Service
A dashboard tells you merely what happened. Clay's business intelligence services, built on Qlik's agentic AI stack, tell you what's happening right now and what's coming next and preemptively act on it before anyone has to ask. This isn't BI with an AI feature bolted on; instead, it's five specialised AI agents sitting on the governed data foundation Clay has spent two decades building for enterprises that got tired of guessing.
What it is
“Is this the number the business should actually trust?”
Clay Tech's Business Intelligence service is about the engagement that gets you the engagement you require, along with the structural integrity you desire and keeps you honest once you arrive: business intelligence solutions built around a real question, rather than a dashboard designed to look finished in a demo.
That question is exactly why the underlying data work matters more than the visualisation layer everyone notices first. Clay has consolidated fourteen separate, untrusted reporting processes into a single governed source of truth for one enterprise client: the unglamorous work of integration and governance that has to happen before any AI agent, forecasting model or dashboard can be trusted with a real decision. Get that foundation wrong, and an agentic AI layer on top just confidently automates wrong answers faster.
14separate reporting processes, reduced to one governed source of truth before any agent was allowed near a decision.
From “What Happened” to “What Should We Do”
Qlik's 2026 agentic AI experience didn't ship as one assistant; it shipped as five distinct, specialised agents, each built for a different question a business actually asks. Clay's BI implementation services are what turn that five-agent architecture into something your teams actually use.
The Monitoring Agent
Continuous anomaly detection across your data, instead of a dashboard someone has to remember to check, ensures that the agent flags the deviation before it becomes next Monday's fire drill.
The Forecasting Agent
Historical and real-time data combined to forecast demand, risk or churn, moving BI consulting from "here's the trend" to "here's what the trend means for next quarter", with the model retrained continuously rather than rebuilt annually.
Qlik Trust Score
A machine-learning-scored confidence rating on every dataset, so a report or an agent knows whether the data underneath is fit to act on before it implements; the governance layer that makes the other four agents safe to actually deploy.
The Content-Building Agent
Natural-language requests turned directly into analytics content, so a business user doesn't file a ticket with the BI team and wait a sprint for a chart that should have taken five minutes.
The Workflow-Triggering Agent
An insight that doesn't just get displayed but gets acted on — triggering the next step in a business process the moment a threshold is crossed, closing the loop between "we noticed" and "we did something about it".
Underneath all five: Qlik Answers, the conversational interface that lets any of these agents be reached in plain language; and Qlik Open Lakehouse, the governed data foundation everything above depends on actually being trustworthy.
Use cases
Proven results
Four engagements. The same pattern each time: governed data first, then an agentic layer the foundation is already ready to carry.
Enterprise
An enterprise client was running fourteen separate reporting processes, and leadership couldn't agree on which single number to trust in a room. Clay consolidated all fourteen into one governed source of truth, the exact kind of foundation Qlik's Trust Score is built to score and certify, and precisely the foundation an agentic AI layer needs before it can be handed any real decision.
Mahindra
Mahindra's teams were working across disparate systems and data silos with no real-time, unified view for decision-making. Clay integrated multiple data sources into a suite of Qlik dashboards, replacing fragmented reporting with a single interactive view of business performance.
Delivered today, that same engagement wouldn't stop at the dashboard. The same governed, integrated data feeding those Qlik dashboards is exactly what a monitoring agent would need to flag a performance anomaly before the next review meeting and what a forecasting agent would need to project the trend forward instead of just displaying it. The hard integration work is already proven; the agentic layer is what that same foundation is ready for next.
EIH
EIH's Customer Management team needed to walk into meetings with department heads already knowing hotel and department-level financials, not scrambling to assemble them beforehand. Clay integrated financial, sales and operational data into Qlik Sense, turning pre-meeting prep into a five-minute dashboard check.
The next version of this engagement removes even that five minutes: a content-building agent answering "What's this month's F&B revenue by property?" in natural language, pulled from the same governed data, with no dashboard navigation required at all.
Burger King
Territory managers needed to understand store-level profit & loss before every site visit, across a network too large to track manually. Clay built Qlik Sense dashboards giving instant visibility into sales, costs and profitability by outlet.
If your BI team is still building the same three reports every month by hand, or your leadership still doesn't trust the number in front of them, this is the exact gap this practice exists to close. Start a conversation about what you need.
Manufacturing · Hospitality · Retail · Banking · Telecom · Government
Business Intelligence (Product) → Databricks & Data Engineering → Chatbot → Integration & API Management →
Tell us which number in your business nobody quite trusts yet. We'll show you what it looks like once an agent can act on it instead of just showing it to you.
Start a conversation →