Resonance Labs
AI agents are about to enter daily human life at an unprecedented scale. Every organization with a phone number will deploy an AI agent to answer it: every call center, every national helpline, every customer service line, and every outreach campaign. The character of those conversations will define the level of trust entire generations have in AI. Yet there are no dedicated labs training models for what those conversations actually demand.
We are Resonance Labs, an applied research lab teaching AI systems how to understand, talk to, and advocate for the people they're meant to help.
In 2023, Resonance Labs' founding team built the conversational and clinical intelligence for the first AI Care Manager in the country, followed by the playbook to safely scale our empathetic voice Agent in production with enterprise clients across every healthcare domain. We're researchers who have created AI Agent call centers.
Since 2024, we have seen:
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Existing frontier labs leave voice AI intelligence behind.Frontier labs have shifted to optimize for software engineering: Agent Reasoning and Coding (ARC) intelligence. As a result, every model since 2024 has regressed on conversational ability.
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Hundreds of voice AI startups each reinventing the same agent skills from scratchIdentity verification, form info collection, noise handling, intent recognition for transfer: 100 startups solving the same problems 100 different ways.
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Successful pilots stall at scale because model intelligence is the bottleneck.The sales deals close, the ROI is proven, and qualified buyers are waiting. Yet builders, whether improving performance within a single deployment or expanding across new sites, hit the same ceiling: the underlying model doesn't know how to have a conversation. Builders are forced to manually engineer the best practices that drive resolution.
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Certain use cases and features are systematically losing deal desks and getting left behind.Small clinics, low-volume outreach, women's health, pediatrics, non-English speakers. The populations the industry is walking away from are the ones that most need AI help. The technical challenges these use cases surface produce the most defensible capabilities.
Resonance Labs is a pioneering applied research lab to address 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 the critical 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:
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Understand how to talk to people.
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Advocate for the person on the other end of the interaction.
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Improve based on real-world production outcomes.
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Are built and verified for specific roles, not general use.
Most voice AI builders try to prompt or manually engineer all three.
Experience and domain knowledge are not prompt-able. The result is a brittle system and product visions that don't scale.
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, and better long-term engagement. User trust is the winning strategy. We are building for it.
At Resonance Labs, every use case that requires human connection is our market. Healthcare established the operational standard that shaped our approach. We will carry the same rigor into every domain where humans and AI meet:
We believe the same paradigm shift that transformed reinforcement learning for coding AI will transform conversational AI. This requires a lab to build:
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A model training method that converts conversations into a verifiable learning signal.
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A production-grade harness that makes those resulting systems scalable and usable for AI Agent jobs in the real world.
Coding intelligence today
SolvedMillions of developers spending the majority of their time on repetitive, structured, high-value tasks.
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.
Coding agents went from novelty to production tools generating $1B in revenue in under two years.
Conversational intelligence today
Verifier missingEvery use case that requires human connection.
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.
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.
Conversational AI has the market. What it lacks is the verifier — an equivalent of "the test passes" for conversation. We are building it.
Our unlock to transform conversations into verifiable dimensions requires 3 intentional choices:
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Bound the role. Define a measurable job definition so every agent has a knowable shape.
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Score conversations. Evaluate across rubric dimensions co-designed with the domain experts who perform and evaluate the work today — not labelers brought in after.
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Train against production outcomes. Use resolution, escalation, and satisfaction rates to turn every deployed call into a learning signal for model training.
Our founding team has shaped frontier AI for safety-critical domains. We are builders and researchers who have led efforts at OpenAI, Hippocratic AI, Luma AI, GRAIL, Tesla, AdventHealth, Florida Blue, Stanford, NYU, Cornell, and Cincinnati Children's. Together we pretrained, post-trained, and scaled the conversational and clinical intelligence behind the first AI Care Manager in the United States. Our agent improved care outcomes for millions of patients. Resonance Labs is our team's next frontier.
The people we built this vision with, and who we are building our larger founding team around.
We are also grateful for the day 0 support from the Together Fund.
If this resonates, we'd love to hear from you.
To request our full research memo or connect, reach out at contact@theresonancecompany.com