Crisp has launched Halo, a retail AI platform that can run inside an enterprise’s own cloud environment, bringing sales and supply-chain analysis closer to the data and governance systems companies already operate. Announced on October 29, the platform targets consumer packaged goods (CPG) manufacturers, retailers, distributors and brokers.
The launch addresses a familiar retail problem: teams can see a change in sales or inventory, yet still spend hours assembling the context needed to explain it. Crisp’s Halo announcement describes a system that combines retail data, industry-specific logic and agentic tools to help users investigate performance and decide what to do next.
A Retail Data Foundation Beneath the AI
Halo’s architecture begins with the information arriving from retailers and distributors. Crisp’s platform overview separates that foundation into data integration, management and modeling, with agentic tools and role-specific applications built above it.
That structure matters because a product, store or reporting period can be represented differently across trading partners. A question about an inventory shortfall becomes more useful when the underlying systems agree on which item, location and time period the question concerns.
Crisp describes its approach as vertical AI: data is cleaned and enriched with retail semantics before being used for analysis. The company’s background materials identify retailer calendars, product hierarchies, fill rates and out-of-stock analysis as examples of the business context involved. Crisp says more than 7,000 customers, including 90 of the top 100 CPG companies, use its services; those are company-reported adoption figures.
Running Inside the Customer’s Cloud
Halo is available as a hosted platform or within customer environments on Databricks, Snowflake and Google Cloud. The latter approach gives enterprises an alternative to moving their retail intelligence into another externally hosted application.
Crisp’s Databricks architecture documentation provides a concrete example. It places retail data in Delta tables governed through Unity Catalog, hosts applications and agents in Databricks Apps, and uses a Lakebase database for application state, conversations and agent memory. Model calls run through Model Serving and AI Gateway, while semantic queries use a selected SQL warehouse.
According to Crisp, proprietary data remains within the customer’s environment, while non-proprietary operational logs and telemetry are shared with its central systems. That distinction is more precise than suggesting that the deployment has no external communication. Crisp also lists MLflow integration and publishing its semantic models as Unity Catalog Metric Views as roadmap items.
For IT teams, the significance is operational: access controls, inference choices and usage tracking can align with infrastructure already in place. Deployment location alone, however, does not establish the accuracy of an answer or the suitability of an automated action.
From Portfolio Questions to Retailer-Specific Decisions
The launch introduces Central Desk and Customer Desk. Central Desk gives headquarters teams a view across their retail portfolio for work spanning sales strategy, category management and supply-chain planning. Customer Desk is aimed at account teams that need the metrics, terminology and operating logic of an individual retailer.
Consider a brand investigating weaker sales for a particular product. The useful question is whether the change reflects demand, availability, distribution or another operational factor. Connecting analysis to store- and item-level records can help teams narrow that investigation rather than treating a portfolio-wide decline as a single problem.
That is the practical distinction between a conversational interface and a retail application: the latter needs to understand the business entities and relationships behind the question. Halo’s broader platform also organizes capabilities around functions such as replenishment, category planning and supplier collaboration.
Traceable Answers and Repeatable Workflows
Crisp’s AI agent materials describe tools that use daily store- and product-level intelligence to support analysis and action. Across Halo, the company lists conversational questions, recurring reports, workflow automation and custom applications as core ways to use those tools.
The launch materials emphasize tracing recommendations to source data and the logic used to produce them. Such provenance can make an answer easier to inspect: an analyst can examine the records and reasoning behind a recommendation instead of accepting a fluent explanation at face value.
For enterprises evaluating the platform, the important questions extend from that explanation to execution: which actions are permitted, which require review, and how changes are measured after implementation. The published materials describe capabilities and deployment architecture, rather than an independent benchmark of forecasting accuracy or financial returns.
Halo’s strongest proposition is the connection between those layers. Retail context gives an AI system more useful information to work with, customer-cloud deployment can fit existing governance, and role-specific workflows put the resulting analysis in front of the people responsible for inventory and commercial decisions.
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