Agent definition
Define how your agents think, speak, and act.
Platform Overview
A high-level look at the platform foundation behind agents, agentic tools, and voice AI.
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It all starts with Agents that take action
Omni channel messaging
Build an agent once, deploy across WhatsApp, SMS, email, Slack, and voice, with human-like voices for 30+ languages, low latency, and proprietary transcription accuracy.
Complex workflow logic
When predictability is not optional, use deterministic logic to govern exactly what your agents do, when, and how.
Agents collect and learn from Context
Agents as a key data source
Automatically extract, classify, and map agent interactions into a structured context layer that powers complex decisions and deeper operational understanding.
Contact Intelligence
Know every contact before they speak - and let every interaction make your agents smarter.
Integrations to your existing systems
Leverage 200+ native integrations to integrate applications your business already runs on.
Agents are evaluated against benchmarks
Behavioral northstars
Define what good looks like precisely and measurably so every agent is held to the same standard.
Pre-deployment testing
Test agent behavior exhaustively across planned scenarios, real production failures, and adversarial attacks before deploying.
In-production audits
Automatically sample and audit everything in production so issues are caught without reviewing every conversation.
Continuous improvement loop
Improve every future evaluation automatically with closed-loop feedback, observability, and A/B testing built in.
Humans interact with agents through Interfaces
Visualize insights generated from agents
Purpose-built operational interfaces turn agent interactions into real-time visibility, action, and control for your teams.
Interfaces for any use case
Build interfaces for any use case including customer support, logistics operations, escalation management, and more, without external tooling.
Manage agents directly via custom interfaces
Configure, monitor, and control your AI agents from a single environment - tailored to how each team actually works.
Enterprise Deployments
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Cloud-native infrastructure built for enterprise scale
Kubernetes-based microservices across AWS, GCP, and Azure, with horizontal autoscaling, single-tenant deployment options, automatic AI model failover, and voice traffic segmented from application load for consistent latency at scale.
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You choose how you deploy
HappyRobot builds your solution end to end while you co-own design and sign-off, or your engineers lead the build with full Forward Deployed Engineer (FDE) support on call and structured onsite training to ramp the team.
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Established process to production
Deployments follow a consistent sequence: discovery and solution design, implementation, testing, and go-live - with both teams co-owning key decisions and sign-off at every stage.
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Scale new markets on what's already built
Every go-live includes a post-deployment care period with FDEs fully on call. The same team then scales every new market, with each region inheriting quality protections from every prior deployment.