Turn agent output into customer trust and delivered outcomes.
Onsite · San Francisco Bay Area
About Solvea
We are a fully AI-native company building AI products and a GTM Agent Swarm, but AI cannot replace the final layer of human trust: the handshake, the room, the emotional read, and the physical delivery. Servicer is the real-world connector.
Why this is the moment
AI-native companies still need human trust. Offline touch, emotional value, and physical delivery are the last defensible human layer.
Agents create leverage; Servicers create belief. An agent can prepare the insight or next action. A Servicer makes it land with a real person.
Customer service is becoming outcome delivery. Solvea is moving from selling tools to selling resolved outcomes.
Field feedback becomes memory. Customer objections, edge cases, SOPs, and emotional signals should flow back into agents and improve the system.
The role
You operate where AI meets the real world. You turn agent-generated insight, messages, workflows, and recommendations into trust, customer learning, and delivered results.
What you will do
Run high-trust customer onboarding, field discovery, and follow-up.
Use in-person, voice, and relationship-based touchpoints to make AI-generated work believable and useful.
Translate agent output into customer-ready actions and delivery plans.
Capture objections, emotional signals, bad cases, SOP gaps, and delivery friction.
Feed real-world learning back into GTM agents, CS agents, memory, and playbooks.
Help customers feel that someone accountable is behind the system, not just automation.
Who you are
You create trust in real human contexts.
You can read the room, reduce anxiety, and make customers feel understood.
You follow through beyond the screen and make outcomes real.
You are comfortable working with agents, but you do not hide behind dashboards.
You can turn customer emotion and field reality into useful system feedback.
You care about delivered outcomes more than account management theater.
AIQ bar
You use AI to prepare better customer conversations, sharper follow-ups, and stronger delivery plans.
You can tell when an AI-generated answer is technically correct but will not create trust.
You feed field reality back into agents as memory, SOPs, edge cases, and better workflows.
You turn AI leverage into human outcomes: trust, adoption, retention, revenue, and real problem resolution.
Benefits
Unlimited AI tokens. Claude Code, Codex, and model usage are treated as core work infrastructure, not a rationed perk.
How we hire
Show evidence of real-world trust creation: customer saves, field work, onboarding wins, relationship-driven revenue, or moments where you made a system actually work for people. The process is agent screening, async AI interview, then founder/core team conversations.
What to send
A concrete story where an AI-generated answer, process, or workflow was not trusted by a real person, and how you made it land.
An example of field feedback you turned into a better SOP, agent memory, customer workflow, or delivery playbook.
A customer save, onboarding win, relationship-driven revenue moment, or trust repair with the human context included.
A short note on how you would use AI to prepare conversations, follow up, and feed reality back into the system.
The challenge
Describe one situation where a system, product, or process was technically correct but not trusted by customers. Explain what you did to create trust and what should be added back into the operating system.
Connect
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