Customer signals change faster than most teams can interpret and act on them. The answer is often buried across dashboards, analyst queues, and handoffs.
Today, we’re introducing Pér, Amperity’s customer data agent. Pér helps teams understand what is changing, decide what matters, and move an approved next step forward. It brings more people into the customer data workflow without asking them to think in tables, fields, or query languages.
Pér is built on Amperity’s trusted customer context. Where available and configured, its answers and recommendations can draw on identity-resolved profiles, customer history, current signals, predictive insights, permissions, and business context.
Key Takeaways
Pér helps teams explore customer data, investigate performance, and uncover opportunities using natural language.
Pér is grounded in trusted customer context rather than relying on a general-purpose AI model alone.
In supported, configured workflows, Pér can prepare saved queries, audiences, recommendations, and Amperity Journeys for people to review.
People remain in control of approval and execution. A proposal from Pér is not a live activation.
Meet Pér
Pér is a customer data agent built on Amperity’s trusted, governed customer context. It helps people understand what is happening across their customer data, decide what matters, and move approved recommendations toward action.
Pér is designed for decisions that span teams. Marketing and business leaders can investigate performance and customer behavior. Data and technology teams can inspect underlying information. Operations and customer-facing teams can explore questions relevant to their work.
Instead of starting with a report or technical request, a user can start with a question.
Understand what changed and why
Imagine that a campaign, customer segment, or product category is performing differently than expected. Finding the reason may require several dashboards, an analyst request, and another round of questions once the first report arrives.
With Pér, a user can ask the question directly. Pér can explore the customer data and predictive outputs available to that user and, where supported, present findings through explanations, tables, charts, or audience views.
A marketing leader might ask why repeat purchases declined within a particular audience. An analyst might investigate which customer attributes distinguish the affected group. A retention team might explore whether the change is concentrated among high-value customers or tied to a particular behavior.
Each question can lead to the next one, helping teams move from a broad performance change to the customer-level context behind it.
Turn insight into a decision
Understanding what happened is only part of the job. Teams also need to decide what to do next.
Pér can propose a reviewable next step, such as an audience or other supported customer-data action. For example, it could surface a group of valuable customers showing signs of disengagement, summarize why the audience matters, and prepare a recommendation for reaching them.
Those recommendations can draw from Amperity’s Intelligence and Decisioning capabilities, including customer attributes and predictive-model outputs. Pér can also create saved queries so teams can inspect, refine, and reuse the logic behind an analysis.
The goal is not to remove people from the decision. Pér helps teams get to a reviewable proposal faster, with the underlying customer context available for inspection.
Move approved ideas into Amperity Journeys
Pér can also help translate an idea into an Amperity Journey.
When enabled and configured, Pér can use the Amperity MCP server to create and persist a Journey, return its status, and provide a link to the Journey in Amperity. The user can then inspect the proposed configuration and continue through the appropriate approval and execution workflow.
This connection closes an important gap between asking a question and putting the answer to work. A recommendation no longer has to end as a chart, document, or handoff. It can become a structured, reviewable object inside Amperity.
A proposal is not a live activation. Pér prepares the work, while people retain control over approval and execution. Teams can learn more about the underlying workflow in the Amperity Journeys documentation.
AI is only as useful as the context behind it
A general-purpose AI assistant may be able to explain a marketing concept, but it does not automatically understand your customers, business rules, permissions, or goals.
Pér works from the context available through Amperity. Depending on what a customer has configured and a user is permitted to access, that foundation can include:
Customer records connected through identity resolution
Historical purchases, engagement, loyalty, and service activity
Current behavioral signals and customer attributes
Predictive-model outputs
Tenant-specific business context
Permissions, memory, and guardrails
Together, these elements create trusted customer context: a more complete, current, and governed understanding of the customer. Pér can use that context to help answer three connected questions: Who is this customer? What is happening now? What should the brand consider doing next?
No single data point provides the whole answer. A current signal without resolved identity can be connected to the wrong person. A prediction without customer history can lack necessary context. A recommendation without permissions or governance may not be appropriate to use.
Amperity’s unified customer profiles bring these elements together so people and AI can work from a shared understanding of the customer.
Work through a governed experience
Customer decisions rarely happen in one application. People ask questions, share findings, and coordinate work through the tools they already use.
During the preview, Pér’s direct web experience provides the primary place to explore customer data and prepare reviewable actions. Available capabilities depend on each customer’s configuration, permissions, and preview eligibility.
Pér is designed to work with supported lakehouse environments as integrations are validated. This approach helps organizations make customer context usable while maintaining the data architecture and governance model they already operate.
For teams that want to see how natural-language customer-data workflows work today, the Amperity Demo Center includes demonstrations of AI-assisted analysis, segmentation, identity resolution, and Journeys.
Pér in controlled availability
Pér debuts in controlled availability today.
This preview gives Amperity an opportunity to develop Pér alongside organizations applying it to real customer and business questions. Availability will expand as customer usage and validation progress.
A new way to work with customer data
Customer data should do more than populate profiles and dashboards. It should help people understand what is changing, decide what deserves attention, and take the next appropriate step.
Pér makes that process more accessible. It brings a conversational experience to trusted customer context, helps teams investigate what matters, and turns insight into work people can review and move forward.
Pér is the customer data agent that helps your team understand what is changing, decide what matters, and move an approved next step forward, with people in control.
Interested? Meet Pér.