The answers you need sit in Azure Synapse warehouse tables, but anything the existing reports do not cover means writing SQL against the right pool or waiting in the BI team's queue. A simple question about last month's volumes or a region's numbers can take days to come back.

Ask Claude, ChatGPT or Microsoft Copilot your question in plain language, and PopdockAI reads the Azure Synapse tables and views your IT team has made available to answer it. The AI can filter by the fields the admin exposes, such as dates, regions or status, and sort or limit the results. You never write SQL or touch a connection string, and every question is logged with who asked it and from which client.

Questions that fall between reports get answered in the conversation, without waiting on someone who can write SQL.

Getting a filtered extract out of Azure Synapse for a month end pack or a variance review means asking for a query, exporting the result and tidying it in Excel. When the period or the filter changes, the whole request starts again.

Ask Claude, ChatGPT or Microsoft Copilot for the rows you need, such as a period, a cost center or an account range, and PopdockAI reads them from the Azure Synapse tables IT has approved and returns a real Excel file with a set file name and worksheet. The filters you can change are the ones the admin exposed, and fixed restrictions keep the extract inside the data you are allowed to see. CSV is available when a file feeds another system.

Filtered warehouse extracts arrive as ready to open Excel files in minutes, not as a ticket in a queue.

When a warehouse report and the ERP disagree, someone has to query Azure Synapse, run the matching report in the source system and line the two up by period and account to find where the load drifted. It is slow, manual and easy to compare the wrong periods.

Ask Claude, ChatGPT or Microsoft Copilot to compare a period, and PopdockAI reads the Azure Synapse tables and your ERP's lists from the same server, so the AI can set the two side by side and point to the accounts, dates or records that do not match. Each system is read through its own approved tools with its own filters and restrictions, and nothing is changed in either one.

Gaps between the warehouse and the source system are found in one conversation, before they reach a report.

Business users want their AI assistants to reach warehouse data, but handing out Azure Synapse credentials or a broad SQL login exposes every table in the pool. Without control over tables, fields, rows and which AI client is used, the safe answer is usually no.

IT chooses exactly which Azure Synapse tables and views become tools, which fields they return and which rows they can reach through fixed restrictions, then grants each tool by user, team and AI client, so a tool can be open to Claude but not ChatGPT. The credentials stay in PopdockAI and never reach Claude, ChatGPT or Microsoft Copilot. Inputs are validated before the warehouse is queried, every MCP call is logged with user, client and response, and tools are versioned so a change can be rolled back.

Teams get AI answers from the warehouse while IT keeps control of every table, row and client that can reach it.

The weekly review of warehouse numbers depends on someone remembering which tables to check, which filters to apply and how last week was summarized. Each person asks the question a little differently, so the answers drift from week to week.

Your admin saves the review as a prompt with inputs such as the week or the business unit, and it appears in Claude, ChatGPT or Microsoft Copilot for you to pick. PopdockAI then reads the same Azure Synapse tables with the same filters every time and the AI summarizes what changed. The prompt and its tools are governed like any other, so only the people and clients granted access can run it.

The weekly KPI review runs the same way every time, from one prompt, with no one rebuilding the query.

Warehouse tables are often too wide or too raw for an AI to use well, and the logic that turns them into the right answer, the joins, the exclusions, the date rules, lives in queries only the BI team knows. Exposing raw tables invites answers built on the wrong logic.

Build the query once in PopdockAI's query builder against Azure Synapse, then expose it as a tool that Claude, ChatGPT or Microsoft Copilot can call. The AI gets your joins and business rules already applied, and can only vary the parameters you choose, such as a date range or a region, each validated before the query runs. Client instructions on the tool tell the AI what the results mean and when to use it.

AI answers use the same approved query logic as your reports, so the numbers match what the business already trusts.