An applied research lab teaching AI systems how to understand, talk to, and advocate for the people they're meant to help.
AI agents are about to enter daily human life at a scale we have never seen. Every organization with a phone number will deploy an AI agent to answer it. Every call center, every national helpline, every customer service line, every outreach campaign. The character of those conversations will define entire industries and the next human - AI interaction frontier: the models powering them are not ready.
In 2023, our founding team built the conversational and clinical intelligence for the first AI Care Manager in the country. Then built the playbook to safely scale our empathetic voice Agent in production with enterprise clients across every healthcare domain: leading Agent go-lives in 3 days and scaling from 0 to 30k patients in 2 weeks.
Since 2024, we have watched:
We are coming together to build Resonance Labs, a dedicated applied research lab for the missing frontier intelligence every voice AI builder is betting on: conversational intelligence — the 80% every voice agent needs, so builders can focus on the 20% that wins their market.
Conversational intelligence is a third profile of AI capability — alongside deliberation (reasoning, code, math) and domain depth (clinical, legal, financial). It is the ability to do the job through human-paced dialogue, under a hard latency budget. It has its own optimization target, and no foundation lab is pointed at it.
Today's voice AI stacks treat experience as something you can prompt. You cannot. Experience is trained.
We are training that intelligence, so the next generation of AI systems:
We are building the foundational intelligence to enable the next generation of conversational AI Agents in every domain that requires human connection. An advocate makes you feel heard AND gets the job done. These goals are not in tension. They are the same.
Conversational intelligence drives the best outcomes for organizations too: higher resolution, fewer escalations, deeper trust. User trust is the winning strategy. We are building for it.
Every use case that requires human connection is our market. We've seen the standard from deploying in healthcare. We will carry the same rigor into every domain where humans and AI meet:
We believe the same reinforcement learning paradigm that transformed coding AI will transform conversational AI. This requires a lab to build (1) a model training method that converts conversations into a verifiable learning signal and (2) a production-grade harness that makes the resulting models usable and scalable for AI Agent jobs in the real world. We are that lab.
Millions of developers spending the majority of their time on repetitive, structured, high-value tasks.
Every use case that requires human connection.
Unit tests for if the code passes or not. Once DeepSeek's RLVR paper demonstrated that verifiable reward signals could drive dramatic model improvement through reinforcement learning, the path became clear. Labs could generate training data at scale by building feedback loops where correctness can be checked automatically.
Conversation has no equivalent to "the test passes". The rubric-as-verifier paradigm, proven on safety, math, science, and humanities, does not work yet on conversations. Without a model training signal that converts conversation into a reliable learning signal, reinforcement learning methods are not tractable.
Coding agents went from novelty to production tools generating $1B in revenue in under two years.
Best-in-class voice Agents are running off base models from 2024. Those that have upgraded to newer models hit a tradeoff: either resolution rates drop, or agent scope shrinks to preserve quality.
Our thesis: Conversational AI has the market. What it lacks is the verifier — an equivalent of "the test passes" for conversation. We are building it.
The unlock is three coupled choices. Bound the role to one job, so the call has a knowable shape. Decompose the call into rubric-scored dimensions, designed with the domain experts who do the work — not labelers brought in after. Train against production outcomes — resolution, escalation, post-call trust — so every deployed call retrains the model.
If this resonates, we'd love to hear from you.
To request our full research memo or connect, reach out at contact@theresonancecompany.com
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