Zeiko vs Quiq (2026)

Zeiko

by Zeiko AI Technologies Inc.

Quiq

by Quiq

Pros

• ✓Publishes a public JSON and markdown agent catalog plus an MCP endpoint, an API, a CLI and an SDK.
• ✓Both billing rates are published for every self-serve tier, and seat overage is priced openly.
• ✓Approvals, approved-source answers and state-preserving handoff are built in rather than sold as an add-on.

Limitations

• ⚠No SOC 2, ISO 27001 or HIPAA attestation is published, and the support agent trust page names no certification.
• ⚠There is no free plan and no free trial, and the vendor states the founding package is not a trial.
• ⚠The pricing page calls Starter and Team support-first while the tier cards describe them as a Shopify-first ecommerce package.

Pros

• ✓Cross-channel agentic AI consistency with documented outcomes: same AI agent handles voice, chat, SMS, and messaging with context preservation, with a 60% automated resolution rate at Molekule and a 67% lower cost per contact at Brinks documented in customer case studies.
• ✓Explicit no-training data policy via contractual LLM agreements: Quiq contractually prohibits all LLM providers from using customer conversation data for model training, providing stronger data governance assurance than competitors with opt-out or not-disclosed policies.
• ✓Quiq AI Studio enables deep enterprise customization with guardrails: enterprises build agents with specialized knowledge bases, simulation testing, step-by-step visibility, and performance scoring before production deployment, supporting complex vertical use cases that pre-trained AI tools handle less well.

Limitations

• ⚠Custom enterprise pricing with no published rates requires a full sales process before any budget estimate is possible: makes cost comparison against Intercom Fin ($0.99 per resolution) or Decagon (custom enterprise) difficult upfront for procurement teams under time pressure.
• ⚠Implementation complexity from deep customization: AI Studio's power comes from extensive configuration, meaning deployments require meaningful investment compared to pre-trained AI agents that deploy faster with less setup, particularly for teams without dedicated AI implementation resources.
• ⚠Smaller G2 review footprint at 45 reviews despite strong enterprise customer outcomes: lower peer review volume can be a procurement concern for risk-averse buyers requiring extensive third-party validation before committing to a custom enterprise contract.