company offers a portfolio of deep learning models built with training data sourced and annotated by a distributed workforce of contributors.” | research | 2026-10-31 |
| s7 | TechCrunch: Hive raises $85M for AI-based APIs to help moderate content, identify objects and more “The company has now raised $121 million.” | press | 2026-10-31 |
| s8 | Hive: data labeling service page “We have more than 5M registered workers on our labeling platform. Our large, globally distributed workforce labels 10M+ items daily.” | official | 2026-10-31 |
| s9 | Defense Innovation Unit: DIU and DoD collaborate to strengthen synthetic media detection capabilities “Hive was one of the initial companies selected to prototype their deepfake detection and attribution technology, delivering innovative solutions to enhance the DoD's ability to identify and counter synthetic media threats with precision and speed.” | regulatory | 2026-10-31 |
| s10 | Deploy: pricing page (thehive.ai/pricing redirects here) “Flexible, usage-based plans made to suit your needs.” | official | 2026-10-31 |
| s11 | Deploy: enterprise commitments page “Deploy adheres to SOC 2 Type II compliance, ensuring rigorous security, availability, and confidentiality for enterprises.” | official | 2026-10-31 |
| s12 | arXiv preprint: 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 | 2026-10-31 |
| s13 | Frontiers in Artificial Intelligence: Detection of cloned voices in realistic forensic voice comparison scenarios “Our findings indicate that HIVE achieves strong performance (precision = 0.86) on commercial voice cloning algorithms in ideal conditions and demonstrates high accuracy in differentiating original from cloned voices.” | research | 2026-10-31 |
| s14 | MIT Technology Review: US investigators are using AI to detect child abuse images made by AI “The Department of Homeland Security’s Cyber Crimes Center, which investigates child exploitation across international borders, has awarded a $150,000 contract to San Francisco–based Hive AI for its software, which can identify whether a piece of content was AI-generated.” | press | 2026-10-31 |
| s15 | Hive: blog post introducing Hive Detect for Enterprises “Today, we are excited to launch Hive Detect for Enterprises, a new way for organizations to access Hive’s best-in-class AI-generated and deepfake detection models in a team-ready user experience.” | official | 2026-10-31 |
| s16 | Hive: blog post announcing its integration with NVIDIA NIM “The first Hive models to be made available with NVIDIA NIM are our AI-generated content detection models, which allow customers to identify AI-generated images, video, and audio.” | official | 2026-10-31 |
| s17 | arXiv preprint: Organic or Diffused: Can We Distinguish Human Art from AI-generated Images? “Hive is the clear winner among all five detectors, with a
98.03% accuracy, 0% FPR (i.e., it never misclassifies human
artworks), and 3.17% FNR (i.e., it rarely misclassifies AI-generated
images).” | research | 2026-10-31 |
| s18 | Amazon Web Services: Amazon Rekognition Content Moderation page, a competitor's page “Amazon Rekognition Content Moderation automates and streamlines your image and video moderation workflows using machine learning (ML), without requiring ML experience.” | official | 2026-10-31 |
| s19 | Microsoft Azure: Azure AI Content Safety page, a competitor's page “Detect and block violence, hate, sexual, and self-harm content. Configure severity thresholds for your specific use case, and adhere to your responsible AI policies.” | official | 2026-10-31 |
| s20 | Deploy (formerly Hive): blog post Hive is now Deploy “Today, we are thrilled to announce that Hive is now Deploy.” | official | 2026-10-31 |
| s21 | Deploy: homepage “Intelligence Infrastructure for Every Ambition | Deploy” | official | 2026-10-31 |
| s22 | Deploy: about page “Deploy also develops specialized AI models for content understanding, including best-in-class classifiers and detectors that solve urgent challenges across content moderation, AI detection, intellectual property protection and ad intelligence.” | official | 2026-10-31 |
| s23 | Deploy: AI-generated and deepfake content detection product page “Deploy's AI Detection models return confidence scores for whether media is AI-generated and, when available, attribution for likely model behind it. For video and audio content, timestamps are returned.” | official | 2026-10-31 |
| s24 | Deploy: open source frontier models product page “The latest open source frontier LLMs, now live on Deploy” | official | 2026-10-31 |
| s25 | Inside Privacy (Covington): The TAKE IT DOWN Act's notice and removal requirements enter into effect “Covered platforms that receive valid requests must remove the visual depictions and identical copies within 48 hours.” | press | 2026-10-31 |
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