Amperity for Analysts

Customer analytics built on resolved identity

Q: What is a customer analytics platform?

A customer analytics platform helps teams analyze customer behavior, value, and engagement. Amperity, the AI-powered Customer Data Platform, resolves connected records into profiles that analysts can query and share with supported tools. Your team defines the metrics and evaluates the resulting insights.

Q: How does Amperity complement dbt and our existing data warehouse?

Your warehouse and dbt models remain part of your data architecture. Amperity adds customer identity resolution, profile-building tools, and connections for sharing resolved data. Your team decides which transformations and business definitions belong in each system.

Q: Do we have to move our customer data into Amperity?

Data movement depends on your architecture and the workflows you configure. Amperity Bridge supports table sharing with Snowflake, Databricks, and Google BigQuery. Evaluate the storage, processing, sharing direction, and regional requirements for your implementation rather than assuming that every workflow is zero-copy.

Q: How does Amperity handle governance and auditability?

Stitch Scores and the Data Explorer help your team inspect matching evidence and contributing records. User policies and PII restrictions control access, while activity logs record supported user actions for review.

Q: Can AI agents and assistants use this customer data?

Yes. When configured, clients such as ChatGPT and Claude connect through the hosted Amperity MCP Server. Access and actions are limited to supported MCP tools and the signed-in user's Amperity permissions. Your organization chooses the AI client and confirms that it is approved to handle the returned data. The in-product AI Assistant is a separate Amperity experience.

Q: How long does implementation take?

Timing depends on your sources, data readiness, architecture, and target workflows. Start with a customer analytics use case so your team and Amperity can identify the integrations, configuration, and validation it requires.

Spend less time reconciling customer records and more time answering business questions. Amperity brings connected data into resolved customer profiles, giving analysts a shared foundation for analysis and tools to inspect how records were matched.

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Be ready for any question that comes your team’s way

Can this AI agent trust the customer context?

Today, the honest answer depends on which pipeline fed it, and which join logic resolved identity along the way. With Amperity, the same governed profile feeds every model, agent, and assistant, with lineage your team can show on demand. The question stops being "can we trust this?" and starts being "what should we build next?"

Can we explain how this profile was created?

Most teams can answer partially, after an investigation that pulls people off the AI roadmap. Stitch Scores trace every match category back to the data that produced it, so the question is answerable in minutes instead of weeks, by anyone who needs to know, without a forensic exercise across pipelines.

Do we need another custom pipeline for this use case?

Almost always, today. Every new request generates a new identity join, a new transformation, a new thing the team maintains. With one resolved foundation consumed across every workflow, new use cases inherit what's already there. The backlog stops growing faster than the team.

Is the same customer addressable in marketing, analytics, and AI?

Today, three systems often mean three different IDs for the same person, and three different versions of what they did last. Amperity resolves identity once, upstream of every tool, so marketing, analytics, and AI act on one customer instead of three approximations.

No model, dashboard, or agent is better than  the customer data underneath it.

Teams keep rebuilding joins, reconciling definitions, and debating who the customer is, instead of delivering trusted data for the decisions and AI initiatives the business is waiting on.

One governed customer identity foundation, in the stack you already run

Inspectable identity

Review matching evidence in Stitch Scores and the Data Explorer's cluster graphs. See how records relate before using the resulting profiles in analysis.

Identity Resolution

Connected to your data stack

Reusable customer profiles

Self-service customer exploration

Window showing Amperity stitch matching two different records. A score analysis section shows the impact of each factor on the overall match.

What changes when the foundation holds

Give teams a shared customer foundation they can inspect, query, and put to work across connected systems.

Connect records across sources

Bring connected CRM, point-of-sale, ecommerce, loyalty, and app records into customer profiles. Inspect contributing records and matching evidence to understand the relationships.

Ground AI in available customer data

Give AI workflows access to relevant customer profiles within the user's permissions. Your team can evaluate the data, business definitions, and output before deciding what to do next.

Reuse identity work for new questions

Start with resolved customer profiles rather than matching the same records for each analysis. Add the source data, transformations, and configuration a new use case requires.

Review access and activity

Use policies to control available actions and access to PII. Review recorded user activity alongside matching evidence to support governance reviews.

Technical leaders who closed the gap

Two female models wearing vest-skirt sets and sunglasses against a desert landscape.

New Look: Governed Identity

Resolved 3.4M fragmented profiles and identified 24% more high-value customers. Addressability moved because identity did.

Read the customer story

Virgin Atlantic: Composable Architecture

VA connected Databricks to Amperity with zero-copy data sharing. 2M+ customer profiles unified across booking, loyalty, and complaints in under four days. Five use cases live within 90 days.

Read the customer story

Wyndham Hotels & Resorts: AI-Ready Context

Wyndham unified profiles across 9,000 hotels, cutting data access from weeks to minutes. Business teams gained self-service segmentation. New loyalty enrollment drove $2.5M in incremental revenue and media costs dropped 35%.

Read the customer story

Seattle Seahawks: Reduced Pipeline Debt

5,000 fans that previous methods missed entirely. 61.5% deduplication rate. Manual processing across three stadium systems replaced with a Snowflake connection that took three minutes to configure.

Read the customer story

Questions technical leaders ask before they evaluate

What is a customer analytics platform?

How does Amperity complement dbt and our existing data warehouse?

Do we have to move our customer data into Amperity?

How does Amperity handle governance and auditability?

Can AI agents and assistants use this customer data?

How long does implementation take?

Connect customer context to your existing stack

See integrations

Evaluate your customer context foundation

Bring one customer analytics question and the sources your team uses to answer it. Explore how resolved profiles, data-sharing options, and access controls fit that use case, then identify the implementation it needs.

Request a demo

Customer analytics built on resolved identity

Can this AI agent trust the customer context?

Can we explain how this profile was created?

Do we need another custom pipeline for this use case?

Is the same customer addressable in marketing, analytics, and AI?