SyncHub says plain-English AI queries are replacing dashboards

SyncHub, a New Zealand company founded in 2019, says more than 500 businesses across 30 countries now use its platform to connect cloud software to reporting tools, AI platforms and business intelligence systems — and that plain-English AI queries are becoming the default way they interrogate that data.

The company connects more than 70 software-as-a-service platforms to reporting tools and AI systems. According to SyncHub, customers are moving past dashboards and static reports. Instead, users ask direct questions about sales, stock, staffing, invoices and performance through AI applications, including SyncHub's own AI Assistant, Anthropic's Claude and OpenAI's ChatGPT.

The shift matters because it changes where the bottleneck sits in business analytics. Instead of exporting CSV files or asking technical staff to write SQL (Structured Query Language, the standard language for database queries), teams want direct answers pulled from live operational data.

What role does Model Context Protocol play?

SyncHub attributes part of the trend to Model Context Protocol, or MCP — an open standard that lets AI tools connect directly to business systems and data sources. The company argues MCP gives users a way to retrieve answers from current operational data rather than relying on manual exports or reports that may already be out of date.

Ben Liebert, SyncHub's founder, said the protocol is compressing a workflow that used to take hours. "For years, the default process was to open a dashboard, export a file, or find someone technical to write a query. MCP is speeding that up, because it lets businesses connect their structured data straight to the AI tools they're already using, whether that's our own AI Assistant or something like Claude," Liebert said.

The claim deserves scrutiny on one point: nothing in the protocol guarantees the answers are right. SyncHub itself concedes the approach depends entirely on the quality of the underlying data. Many businesses run multiple cloud systems — accounting, customer relationship management, human resources, inventory and timesheet platforms — each storing information in a different format.

That fragmentation is the market gap SyncHub is positioning itself in.

How does SyncHub differentiate from vendor-built MCP servers?

Some software providers have started offering their own MCP servers. SyncHub says these come with a limitation: each server exposes only the data held in that individual platform. A question spanning sales and inventory, for example, would still require stitching answers together.

SyncHub's model is to aggregate data from multiple sources into a single structured database, so queries can span the wider business. In service of that strategy, the company recently released a Custom Database connector. It allows companies to query cloud data alongside on-premise systems, legacy databases and databases built for internally developed software.

Liebert said the connector was built in response to customer demand. "Although businesses lean on SaaS platforms for the majority of their software, many have also had to build custom software to meet their particular requirements. We had a lot of demand from customers who needed that data alongside their cloud data, so we built a connector for it," he said.

SyncHub says it has recorded an increase in custom database connections since the connector launched. The company links that rise to the spread of AI coding tools, which it says are contributing to more companies building their own software and databases.

That proliferation recreates a familiar reporting problem. Even where bespoke tools are used, the resulting data still needs to be combined with finance, sales, staffing and other operational systems if managers want a complete view. The more companies build, the more integration work they create — a dynamic that benefits middleware vendors like SyncHub.

What do customers actually experience?

One customer cited by SyncHub is Pooky, a UK business that used the platform to improve access to data from its enterprise resource planning (ERP) system and later connected SyncHub to Claude through an MCP connector.

Benjamin Durrant, Business Solutions Architect at Pooky, described the impact in sweeping terms. "We joined SyncHub originally to achieve what seemed impossible: to get good structured data from our ERP. Since then we have used the built-in AI to help create SQL queries, which we now use for our daily KPIs. More recently we connected SyncHub to Claude using the MCP connector, and it's made getting insights a thousand times easier. We now have reports for every area of our business providing insights and tracking every day with little to no effort," Durrant said.

A figure like "a thousand times easier" is customer testimony, not measurement. What it does illustrate is the appetite among mid-sized businesses for analytics that require no technical intermediary — and how quickly MCP connectors have moved from novelty to daily workflow in at least some organizations.

Liebert framed the customer demand as a reaction against accumulating layers of visualization software. "What we're seeing is that people don't want another layer of dashboards if they still have to interpret everything themselves. They want to ask a direct question about their business and get a reliable answer back. That only works if the data underneath is clean, structured and properly connected. AI isn't magic. If the data foundation is poor, the answers will be poor too," he said.

Do traditional reporting tools still matter?

Yes, according to Liebert — but in a narrower role. He said traditional reporting tools retain a place for dashboards and formal reporting, while plain-English querying is becoming the starting point when staff want a quick answer.

That division of labor suggests a practical reading of the AI-analytics trend. Conversational interfaces handle ad-hoc questions; conventional reporting handles standardized, audited outputs. Neither replaces the other, and the persistence of both keeps data-integration vendors relevant regardless of which front end wins.

The structural constraint remains data quality. SyncHub's pitch rests on the claim that aggregation into one clean, structured database makes AI answers trustworthy — a claim every customer will need to test against its own messy source systems.

SyncHub has not disclosed user numbers for its AI Assistant, connection volumes tied to the Custom Database connector, or revenue figures. The reported rise in custom database connections is described only as an increase, without published numbers.

What the company has demonstrated is that MCP has expanded the addressable market for data middleware: businesses that already normalized their SaaS data for reporting can now expose the same structured layer to general-purpose AI tools. Whether demand keeps compounding depends on whether plain-English answers stay reliable as query volumes and data sources grow.