AmplifAI pricing isn’t published on its website, and all plans require a custom quote through a demo.

Before booking that call, here’s what each product module includes, what drives the cost, and how to decide if it fits your team’s budget and use case.

What you get with AmplifAI

AmplifAI is sold as a platform with 81 features across 7 integrated modules. Teams can license specific modules or the full platform, and AmplifAI pricing is configured based on team size, data sources, and feature requirements.

AmplifAI pricing breakdown by module

1. Data integration

What's included: Connects to 150+ cloud APIs, on-premise systems, CRM platforms, WFM tools, conversational intelligence systems, and internal data sources. Includes organization and roster sync, flexible scorecard management, and dedicated customer success during onboarding.

Best for: Enterprise contact centers that need all performance, QA, and CX data unified into one system before any AI-driven actions can run.

Pros:

✅Agnostic integration approach works with virtually any existing stack

✅On-premise system support for teams with behind-the-firewall deployments

✅Dedicated customer success team handles the data mapping during setup

Cons:

❌Teams with simpler data environments may find the integration depth more than they need

❌Configuration requires onboarding investment before the platform delivers full value

2. Contact center AI

What's included: High-performer persona modeling that identifies top behaviors and builds them into a replicable framework. AI-driven next best actions for agents, supervisors, QA leaders, and directors. Scales winning behaviors across the team rather than relying on manual identification.

Best for: Large contact centers where the gap between top performers and the rest is a known revenue problem, and managers need AI to identify what to replicate at scale.

Pros:

✅Learns from your actual high performers rather than applying generic benchmarks

✅Next best actions are role-specific, so agents, team leads, and directors each see relevant recommendations

✅Runs in the background without requiring manual configuration for each action

Cons:

❌Requires enough performance data to build reliable high-performer personas before recommendations become accurate

❌Enterprise focus means smaller teams may not see the full value of the AI modeling layer

3. CX intelligence

What's included: Customer intent and call-reason analysis, predictive NPS, sentiment and root-cause analysis, survey commentary analysis, customer journey insights, conversational intelligence, and a natural language interface for querying call transcripts and CX metrics on demand.

Best for: Teams that need to connect what happens in conversations to broader CX outcomes and surface which call reasons, behaviors, or agent patterns drive the highest and lowest satisfaction scores.

Pros:

✅LLM-powered insights engine connects dots across QA, surveys, sentiment, and call reasons automatically

✅Natural language querying lets leaders ask questions about their data without running reports manually

✅Compliance monitoring detects risky behaviors across calls, chats, and transcripts without manual review

Cons:

❌CX intelligence is most valuable when paired with other AmplifAI modules. Standalone use limits the action layer

❌Teams focused on sales conversion rather than satisfaction metrics will find some features less directly relevant

4. Automated QA and quality management

What's included: Includes auto QA scoring across all interactions, customizable evaluation forms, calibration workflows, auto-fail triggers tied to coaching, QA-based recognition, compliance monitoring, and a unified layer connecting QA to CRM, CCaaS, and survey data.

AmplifAI was named a Leading Automated QA/QM Provider in the 2026 CMP Research Prism report.

Best for: Contact centers that currently manually score only a small fraction of calls and need full coverage without hiring additional QA analysts.

Pros:

✅Scores 100% of interactions across voice, chat, email, and AI agent interactions

✅Auto-fail triggers automatically connect compliance misses to coaching actions without manual routing

✅Multiple custom evaluation forms support different scoring frameworks across support, sales, and collections teams

Cons:

❌Teams that already have a mature manual QA program will need to invest time in migrating forms and scoring logic

❌Some advanced features, like calibration workflows and 360-degree reporting, require the broader platform to deliver full value

5. Performance management

What's included: Includes real-time performance views by role, weighted live scorecards, role-specific dashboards across teams, and synced metrics that connect KPIs to rep behavior.

Best for: Large-scale operations where managers at different levels need different views of the same performance data without manually building custom reports.

Pros:

✅Role-specific dashboards mean agents see their own performance while directors see team and org-level trends

✅Scorecards adapt as business priorities change rather than requiring manual reconfiguration

✅Connects directly to coaching and QA modules, so performance data triggers actions rather than sitting in reports

Cons:

❌Full value requires the performance module to be connected to coaching, QA, and data integration layers

❌Setup requires time investment in scorecard configuration to reflect your specific KPIs and weightings

6. AI-enabled coaching

What's included: Includes AI-driven next-best coaching actions, coaching effectiveness scoring, automated follow-ups and recognition, AI summaries, custom coaching forms, coach-the-coach actions, behavior-based coaching tied to outcomes, and AutoQA workflows that turn QA into real-time coaching.

Best for: Contact center managers who need to coach 10 to 15 agents each and currently spend significant time compiling data before sessions rather than having actual coaching conversations.

Pros:

✅Coaching Effectiveness Index shows which coaches are driving improvement and which need support themselves

✅Behavior-based coaching targets skills tied to outcomes rather than surface-level metric changes

✅AutoQA coaching workflow converts every QA result into a coaching opportunity automatically at scale

Cons:

❌Teams without a defined coaching cadence may struggle to adopt the platform's structured coaching workflows

❌Industry estimates on effectiveness improvements vary by team size and baseline coaching maturity

Gamification and engagement

What's included: Includes AI-driven recognition tied to performance gains, KPI-based games, real-time leaderboards, incentive tracking and rewards integrations, cohort segmentation by performance, and in-game peer recognition.

Best for: Contact centers that want recognition and competition tied directly to QA scores and coaching milestones rather than standalone leaderboards disconnected from performance data.

Pros:

✅Gen AI Recognition triggers automatically when agents improve after coaching, so high performers and improving reps both get acknowledged

✅Data-powered games reflect real performance rather than activity volume

✅Cohort-based competition gives every performance tier a fair chance to win, rather than the same agents always topping the leaderboard

Cons:

❌Teams looking for standalone gamification without QA or coaching integration will find the module most useful as part of the broader platform

❌Full value depends on the coaching and QA modules being in place to generate the recognition triggers

Which AmplifAI module should you prioritize?

Start with automated QA if you:

• Are currently manually reviewing less than 10% of calls

• Need compliance monitoring across every interaction in a regulated industry

• Want to connect QA results directly to coaching actions without manual routing

Start with AI-enabled coaching if you:

• Have managers spending more time preparing for coaching sessions than running them

• Need visibility into whether coaching is actually driving performance improvement

• Want coaching actions tied to behavior, not just metric movement

Start with the full platform if you:

• Need a unified data layer that connects QA, coaching, performance, and CX intelligence

• Operate at enterprise scale across multiple teams, channels, or BPO partners

• Want next best actions that run across every role without manual configuration

Is AmplifAI worth the cost?

AmplifAI is worth evaluating for large contact centers with fragmented data, limited QA coverage, and varying coaching quality. This is where AmplifAI pricing needs to align with operational scale.

The strongest ROI comes from moving to 100% automated QA and reducing time spent on manual coaching prep. These efficiency gains are what typically justify AmplifAI pricing for larger teams.

AmplifAI is worth it if you:

• Operate a contact center with 100+ agents where manual QA and coaching don't scale

• Need a unified platform that connects performance, QA, coaching, and recognition without stitching together separate tools

• Are in a regulated industry like healthcare or financial services, where compliance monitoring needs to run at scale

Consider alternatives if you:

• Need published pricing before engaging with a sales process

• Are primarily focused on sales call performance and rep coaching rather than full contact center QA and CX intelligence

• Have a smaller team where a purpose-built sales coaching tool delivers more targeted value at lower complexity

AmplifAI alternatives and pricing comparison

Alpharun vs. AmplifAI: Which should you choose?

Both platforms analyze call data and coach reps based on what's actually happening in conversations. The difference is in scope and focus.

AmplifAI is built for full contact center management, with a broad platform designed to support multiple teams and functions across the operation.

AmplifAI focuses on:

• QA, coaching, performance management, and CX intelligence in one platform

• Gamification and engagement across teams

• Support, sales, and collections use cases

• Managing fragmented data across systems

• Role-based dashboards for different teams and leadership levels

Alpharun is built for high-volume B2C sales teams, with a narrower focus on improving call performance and coaching at scale.

Alpharun focuses on:

• Analyzing real calls to identify top performer behavior

• Building custom playbooks from actual conversations

• Real-time coaching during live calls

• Native integrations with Five9 and Genesys

• Fast onboarding with low implementation overhead

Bottom line on AmplifAI pricing

AmplifAI is a serious enterprise platform, and the pricing reflects that. Without published rates, the only way to know what it costs for your team is to go through their demo process.

That's worth doing if you're running a large contact center where fragmented data, limited QA coverage, and inconsistent coaching are real problems.

For teams focused on sales reps' call performance, the platform’s breadth may be more than needed. A purpose-built sales coaching tool with faster onboarding and clearer pricing delivers more targeted value.

Better calls lead to better outcomes. Alpharun shows you exactly how to get there. Book a demo to see how it drives performance.

Frequently asked questions

How much does AmplifAI cost?

AmplifAI pricing isn’t publicly available and requires a custom quote through a demo. Costs vary based on team size, selected modules, data complexity, and contract terms.

Does AmplifAI offer a free trial?

No, AmplifAI doesn’t advertise a free trial on its website. Access is typically provided through a demo or product tour, though you can request trial options from their team.

What is included in AmplifAI’s platform?

AmplifAI includes data integration, contact center AI, CX intelligence, automated QA, performance management, AI coaching, and gamification. Teams can choose individual modules or the full platform.

Who uses AmplifAI?

AmplifAI is used by teams across retail, financial services, healthcare, BPOs, and more. Customers include companies like Cox Communications, GoDaddy, and The Home Depot.

Is AmplifAI good for small call centers?

No, AmplifAI is designed for mid- to large-sized contact centers. Smaller teams may find the platform too complex and may benefit more from a focused solution.