Qevlar AI

Qevlar AI is an autonomous Security Operations Center (SOC) platform that investigates every security alert the way a top analyst would — without playbooks, without hallucinations, and without adding headcount. Founded in 2023 by Ahmed Achchak and Hamza Sayah and headquartered in Paris, France, the company runs a proprietary graph-based AI orchestrator (not an LLM agent) that delivers deterministic, evidence-based investigations across the full security stack. As soon as an alert fires from SIEM/EDR tools, Qevlar autonomously pulls, enriches, and analyzes internal and external data, then returns a conclusive malicious/benign verdict, a full investigation report, and recommended remediation — typically in under three minutes, closing up to 80% of tickets automatically. The SaaS platform is SOC 2 Type 2 certified, hosted in the EU (Belgium) on GCP, and connects via API to 50+ security tools (Microsoft Sentinel, Splunk, CrowdStrike, Cortex, Google SecOps, and more). It is trusted by Fortune Global 500 enterprises and leading MSSPs/MDRs across Europe and the US. Backed by EQT Ventures and Partech.

Qevlar AI is profiled on the APIs.io network. Tagged areas include Company, Engineering Services, Security, Cybersecurity, and Security Operations.

Qevlar AI’s developer surface includes engineering blog, support, and 7 more developer resources.

🌐 Visit website 📡 Source on GitHub
15.4/100 emerging ▬ flat Agent 0/100 human only Full breakdown ↓
scored 2026-07-27 · rubric v0.5
0 APIs
CompanyEngineering ServicesSecurityCybersecuritySecurity OperationsSOCArtificial IntelligenceThreat DetectionIncident ResponseSaaS

Kin Score

Kin Score Kin Score How this is scored →
scored 2026-07-27 · rubric v0.5
Composite quality — 15.4/100 · emerging
Contract Quality 0.0 / 25
Developer Ergonomics 1.3 / 20
Commercial Clarity 7.4 / 20
Operational Transparency 0.0 / 13
Governance 0.0 / 12
Discoverability 6.8 / 10
Agent readiness — 0/100 · human only
Machine-Readable Contract 0 / 18
Agentic Access Contract 0 / 15
MCP Server 0 / 12
Machine-Readable Auth 0 / 10
Idempotency 0 / 9
Stable Error Semantics 0 / 8
Request/Response Examples 0 / 7
Rate-Limit Signaling 0 / 7
Typed Event Surface 0 / 6
Agent Skills 0 / 5
Well-Known Catalog 0 / 4
Consent & Bot Identity 0 / 3
Improve this rating by publishing the missing artifacts — every area above can be raised, and the full rubric is at apis.io/rating/. This rating is computed from github.com/api-evangelist/qevlar-ai: open an issue to ask a question, or submit a pull request to add artifacts. Want it done for you? Prioritized profiling — $2,500 →

Security Posture 2

Authentication, domain security, vulnerability disclosure, and trust-center signals.

Qevlar Ai Domain Security

TLSv1.3 · HSTS · DMARC

SECURITY

Qevlar Ai Trust Center

SOC 2 Type 2

SECURITY

Resources

Agent Surfaces 1

MCP servers, agent skills, and machine-readable catalogs

LLMsTxt
LLMsTxt

Access & Security 3

Authentication, authorization, and security posture

DomainSecurity
DomainSecurity
 TrustCenter
TrustCenter
 Compliance
Compliance

Operate 1

Status, limits, changes, and where to get help

Support
Support

Commercial 2

Pricing, plans, and the legal terms of use

TermsOfService
TermsOfService
 PrivacyPolicy
PrivacyPolicy

Company 2

The organization behind the API

Website
Website
 Blog
Blog

Source (apis.yml)

apis.yml Raw ↑ 
aid: qevlar-ai
name: Qevlar AI
description: Qevlar AI is an autonomous Security Operations Center (SOC) platform that investigates every security alert the
  way a top analyst would — without playbooks, without hallucinations, and without adding headcount. Founded in 2023 by Ahmed
  Achchak and Hamza Sayah and headquartered in Paris, France, the company runs a proprietary graph-based AI orchestrator (not
  an LLM agent) that delivers deterministic, evidence-based investigations across the full security stack. As soon as an alert
  fires from SIEM/EDR tools, Qevlar autonomously pulls, enriches, and analyzes internal and external data, then returns a
  conclusive malicious/benign verdict, a full investigation report, and recommended remediation — typically in under three
  minutes, closing up to 80% of tickets automatically. The SaaS platform is SOC 2 Type 2 certified, hosted in the EU (Belgium)
  on GCP, and connects via API to 50+ security tools (Microsoft Sentinel, Splunk, CrowdStrike, Cortex, Google SecOps, and
  more). It is trusted by Fortune Global 500 enterprises and leading MSSPs/MDRs across Europe and the US. Backed by EQT Ventures
  and Partech.
url: https://raw.githubusercontent.com/api-evangelist/qevlar-ai/refs/heads/main/apis.yml
accessModel:
  pricing: unknown
  onboarding: unknown
  trial: false
  try_now: false
  public: false
  label: Unknown
  confidence: low
  source: []
  generated: '2026-07-22'
  method: derived
image: https://cdn.prod.website-files.com/69d65680938c2879b2054ed8/6a02ea4b27b9d388ac10d9a2_Open%20graph%201200x630.jpg
x-type: company
x-source: vc-portfolio
x-backed-by:
- eqt-ventures
- partech
x-tier: stub
x-tier-reason: portfolio-lead
specificationVersion: '0.20'
created: '2026-07-17'
modified: '2026-07-20'
tags:
- Company
- Engineering Services
- Security
- Cybersecurity
- Security Operations
- SOC
- Artificial Intelligence
- Threat Detection
- Incident Response
- SaaS
apis: []
maintainers:
- FN: Kin Lane
  email: kin@apievangelist.com
- FN: APIs.json
  email: info@apis.io
common:
- type: Website
  url: https://www.qevlar.com/
- type: Blog
  url: https://www.qevlar.com/post
- type: Support
  url: https://www.qevlar.com/faq
- type: TermsOfService
  url: https://www.qevlar.com/term-of-use
- type: PrivacyPolicy
  url: https://www.qevlar.com/privacy-policy
- type: LLMsTxt
  url: llms/qevlar-ai-llms.txt
- type: DomainSecurity
  url: security/qevlar-ai-domain-security.yml
- type: TrustCenter
  url: security/qevlar-ai-trust-center.yml
- type: Compliance
  url: security/qevlar-ai-trust-center.yml
x-enrichment:
  date: '2026-07-20'
  status: enriched
  artifacts_added: 3
  pass: local-v1