Ami vs QualGent: Features, Pricing & Which Is Better (2026)

A side-by-side comparison of Ami and QualGent — features, pricing, and ideal use cases — to help you decide which AI tool fits your workflow.

Ami

AiSDR

Paid

AI GTM agent that picks the audience, writes and launches outbound campaigns, reads the results and fixes what stops working.

Autonomous Campaign Loop: Ami picks the target audience, builds and launches the campaign, reads what comes back and changes what is not working, so each campaign sharpens the next without a human restarting the cycle.
Baked-In GTM Experience: Arrives with 27 industry playbooks and the lessons of 17,150 prior AiSDR campaigns and 19,501 meetings, so a first campaign launches with patterns other teams paid to learn.
Signal-Triggered Outreach: Watches hiring, funding and job-change signals and acts at the moment they happen rather than months later.
Performance Triage: When response rates slip, Ami digs into audience, message and sequence to pinpoint what is breaking and proposes fixes before the budget is spent — flagging, for example, a positive response rate under 1% after 21+ days.
Omnichannel Sequences: Configurable sequences combining email via Gmail or Outlook, LinkedIn connection requests, DMs and InMail, and AI call steps through the Aircall dialer with scripts and automated follow-ups.
Deep Per-Lead Personalization: Researches the top three most relevant data points per lead and personalizes from ICP data, activity, LinkedIn data and HubSpot properties.
Native CRM Sync: Two-way HubSpot sync on every plan and two-way Salesforce sync on higher tiers, with AI research and monitoring running over that CRM data.
Review Mode: Campaigns and Ami's proposed corrections stay drafts until approved, so the agent's autonomy is opt-in rather than assumed.

Founder-Led Outbound: A solo founder builds pipeline without hiring an SDR, starting self-serve with no sales call required.
Rescuing Stalled Campaigns: A revenue team catches a dying sequence early when Ami flags a collapsing positive-response rate and rewrites the audience or message.
Replacing Outbound Agencies: A company that has paid outside firms without results brings the motion in-house under one agent.
Warm-Signal Prospecting: A sales team reaches buyers right after a funding round, a relevant hire or a job change instead of cold-listing an industry.
CRM-Grounded Targeting: A HubSpot or Salesforce team has outreach built from and logged back into existing CRM data rather than a disconnected tool.
Multichannel Follow-Up: A team runs email, LinkedIn and dialer touches in a single sequence with replies handled in 5-10 minutes or in co-pilot mode.

Freemium

Mobile-native AI QA agent that autonomously tests iOS and Android apps, mimicking human testers to find bugs and scale QA instantly.

Human-like Mobile Testing: AI agents mimic real human testers to navigate app UIs, interact with elements, and discover functional and UX bugs without hand-written test scripts.
Cross-Platform Coverage: Supports automated testing of both iOS and Android applications, enabling consistent QA across mobile platforms.
Always-On Execution: Agents run 24/7 to continuously exercise app flows and return results in minutes, reducing the time between code changes and test feedback.
Massive Horizontal Scaling: Infrastructure-style scaling that allows teams to provision from a single agent up to thousands (advertised scale from 1 to 10,000 agents) to increase parallel test coverage.
Scriptless UI Understanding: The AI interprets and reasons about app UI structure and behaviors, eliminating the need to maintain manual scripted test cases for many scenarios.
Rapid Results and Reporting: Designed to surface issues quickly so teams can act on bugs during development cycles rather than waiting for lengthy manual test runs.
Mobile-native AI QA agents that mimic real human testers
Automated testing for iOS and Android apps without manual test scripts
UI understanding to interact with app screens and workflows
24/7 testing with rapid results (minutes, not weeks)
Elastic scaling of agents (advertised from 1 to 10,000 agents)
Comprehensive testing across app features and regressions

Pre-release Regression Testing: Run continuous automated regression suites across iOS and Android builds to catch regressions minutes after changes are merged.
Scaling QA Coverage Without Headcount: Expand testing capacity across devices and configurations instantly without hiring additional manual QA testers.
Shortening Release Cycles: Provide fast, always-on feedback to developers so bugs are discovered and fixed earlier, enabling more frequent releases.
Exploratory UI Testing: Use human-like agents to explore complex UI flows and find edge-case bugs that are costly to write manual scripts for.
Nightly or Continuous Smoke Tests: Execute rapid smoke tests around the clock to ensure core functionality remains intact between development iterations.
High-Parallel Device Testing: Run large numbers of parallel test sessions to validate app behavior across many device models and OS versions simultaneously.
Automated regression and functional testing for mobile apps
Continuous integration / continuous delivery (CI/CD) mobile test automation
Exploratory and end-to-end testing that mimics human behavior
Scaling QA capacity during major releases without hiring testers
Rapid pre-release sanity checks to catch critical bugs