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 |
|---|---|
| Problem Clarity How precisely the company defines its problem, with evidence the problem exists at the scale claimed. | 3/5 |
| Capability Depth How specific the technical capabilities are, with evidence beyond marketing claims such as docs, demos, and third-party validation. | 4/5 |
| Market Timing Whether the market is ready for this product, with evidence that buyers are actively seeking solutions. | 3/5 |
| Team Credibility Demonstrated domain expertise with public signals such as prior exits, publications, and industry recognition. | 3/5 |
| GTM Proof Evidence of actual traction (customers, revenue signals, partnerships) beyond stated intentions. | 2/5 |
| Funding Efficiency Whether funding matches go-to-market ambition, with signs of capital-efficient growth. | 2/5 |
| Category Clarity Whether the company creates or fits a recognizable category that buyers can quickly place in their stack. | 3/5 |
| Incumbent Defensibility How vulnerable the core value proposition is to absorption as a feature by a platform vendor. | 3/5 |
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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
| Dimension | Score |
|---|---|
| Value Delivery Does the product sell software as the product, or judgment, trust, or accountability with software as the delivery mechanism. | 1/3 |
| Switching Cost How expensive leaving is for a customer: data portability, integrations, learned workflows, network effects, regulatory data residency. | 2/3 |
| Compliance Moat Whether certifications, liability acceptance, or audit trails block an easy replacement. | 1/3 |
| Problem Complexity Whether the product requires ML, optimization, real-time systems, or years of specialized expertise. | 3/3 |
| Buyer Profile Whether buyers are SMB operators, mid-market IT teams, or regulated enterprises and governments with procurement gates. | 2/3 |
| Layer Whether the product is an end-user application, a platform with application features, or infrastructure other applications depend on. | 2/3 |
| Proprietary Data, Content, or IP Whether the product accumulates datasets, content licenses, or IP that a rival cannot recreate from scratch. | 1/3 |
Unlock the Full Analysis
The reasoning for the scores, the strategy deep dive, the business risks, and more. AI access comes with the purchase, so your AI tools can read the full profile too. You keep 12 months of access.
One-time purchase: $20 per profile.
UnlockReading several? Unlock the entire catalog.
| 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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