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Veridu vs EasyCommand

A side-by-side comparison built from both listings. Blank cells mean the company has not given that detail; we never guess.

Veridu compared with EasyCommand
DetailVeridu logo Veridu EasyCommand
What it doesVerification for code your AI agent writes Fine-tuned small models for English-to-Bash command generation
StageMVP Not given
FoundedNot given Not given
Based inUS Not given
Pricing modelFreemium Open source
PricingFree: $0, 20 free reviews per month on public GitHub repos, no card required. Solo: $25/month ($20/month billed annually), includes 20 reviewed changes per month, private repo access, full reviewer catalog. Team: $25/user/month ($20 billed annually, 3 seat minimum), 20 reviewed changes per seat mont Not given
Key features
  • Change-impact analysis showing callers, implementations, and removed code references
  • Two independent reviewers (Codex and Claude) with parallel verification and reconciliation
  • Five surfaces: MCP, CLI, Web, GitHub App, API
  • MCP integration for agent verification before commit
  • GitHub App for automatic pull request reviews
  • Grounded findings with file, line, and evidence for each issue
  • Per-review model selection on paid plans
  • No repository connection required; only files from the agent are read
  • Code review artifact retention and export
  • 1.5B and 0.6B fine-tuned models
  • 401,975 synthetic training examples
  • 4-bit quantized models
  • CLI with llama.cpp integration
  • Command preview mode
  • Covers file operations, text processing, Git, archives, networking
  • Training data and checkpoints available
  • Apache 2.0 and MIT licensed
  • ALFA-updated benchmark evaluation
  • Supervised fine-tuning with LoRA
What makes it different
  • Checks code context outside the diff, not just the change itself
  • Dual independent reviewer reconciliation catches contradictions humans need to judge
  • Only grounded findings block; AI certainty is not enough
  • Does not train on customer code and never will
  • Integrates with coding agents before commit to catch issues early
  • 70.7% pass rate on ALFA-updated benchmark vs 63.7% for competing models
  • CPU-friendly quantized models
  • Specialized for bash command generation from natural language
  • Agentically trained with synthetic data generation and evaluation
Free planNot given Not given
Open sourceNot given Not given
PlatformsNot given Not given
Public APINot given Not given
Community votes00
DR (Domain Rating by Ahrefs)2 no change since last check36 no change since last check
Trust Flow (Majestic)16
Citation Flow (Majestic)2532
Referring domains (Majestic)20219

4 details filled in for both companies.

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