Most retail marketing teams have more customer data than ever. But getting meaningful insight from it, the kind you can actually trust and act on, is the bigger bottleneck. 

For most teams, getting any information beyond the static reports and dashboards served up by your platforms usually spells manual spreadsheet work, tickets to your data team, or expensive data projects.

AI has started to change this. You've likely seen Model Context Protocol servers (MCPs) springing up across martech, connecting platforms to LLMs like Claude and ChatGPT, and letting marketers query their data conversationally. 

But the quality of any AI answer is only ever as good as the data going in, and this is where most MCPs fall short. 

Today, we're launching the Deep Insights Agent, and we’ve taken a different approach: before any question reaches the model, your data is fully cleaned, normalized, and made AI-ready. What you get back are answers grounded in reality, not an LLM's best guess.

What is the Deep Insights Agent?

The Ometria Deep Insights Agent links your Ometria data to Claude. You ask questions about your customers, campaigns, or strategy in plain English, and Claude returns answers, visualizations, and dashboards drawn from your full customer universe.

See the Deep Insights Agent in action:

Why it matters

The Deep Insights Agent changes three things about how retail marketing teams work with their data.

• Insight beyond your dashboard's limits: Dashboards only show you the metrics you've already decided to track. The Deep Insights Agent lets you explore your data iteratively, follow a thread of curiosity wherever it leads, and surface trends and opportunities you'd never spot in a fixed report.
• Strategic questions answered today: Answers in seconds, not weeks. No more cross-team requests or drawn-out projects; strategic questions that usually get deferred to a future project can be answered today, and routine reporting that used to take days happens in minutes.
• Answers grounded in your real data: Answers are grounded in your complete, reliable customer dataset rather than whatever happened to be queryable, and you can act on them with confidence rather than wondering what an LLM might have invented along the way.

What makes the Deep Insights Agent different?

The thing that separates a reliable AI insight tool from an unreliable one is how the data is prepared before a query is run. This is exactly where most MCP-style tools come up short.

The Deep Insights Agent automatically cleans, contextualizes, and enriches your data so the model works from an AI-ready foundation. Specifically, it:

• Normalizes source data: AI agents automatically identify and correct inconsistencies, primarily around product attributes, matching duplicates, resolving spelling variants, and ensuring only populated attributes are surfaced for querying
• Semantically enriches product data: Product attributes, custom fields, and order properties are transformed into bespoke dimensions that reflect each client's specific catalogue, including multi-value attributes and inferred latent dimensions like color and size
• Bakes in retail KPI definitions: Standard retail metrics like AOV, repurchase rate, and lapsed customer status are provided as context alongside each retailer's own definitions, so there's no ambiguity in how key metrics are calculated
• Loads in business context: A detailed context layer is configured by your Ometria CSM, covering your strategic priorities, brand positioning, target market, trading patterns, reporting preferences, and data hierarchy, so the AI understands not just how the business measures performance but how it operates
• Strips PII before any data reaches the model: The agent works with analytical data only and can never modify what it queries

No other player in this space does this. Other MCP connectivity tools pass raw API data straight to the model, with no cleaning, no context, and no retail logic. In-platform AI tools are limited to the data held within their own platform, which isn't necessarily structured for AI to read and carries none of the business context that makes the answers meaningful.

In a category where everyone is racing to add AI capabilities, the meaningful differentiator now sits in the data layer underneath the model.

What can you use the Deep Insights Agent for?

The Deep Insights Agent is useful across three broad areas of work.

Everyday reporting becomes a single conversation rather than a weekly grind, with campaign stats, revenue overviews, product performance, and health checks all in one place.

For example: “Show me campaign performance last week, broadcast and automation, compared to the week before” or “What were our top 10 products by revenue last 30 days?”

Customer intelligence helps you understand the health of your customer base, lifecycle, repeat purchase behavior, and high-value customer profiles, drawing on Ometria's full unified customer view. 

For example: “Profile our top 10% of customers by acquisition channel, first purchase category, and campaign engagement” or “How did the Superfan segment perform on email in the last 3 months?”

Strategy and optimization turns analysis into action. Ask what you should do next, and get a data-backed answer. 

For example: “If we offer a discount after a first purchase, what could this push in repeat rate and incremental revenue?” or “Which customers should I be suppressing from sends to improve conversion?”

For more ideas, take a look at our prompt library of ready-to-use prompts for retail analytics.

Get started

The Deep Insights Agent is now generally available to Ometria customers. If you'd like to see what it could do for your team, get in touch with your Customer Success Manager.

If you're not yet an Ometria customer and would like to see the Deep Insights Agent in action, book a demo.