Ace Waves provides enterprise-ready AI agents that automate up to 80% of customer support operations. The platform combines a multi-agent orchestration engine called Agent Procedures with strict guardrails and a supervisor layer to prevent hallucinations from becoming real actions. Forward-deployed engineering squads work directly with clients to structure workflows and integrate agents into existing tech stacks, with customers like Eneba cutting support costs by 60%.
Funding
Funding not disclosed
Founders
Product
Problem
Most customer service operations lack the structured procedures, documented policies, and clean system integrations needed for reliable AI execution. This "Customer Service Debt" — fragmented logic scattered across docs, tribal knowledge, and inconsistent workflows — causes AI agents to hallucinate, improvise, and make costly errors when deployed without addressing these foundational gaps.
Solution
Ace Waves provides enterprise-ready AI agents that automate up to 80% of customer support by combining forward-deployed engineering with a proprietary multi-agent orchestration engine called Agent Procedures. The platform translates ambiguous human workflows into deterministic logic that AI agents can execute reliably, integrating directly with each client's existing tech stack and business systems. A guardrails layer validates every action and blocks anything outside the defined workflow, including prompt injection attempts, while a supervisor layer escalates to humans when confidence drops. This architecture ensures AI agents follow actual business procedures rather than model guesses, enabling accurate, multilingual, 24/7 support across channels.
Target Audience
Primary customers are large B2C companies across Europe and the US with high-volume customer support operations, including e-commerce platforms, consumer brands, and hypergrowth startups that need to scale support without proportional headcount growth.
Features
- Agent Procedures orchestration engine that defines exact actions, timing, and permitted tools for each AI agent
- Multi-layer guardrails that validate every action and block out-of-workflow behavior, including prompt injection attempts
- Supervisor layer that monitors agent performance and escalates to human agents when confidence drops
- Forward-deployed engineering squads that structure workflows, define edge cases, and integrate with legacy systems
- Real-time accuracy across channels with custom-built agents for each client's workflows and tech stack
- Production testing and iteration process that ensures agents handle edge cases and move business metrics