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Xfuze Agentic Insights Application

Agentic-driven self-service analytics, AI-built dashboards, natural language querying and AI-enabled exception reporting. From manual to automated insights.

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Three Xfuze AI agents overlaid on any retailer data warehouse, answering questions, building dashboards from a sentence and flagging exceptions worth acting on.

What the agents do

Interprets questions, selects the metrics to display, creates, reviews and corrects its own queries, generates new visualisations and decides which changes are worth flagging.

Brand
Specialist and multi-channel retailers (clients not named)
Built by
Xiatech, on Google Cloud
Contributed by
Xiatech (Agentic analytics application)
Industry
Retail, Apparel & Footwear, Grocery, CPG
Audience
B2B
Category
Data & Analytics
01
The problem

Why this exists

Retailers hold years of trading, stock, customer, and store data in their warehouse, but most people who run the business cannot query it. Every new question becomes a request to a small analytics team, and every new dashboard takes weeks to build.

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Trading, merchandising, marketing, supply chain, and store teams decide from static reports and spreadsheet exports, and different reports often give different numbers. Changes that matter, such as a store falling behind or a category dropping, are spotted late because nobody has time to check every number every day.

02
Outcomes

What changes when this is in place

  • More than 70 ready-made retail dashboards across 11 domains.
  • Business questions answered in natural language from the retailer's own data, with every number coming from a query result.
  • A named person approves every AI-built dashboard before it reaches production.
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  • Each dashboard is checked from data warehouse to screen, and automated checks on freshness, schema, and reconciliation flag bad data before anyone acts on it. Performance targets are agreed with each customer against its own baseline.
03
How it works

Architecture

The application connects to a retailer's own data warehouse through a governed semantic layer, with its own sign-in, roles, row-level security, audit log, and cost controls. It runs standalone on Google Cloud or on the Xfuze Agentic Orchestration Platform, where Xfuze brings in and models data from point of sale, ecommerce, ERP, and other systems.

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Our Xfuze agent, CHIP, is a family of AI-enabled agents performing different tasks on the data warehouse via the Xfuze semantic layer. CHIP Assist picks the right measures, writes read-only queries, checks the results, and shows them as a chart or table. CHIP Dashboard Builder builds a complete dashboard from a description, validates every chart against the live semantic layer and corrects its own invalid queries. CHIP Pulse sweeps every measure on every dashboard by store, region, and category, and raises statistically significant anomalies and trends, filtered to what each user may see.

Queries are read-only and limited to approved data, row-level security is applied on the server, Google Cloud Model Armor screens every prompt and response, and per-query and daily cost caps stop runaway spend. New dashboards start in development and reach production only when a named approver promotes them.

Point of sale, ecommerce, ERP, and other source systems feed the retailer's data warehouse, optionally through Xfuze. A governed semantic layer with guardrails, read-only queries, row-level security, Model Armor screening, cost caps, and an audit log, sits over the warehouse. Three agents work through it: CHIP Assist answers questions in natural language from live query results, CHIP Dashboard Builder builds a dashboard from a description and corrects its own queries, and CHIP Pulse sweeps every measure for anomalies and trends. A named approver promotes AI-built dashboards to production, and results reach users in their existing dashboards.

Point of sale, ecommerce, ERP, and other source systems feed the retailer's data warehouse, optionally through Xfuze. A governed semantic layer with guardrails, read-only queries, row-level security, Model Armor screening, cost caps, and an audit log, sits over the warehouse. Three agents work through it: CHIP Assist answers questions in natural language from live query results, CHIP Dashboard Builder builds a dashboard from a description and corrects its own queries, and CHIP Pulse sweeps every measure for anomalies and trends. A named approver promotes AI-built dashboards to production, and results reach users in their existing dashboards.
Open standards used
REST APIsOAuth 2.0
Tech stack
Google CloudVertex AIGoogle Cloud Model ArmorCube semantic layerXfuze Agentic Orchestration Platform (optional)
Next step

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Xiatech is behind this solution. Ask about letting the whole business query its own data, and the MACH Alliance will put you in touch with the person who can answer.

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Everyone credited on this solution

  • XiatechAgentic analytics application

Every organization named here has a page listing the rest of its work on the Hub.

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Verification

Contributor submission

The detail on this page comes from the contributor's own account of their own work. The MACH Alliance reviews a submission before publishing it and does not independently verify the figures in it.

Where this came from

Solution detail is drawn from Xiatech's submission to the hub.

Status: Live in productionAdded Oct 26, 2026A listing is an editorial decision, not a MACH Alliance certification.
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