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.
Check Point agreed in May 2026 to buy the team and intellectual property of Deepchecks, an Israeli company whose software tests and monitors AI applications, in a deal industry sources put at $10 million to $20 million. What Check Point is buying is the team and its technology. Deepchecks published an academic paper co-authored by its CTO and maintains an open-source machine-learning testing library with more than 4,000 GitHub stars, and Check Point said the team would accelerate its work on autonomous agents that run network-security operations. The commercial side is thinner. Deepchecks raised $14 million after its 2019 founding and names no paying customers in the public record, so the deal reads as a purchase of a credible team and its technology rather than a growing business.
| Description | Deepchecks makes a platform that AI teams use to test, evaluate, and monitor their large language model applications and agents, tracking quality once those systems run in production. | [f1] |
|---|---|---|
| Founded | 2019 | [f2] |
| HQ | Israel | [f2] |
| Funding | $14M total | [f2] |
| Product | What it does |
|---|---|
| Deepchecks LLM Evaluation | Enterprise AI testing, observability, and monitoring platform that evaluates prompts, models, and agents, runs an LLM pentesting environment, and monitors production quality. |
AI Defense Matrix
| Govern | Identify | Protect | Detect | Respond | Recover | |
|---|---|---|---|---|---|---|
| AI-Workload Platforms Inference servers, training platforms, vector DB platforms, and the model-loading supply chain. | ||||||
| AI Orchestration Tools Agentic orchestration tools, plus their plugins, skills, hooks, system prompts, scaffolding, harnesses, configuration settings, and MCP clients on user devices. | ||||||
| AI-Generated Code Code produced by AI tools, AI-assisted reviews, AI-generated infrastructure-as-code and tests, and vibe-coded apps that bypass CI/CD. | ||||||
| AI Gateways & Routers MCP proxies and gateways, LLM routers, outbound AI-service traffic, shadow AI egress, and model-registry traffic. | ||||||
| AI Model Model weights, fine-tuning checkpoints, model cards, registries, AIBOM, and the third-party LLMs your enterprise consumes. | ||||||
| Training Data Datasets used for training, fine-tuning, and continued learning. | ||||||
| Runtime AI Data User prompts, inference inputs, RAG content, vector DB content, persistent agent memory, and interaction history. | ||||||
| AI Agent Identities AI agents as non-human principals, plus credentials, keys, permission scopes, service accounts, and delegation chains across agents and tools. |
Deepchecks monitors LLM apps by tracking annotation and property scores to detect degradation, generates adversarial prompts (prompt injection, jailbreaks, PII extraction, bias) in a pentest environment, and evaluates agentic pipelines. These capabilities are mapped to the AI Defense Matrix. [f3]
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 | Deepchecks names a clear buyer and problem, teams shipping LLM and machine-learning applications that can fail silently in production, but the pain is stated on its own pages and in a paper its founders authored rather than quantified by an independent source, holding it at the present-but- unproven level shared with the AI-testing peer set. [s1, s6, s10] |
| Capability Depth How specific the technical capabilities are, with evidence beyond marketing claims such as docs and third-party validation. | 4/5 | The commercial platform documents adversarial pentesting, agent evaluation, and production monitoring that tracks annotation and property scores for degradation, and the company also maintains an open-source machine-learning validation library with more than 4,000 GitHub stars plus an arXiv preprint describing it, a public technical account beyond its own marketing. [s2, s3, s4, s5, s6] |
| Market Timing Whether the market is ready for this product, with evidence that buyers are actively seeking solutions. | 3/5 | Deepchecks began in 2019 as open-source machine-learning testing and repositioned to LLM and agent evaluation as production language-model applications spread, and Check Point's May 2026 purchase to build autonomous AI-security agents signals demand for AI oversight, but buyer-side demand for Deepchecks specifically is indirect rather than the multiple independent signals a higher score needs. [s1, s8, s9] |
| Team Credibility Demonstrated domain expertise with public signals such as prior exits, publications, and industry recognition. | 3/5 | Founders Philip Tannor and Shir Chorev built a machine-learning testing library with a sustained developer following and published an arXiv paper on it, and Check Point's agreement to buy the team is a real domain signal, but the modest, still-pending team-and-technology deal and the absence of a documented prior exit hold the team at the present-but-unproven level. [s4, s6, s7, s8] |
| GTM Proof Evidence of actual traction (customers, revenue signals, partnerships) beyond stated intentions. | 2/5 | Deepchecks shows open-source adoption of more than 4,000 GitHub stars but names no paying customer of the commercial platform in the reviewed sources, and Check Point's roughly $10 million to $20 million purchase of the team and intellectual property rather than a customer base points to thin commercial traction, below the peer set that names production references. [s4, s5, s7, s8] |
| Funding Efficiency Whether funding matches go-to-market ambition, with signs of capital-efficient growth. | 2/5 | Deepchecks raised $14 million after its 2019 founding with no disclosed later round, and the announced outcome is a still-pending team-and-technology sale to Check Point rather than a growth round, a stale-raise-past-a-full-cycle pattern that sits below the deploying-startup default. [s7, s8] |
| Category Clarity Whether the company creates or fits a recognizable category that buyers can quickly place in their stack. | 3/5 | Deepchecks fits the forming AI and LLM testing, evaluation, and observability category, but the category is nascent and contested and placement still needs vendor explanation. [s1, s2] |
| Incumbent Defensibility How vulnerable the core value proposition is to absorption as a feature by a platform vendor. | 3/5 | Automated LLM evaluation and monitoring has workflow friction but no structural moat a platform vendor could not replicate, and Check Point chose to acquire the team and technology rather than build the capability, which shows absorption value without establishing durability. [s2, s4, s8] |
Deepchecks sells software that tests and monitors the AI applications an organization puts into production. The homepage frames the product as an enterprise-grade platform for AI testing, observability, and monitoring that gives teams visibility and control over LLM and agent systems in production. The buyer is the engineering or data-science team responsible for shipping a language-model application and answerable for its quality.
The problem traces to the company's origin. Deepchecks began as an open-source library for validating machine-learning models and data, built by a team that found no tool to tell them when a model was failing silently. That same failure mode, an AI system that degrades or misbehaves without an obvious error, is what the commercial platform now watches for in LLM and agent applications.
The pain is credible but vendor-framed. Deepchecks and a paper its founders published describe the testing problem, and the broader market signal is Check Point's decision to buy the team to build AI oversight into its own products, yet no independent source quantifies the specific pain for Deepchecks' buyers. [s1, s6, s9, s10]
Deepchecks ships across two layers, a maintained open-source library and a commercial LLM evaluation platform. The open-source project tests machine-learning models and data from research to production and carries more than 4,000 GitHub stars, and the company published an arXiv paper describing the library. That library is the credibility base the commercial product builds on.
The commercial platform, Deepchecks LLM Evaluation, evaluates prompts, models, and agents across the application lifecycle. Its documentation, quoted in the AI Defense Matrix Catalog, describes an LLM pentesting environment that fires adversarial prompts including prompt injection, jailbreaks, PII extraction, and bias triggers, alongside an end-to-end agent evaluation pipeline.
Its production capability is monitoring rather than blocking. Deepchecks watches a deployed application over time, tracking annotation trends and property scores and flagging degradation automatically, and it offers both a managed SaaS and a self-hosted deployment for data-privacy constraints. The product observes and evaluates AI outputs rather than intercepting them in real time. [s2, s3, s4, s5, s6]
Deepchecks competes in AI testing, evaluation, and observability, a category populated by both independent specialists and the observability platforms adding LLM support. Its distinguishing move is the open-source testing library that seeds developer adoption ahead of any commercial sale, the same bottom-up motion several peers in this category run.
The company's differentiator is testing depth backed by published research rather than a proprietary data asset. The maintained library and the arXiv paper give Deepchecks a public, inspectable body of testing work, though the underlying methods are reproducible by a funded rival.
Absorption by a larger vendor is the main risk, and it has already materialized. Check Point agreed to buy the team and technology to build AI oversight into its own security platform, direct evidence that a larger vendor can take the capability in-house rather than buy Deepchecks as a standalone product. [s4, s6, s8, s9]
Deepchecks' clearest go-to-market engine is its open-source library. More than 4,000 GitHub stars show developer interest in the free testing tool, which the company converts into awareness for its commercial platform. This is bottom-up adoption rather than proof that enterprises buy the paid product.
Named paying-customer evidence is absent from the reviewed sources. The public record shows the open-source following and the company's own product pages, but no named reference customer of the commercial LLM Evaluation platform, so paid traction stays unproven.
The acquisition itself says the most about commercial traction. Check Point agreed to buy the team and technology at a figure industry sources put at $10 million to $20 million, taking engineering talent and intellectual property rather than a book of paying customers, which reads as limited standalone commercial traction. [s4, s5, s7, s8]
Deepchecks was founded in 2019 by CEO Philip Tannor and CTO Shir Chorev, who describe the team as machine-learning practitioners who led research groups before building the company. Their public track record is the testing library and the research behind it.
The team's strongest verifiable signal is sustained open-source and research output. The founders maintained a machine-learning validation library with a real developer following and published an arXiv paper on it, an in-domain body of work rather than a single event. That record is what Check Point valued in acquiring the team.
The gap relative to stronger-scoring peers is a documented prior exit or marquee pedigree. The reviewed sources show no prior founder exit, and the Check Point deal is a modest team-and-technology acquisition rather than a large outcome, so the team's credibility comes from its testing work more than from a track record of scaled companies. [s4, s6, s7, s8]
Deepchecks offers deployment choices that matter for buyers with data-privacy constraints. The platform is available as a managed multi-tenant SaaS and as a self-hosted option, letting a team keep evaluation data inside its own environment. For a tool that inspects proprietary AI applications, that self-hosted path is part of the trust argument.
Open-source transparency is the second element. Because the core testing library is open source, a technical evaluator can read the testing logic directly rather than trust a black box, which can support a trust review when a vendor's product examines a customer's AI systems.
Procurement now runs through the acquirer. With Check Point acquiring the team and technology, a buyer evaluating Deepchecks is increasingly weighing a Check Point roadmap decision rather than an independent vendor's offering. [s1, s4, s8]
| Company | Relationship | Note | Compare |
|---|---|---|---|
| Arize AI | competes with | AI observability and evaluation platform contesting the same production LLM monitoring and testing buyer. | 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. |
| Fiddler AI | competes with | AI observability and monitoring platform that also scores model and LLM outputs, overlapping Deepchecks' evaluation motion. | |
| Giskard | competes with | Open-source AI testing and red-teaming vendor with the same free-library-to-enterprise motion. | |
| Patronus AI | competes with | LLM evaluation specialist competing for the same AI testing and quality-assurance buyer. |
Add analyzed competitors to compare them side by side with Deepchecks.
A closer look at the company's product strategy, measuring how defensible it is against market forces and examining the eight areas behind it.
pivot urgently
Deepchecks' edge is engineering, not an asset a rival is barred from building. Evaluating prompts, models, and agents and tracking production quality for degradation is hard machine-learning work, and a team that adopts it wires evaluation and monitoring into how it ships AI, so leaving means rebuilding that wiring around a replacement. Beyond that the moat is thin. Customers run the software themselves, the record shows no regulation requiring AI testing, the library is open source, and no private dataset sits behind its checks. It tests AI after the fact rather than blocking bad outputs live, so a buyer can swap in another evaluation tool. Check Point's team-and-technology purchase at a modest reported price points the same way, though the deal terms do not say why Deepchecks sold.
| 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 | Deepchecks delivers software the customer configures and runs, the open-source library and the managed or self-hosted LLM Evaluation platform, with no evidence in the reviewed sources of a human-expertise or managed-service layer that accepts accountability for outcomes, which is the software-product level of delivery. |
| Switching Cost How expensive leaving is for a customer: data portability, integrations, learned workflows, network effects, regulatory data residency. | 2/3 | A team that adopts the platform wires evaluation, pentesting, and production monitoring into how it ships AI, so leaving means rebuilding that wiring around a replacement, a real reintegration cost in effort, though the free open-source library keeps the floor low. |
| Compliance Moat Whether certifications, liability acceptance, or audit trails block an easy replacement. | 1/3 | Deepchecks self-displays SOC 2 Type 2, GDPR, and HIPAA compliance, table-stakes assurance, and the cited record identifies no regulation requiring AI testing or evaluation, so nothing in the record forces a buyer to purchase the platform and compliance is not a barrier a rival must clear to compete. |
| Problem Complexity Whether the product requires ML, optimization, real-time systems, or years of specialized expertise. | 3/3 | Building automated LLM and agent evaluation, an adversarial pentesting environment that runs injection and jailbreak prompts, and production monitoring that detects quality degradation is hard applied machine-learning work, supported by an arXiv paper on the underlying library. |
| Buyer Profile Whether buyers are SMB operators, mid-market IT teams, or regulated enterprises and governments with procurement gates. | 2/3 | The buyer is a machine-learning or AI engineering team adopting a testing and monitoring tool, a developer-adjacent purchaser rather than a security or compliance function with independent budget authority. |
| Layer Whether the product is an end-user application, a platform with application features, or infrastructure other applications depend on. | 2/3 | Deepchecks runs beside the AI workload as an out-of-path testing and monitoring layer that a customer can adopt and remove without re-architecting, and the fetched pages document testing and monitoring rather than inline enforcement. |
| Proprietary Data, Content, or IP Whether the product accumulates datasets, content licenses, or IP that a rival cannot recreate from scratch. | 1/3 | The core testing library is open source, the evaluation and pentesting techniques are documented and reproducible, and the reviewed sources name no private dataset behind the checks, so any detection advantage can be rebuilt by a funded rival. |
Deepchecks targets the team putting an LLM or agent application into production and answerable for its quality. The homepage frames the buyer as an organization that needs visibility and control over AI systems in production, and the origin story frames the pain as models that fail silently. The asset under test is the AI application, and the failure modes are degraded quality, unsafe outputs, and adversarial manipulation.
The open-source library widens the top of that segment. The free machine-learning testing tool has drawn more than 4,000 GitHub stars, an interest signal that seeds awareness inside the same organizations the commercial platform later sells to. One motion reaches both the individual developer and the enterprise team.
The segment splits along who adopts and who pays. The free tool draws model-quality engineers, while the commercial LLM Evaluation platform targets teams running production AI that need continuous monitoring and pentesting, and whether the free audience converts to paying customers is the open question the public record does not answer.
Deepchecks' claimed advantage is breadth of automated AI testing rather than a single check. The commercial platform evaluates prompts, models, and agents, runs an LLM pentesting environment that fires adversarial prompts including prompt injection, jailbreaks, PII extraction, and bias triggers, and provides an end-to-end agent evaluation pipeline, per documentation quoted in the AI Defense Matrix Catalog.
The footprint is publicly verifiable. The open-source library carries more than 4,000 GitHub stars and gives any engineer a public, inspectable implementation of its testing logic, and the founders published an arXiv paper describing the library, a public technical account beyond the vendor's own marketing.
Its production capability is observation, not enforcement. Deepchecks monitors a deployed application over time, tracking annotation trends and property scores and detecting degradation automatically, but it tests and evaluates rather than blocking unsafe outputs in real time. The testing techniques themselves are reproducible by a funded rival.
Deepchecks runs a bottom-up open-source motion ahead of a commercial sale. The free library builds developer adoption and inbound awareness, and the company positions the commercial LLM Evaluation platform for teams that need continuous testing and monitoring. The public pages route a prospect to a free trial and a demo rather than to published prices.
Named customer evidence is the weak point. The reviewed sources show the open-source following but no named paying customer of the commercial platform, so the go-to-market rests on community adoption rather than referenceable production deployments.
The acquisition itself says the most about commercial traction. Check Point agreed to acquire the team and technology at a figure industry sources put at $10 million to $20 million, a deal the coverage describes in team-and-intellectual-property terms without mentioning a customer base.
Deepchecks does not publish prices in the reviewed pages. The homepage routes a prospect to a free trial of the LLM Evaluation platform and a demo rather than to seat or usage tiers, the posture of a vendor selling negotiated deals rather than self-serve subscriptions.
The free open-source library sets the floor that paid pricing must clear. Because the open tool already covers machine-learning validation, the commercial platform has to charge for what the library does not do, the LLM and agent evaluation, the pentesting environment, and the managed production monitoring.
The charged unit stays private. Whether Deepchecks bills by application, by evaluation volume, or by seat is not stated in the reviewed sources, which withholds the budget-anchoring signal a published-price product gives a buyer.
Deepchecks delivers as software the customer runs. A developer installs the open-source library directly, and an enterprise adopts the LLM Evaluation platform through the published deployment options, multi-tenant SaaS, a virtual private cloud, bare metal, or an AWS-managed offering through SageMaker Partner AI Apps. The managed options handle infrastructure and scaling, while the self-managed options keep data inside the customer's environment.
There is no evidence in the reviewed sources of a managed-service or human-accountability layer above the software. Deepchecks describes a platform teams configure and operate themselves, not an assessment service where the vendor's experts sign off on results, so the delivered artifact is the software.
Deployment flexibility is the operations story that fits regulated buyers. Offering SaaS alongside VPC and bare-metal options lets a team with data-privacy constraints run the evaluation platform where its AI and data already live. Published uptime or support commitments do not appear in the reviewed sources.
Deepchecks' trust argument for a product that inspects proprietary AI rests on deployment control, published compliance signals, and open code. The homepage lists SOC 2 Type 2, GDPR, and HIPAA compliance alongside SSO, access controls, data isolation, and AWS GovCloud support, and the platform's self-managed deployments keep evaluation data inside the customer's own environment.
Open-source transparency is the second pillar. The core testing library is open source, so a security team can read the testing logic directly rather than trust an opaque scanner, which addresses part of the concern a buyer raises about a tool that examines its AI systems.
The acquisition reshapes the trust question. A buyer evaluating Deepchecks is increasingly weighing Check Point's roadmap for the team and technology rather than an independent vendor's commitments, and a procurement team would resolve data-handling terms through that lens.
Deepchecks sits beside the AI workload as a testing and monitoring layer rather than infrastructure the workload depends on. It evaluates the models, prompts, and agents a customer already runs and reports on them, easy to adopt without re-architecting, and removing it means replacing testing and monitoring coverage rather than unwinding a runtime dependency.
Its ecosystem base pairs the open-source community with AWS distribution. The more than 4,000 stars on the machine-learning testing library give Deepchecks reach among developers, and the homepage advertises an AWS-managed SageMaker Partner AI App and integrations with Amazon Bedrock and SageMaker AI, though the reviewed sources show no exclusive partnership that would lock it into a stack.
The acquisition redirects the ecosystem question. Check Point intends to fold the team and technology into its own agentic network-security work, so Deepchecks' most consequential platform relationship going forward is with its acquirer rather than with the AI platforms its product tests.
Deepchecks was founded in 2019 by CEO Philip Tannor and CTO Shir Chorev, machine-learning practitioners who describe having led research groups before building the company. The team's public reputation rests on its testing library and the research behind it.
The research and open-source record is the team's strongest verifiable signal. The company maintains a machine-learning validation library with more than 4,000 GitHub stars, its CTO co-authored an arXiv paper on it, and Check Point's agreement to buy the team signals that a major security vendor valued that engineering capability.
The reviewed sources name no earlier founder exit, and the pending Check Point deal is a modest team-and-technology transaction, so the team's credibility is grounded in its research and testing work rather than a history of scaled outcomes.
| Id | Source | Tier | Accessed |
|---|---|---|---|
| f1 | Deepchecks: LLM Evaluation, Evaluate AI Progress with Know Your Agent | official | 2026-07-09 |
| f2 | CTech on Deepchecks funding and founding | press | 2026-07-06 |
| f3 | AI Defense Matrix Catalog mapping (aligned to catalog) | other | 2026-07-06 |
| Id | Source | Tier | Accessed |
|---|---|---|---|
| s1 | Deepchecks LLM Evaluation homepage “Deepchecks LLM Evaluation is an enterprise-grade AI testing, observability and monitoring platform that provides visibility, control, and trust across AI systems in production.” | official | 2026-07-06 |
| s2 | Deepchecks LLM pentesting capability (AI Defense Matrix Catalog quoting the vendor docs) “Test your LLM application against a broad set of adversarial prompts - prompt injections, jailbreaks, PII extraction, bias triggers - and analyze resilience.” | other | 2026-07-06 |
| s3 | Deepchecks production monitoring capability (AI Defense Matrix Catalog quoting the vendor docs) “Deepchecks can monitor its quality in production over time - tracking annotation trends, property scores, and detecting degradation automatically.” | other | 2026-07-06 |
| s4 | Deepchecks open-source ML validation library on GitHub “Deepchecks is a holistic open-source solution for all of your AI & ML validation needs, enabling to thoroughly test your data and models from research to production.” | official | 2026-07-06 |
| s5 | GitHub API statistics for deepchecks/deepchecks “"stargazers_count":4032, "forks_count":300” | other | 2026-07-06 |
| s6 | Deepchecks: A Library for Testing and Validating Machine Learning Models and Data (arXiv preprint, Deepchecks CTO Shir Chorev a co-author) “Deepchecks: A Library for Testing and Validating Machine Learning Models and Data, by Shir Chorev and 9 other authors” | research | 2026-07-06 |
| s7 | CTech on Deepchecks founding, funding, and founders “According to PitchBook, Deepchecks has raised $14 million since its founding in 2019 from investors including Alpha Wave Ventures, Hetz Ventures, and Grove Ventures. The company was founded by CEO Philip Tannor and CTO Shir Chorev.” | press | 2026-07-06 |
| s8 | CTech on the Check Point acquisition value and count “is estimated by industry sources at between $10 million and $20 million, although the companies did not disclose financial terms. It marks Check Point's fourth acquisition of an Israeli cybersecurity startup this year” | press | 2026-07-06 |
| s9 | CTech on Check Point's plans for the Deepchecks team and technology “Check Point said Deepchecks' technology and engineering team would help accelerate development of autonomous AI agents designed to manage enterprise network security operations with minimal human intervention.” | press | 2026-07-06 |
| s10 | Deepchecks about page and origin story “Deepchecks was founded by a group of geeks, that lived and breathed machine learning before it was a thing.” | official | 2026-07-06 |
| Id | Source | Tier | Accessed |
|---|---|---|---|
| s1 | Deepchecks LLM Evaluation homepage “Deepchecks LLM Evaluation is an enterprise-grade AI testing, observability and monitoring platform that provides visibility, control, and trust across AI systems in production.” | official | 2026-07-06 |
| s2 | Deepchecks LLM pentesting capability (AI Defense Matrix Catalog quoting the vendor docs) “Test your LLM application against a broad set of adversarial prompts - prompt injections, jailbreaks, PII extraction, bias triggers - and analyze resilience.” | other | 2026-07-06 |
| s3 | Deepchecks production monitoring capability (AI Defense Matrix Catalog quoting the vendor docs) “Deepchecks can monitor its quality in production over time - tracking annotation trends, property scores, and detecting degradation automatically.” | other | 2026-07-06 |
| s4 | Deepchecks open-source ML validation library on GitHub “Deepchecks is a holistic open-source solution for all of your AI & ML validation needs, enabling to thoroughly test your data and models from research to production.” | official | 2026-07-06 |
| s5 | GitHub API statistics for deepchecks/deepchecks “"stargazers_count":4032, "forks_count":300” | other | 2026-07-06 |
| s6 | Deepchecks: A Library for Testing and Validating Machine Learning Models and Data (arXiv preprint, Deepchecks CTO Shir Chorev a co-author) “Deepchecks: A Library for Testing and Validating Machine Learning Models and Data, by Shir Chorev and 9 other authors” | research | 2026-07-06 |
| s7 | CTech on Deepchecks founding, funding, and founders “According to PitchBook, Deepchecks has raised $14 million since its founding in 2019 from investors including Alpha Wave Ventures, Hetz Ventures, and Grove Ventures. The company was founded by CEO Philip Tannor and CTO Shir Chorev.” | press | 2026-07-06 |
| s8 | CTech on the Check Point acquisition value and count “is estimated by industry sources at between $10 million and $20 million, although the companies did not disclose financial terms. It marks Check Point's fourth acquisition of an Israeli cybersecurity startup this year” | press | 2026-07-06 |
| s9 | CTech on Check Point's plans for the Deepchecks team and technology “Check Point said Deepchecks' technology and engineering team would help accelerate development of autonomous AI agents designed to manage enterprise network security operations with minimal human intervention.” | press | 2026-07-06 |
| s10 | Deepchecks about page and origin story “Deepchecks was founded by a group of geeks, that lived and breathed machine learning before it was a thing.” | official | 2026-07-06 |
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