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Eric Kades, Founder and CEOA sentiment agent may read the customer’s tone, a recommendation agent may suggest the next best action and a human representative may step in when judgment or empathy is needed. But when these agents work across separate systems and providers, each one sees only part of the customer’s story. As the interaction moves from one agent to another, important context can get lost.
Palera is designed to prevent that fragmentation.
Its AI Agent Workspace brings human representatives and third-party AI agents into one shared environment centered on the same customer conversation. Everyone works from a common history, so CX teams can assign the right agent to the right task without making customers repeat themselves when the agent, provider or channel changes.
Direct channel connections place every call, text, chat and AI-assisted exchange on one continuous customer timeline in real time, so any agent, human or AI, sees the full story the moment it joins.
“We partner with AI agent companies so organizations aren’t locked into one provider. Whichever agents they choose, Palera gives them one shared customer record to work from,” says Eric Kades, founder and CEO of Palera.
As the neutral system of record, Palera keeps the conversation history with the organization, not the AI provider, so switching providers means handing the new agent full context instead of starting over.
Kades saw the problem firsthand in contact centers, where legacy platforms were built around separate channels. Palera was designed as the shared memory those systems never had.
Cutting the Time to Resolution
When an AI agent begins a conversation and a human representative takes over, Palera presents a summary of what has happened. Third-party AI agents can also pass recommendations, giving the representative the issue, progress made and potential next steps without requiring the customer to explain everything again.
We partner with AI agent companies so organizations aren’t locked into one provider.
The exchange works both ways. Once the human responds, that interaction is sent back to the AI agent in real time. Kades says the approach can cut resolution time by half or more, lowering cost per contact and sparing customers the repetition that erodes satisfaction.
Turning Human Expertise into Better AI
Supervisors can monitor and grade interactions, then use strong human conversations as examples for improving AI responses.
“Look, here’s a great human conversation. This is how we want you, Mr. AI Agent, to handle your conversations going forward,” says Kades.
The result is AI that learns from an organization’s best people, improving consistency and customer satisfaction.
Keeping Intent Alive after the Call
One client was working with hundreds of thousands of prospective customer leads but reaching only 23 percent through its voice-based approach.
Palera introduced a cadence combining outbound calls with two-way texting. If a prospect did not answer, the representative could follow up by text and continue there. The contact rate increased from 23 percent to 59 percent, turning more unanswered calls into active conversations. The conversation simply moved channels with its context intact.
Making the Next AI Agent Easier to Add
Palera is building for a CX environment where the next useful AI capability may come from a provider yet to emerge.
Its AI agent marketplace is expanding to make dozens of specialized solutions available as pre-integrated options. Recommendation, sentiment-analysis and other agents can be added without rebuilding the environment.
Organizations also control where their data lives. Some AI-agent partners run open-source models on a client’s own servers, keeping sensitive conversations away from large frontier language models and simplifying compliance.
With Palera, each human or AI agent can pick up where the last one left off. Here, customers are recognized, not reintroduced, an approach that has earned Palera recognition as Top AI Agent Workspace Platform 2026.
One Workspace for Every Customer Conversation
Customer service teams can add AI agents faster than they can fix the conversation gaps those agents inherit. A bot may resolve an exchange in one channel, yet the next human still receives only part of the history. Another AI tool may sit outside the contact center record altogether. The cost appears in repeated questions, longer resolution work, duplicated effort and supervisors reconstructing what happened after the fact. An AI agent workspace should be judged less by how many assistants it can display than by whether it preserves the customer’s story while responsibility moves.
Conversation continuity begins at the data layer. Voice, SMS, live chat and social messaging arrive in different formats, often through systems designed around individual channels. A workspace that merely pulls copies of those interactions into a later reporting layer can look consolidated while the underlying records remain divided. Executive teams should look for a design that keeps conversations tied to the same customer record from the moment they occur. That limits the context an agent must recover and reduces handoff friction for the customer.
AI-to-human escalation exposes another weakness. A useful handoff is not just a transcript dump. The person taking over needs a concise account of what has already happened and enough live context to continue without restarting the exchange. The reverse path matters too. Once a human resolves or redirects a conversation, the AI agent should receive that information. A shared record can turn handoffs into a continuing exchange rather than a break between two separate service systems.
“Palera’s workspace keeps AI and human exchanges in a single customer record, while direct channel connections preserve context across voice, SMS, live chat and social messaging.”
Switching AI vendors can expose a different form of lock-in. Contact centers may want different agents for sentiment analysis, recommendations, agent coaching and other specialized functions, yet no single supplier is likely to remain the right fit for every function. Changing providers becomes harder when conversation history and learned context remain attached to the departing system. A workspace should make outside agents easy to integrate while keeping the customer record independent of any one model provider. APIs matter here, but so does control over accumulated interaction history.
Supervisors also need a way to inspect how humans and AI are handling conversations inside the same environment. Conversation grading has more value when it feeds better examples back into AI behavior instead of living only in a retrospective quality dashboard. Real-time visibility gives managers a clearer basis for intervention and retraining. For procurement teams, the stronger platform treats conversation history as a shared working context, not as data assembled after the interaction is over.
Palera emerges as a premier choice for contact centers that want an AI agent workspace without tying conversation continuity to one agent provider. Its workspace keeps AI and human exchanges in a single customer record, while direct channel connections preserve context across voice, SMS, live chat and social messaging. Human agents can receive conversation summaries and recommendations during handoffs, while AI agents receive updated context after human intervention. Each customer’s conversation history remains in Palera rather than with one AI agent vendor, enabling organizations to change providers without discarding prior records. API-based integrations also allow third-party AI agents to enter the workspace without making Palera the agent provider itself. For teams prioritizing continuity and provider flexibility, it’s a practical platform to shortlist....Read more
Company
Palera
Management
Eric Kades, Founder and CEO
Description
Palera is an AI agent workspace that unifies customer interactions across voice, chat, SMS, social media and AI channels. Its shared conversation record helps human and third-party AI agents retain context, improve responses, accelerate resolution and preserve organizational knowledge securely.
