FAQs

When using Expana IQ Connect via your own AI tool, your chat transcripts are not shared with Expana. However, the inputs to all Expana IQ Connect tool calls (together with information identifying you as the user) are shared with Expana to enable Expana IQ Connect to provide you with the requested information (e.g. user X’s AI tool searched for UK chicken prices, user Y’s AI tool requested forecast information for series code 522, user Z’s AI tool searched for news and insights related to the Strait of Hormuz, etc). 

 

Expana also processes this data, together with other personal data (such as your IP address, the AI tool you’re using, etc) and any feedback you choose to provide about Expana IQ Connect, for security purposes, to enable us to investigate bugs or issues, and to enable us to learn from how users interact with Expana IQ Connect. 

 

We take the privacy and security of your personal data seriously. Any personal data shared with Expana (including Expana IQ Connect tool inputs): 

 

Is not available to other customers and is only used to serve your experience and carry out the activities outlined above. 

Is not sent to any LLM outside of Expana’s private cloud infrastructure on Azure and is therefore not accessible by any external third-party LLM provider. 

Is not used to train any LLMs. 

Expana’s use of personal data is governed by Expana’s Privacy Policy and the Data Protection terms between Expana and the subscribing customer. 

When your AI tool is connected to Expana IQ Connect, your AI tool requests specific information from Expana IQ Connect, to enable it to carry out your request. Expana IQ Connect then provides your AI tool with the requested information and your AI tool then uses this information to generate the output you requested. 

 

AI tools can sometimes behave in ways that are inaccurate, incomplete, or unreliable. For example, the responses that you receive may not accurately reflect the information they are based on, or they may generate content that sounds reasonable but is incomplete.  

 

Outputs produced by an AI tool (including one that is connected to Expana IQ Connect) should therefore not be used as a definitive authority. We encourage you to think about the situations when you use AI tools and review the quality of the outputs you receive from them. 

 

In some cases, information returned by Expana IQ Connect to your AI tool may itself be AI generated. As with all AI tools, Expana IQ Connect can hallucinate or make mistakes. Whilst we take great care in creating the best products that we can, users are responsible for checking that the outputs from Expana IQ Connect are correct. 

Agentic procurement workflows are only as good as the market data behind them. If the data doesn’t hold up, neither does the answer. Expana IQ Connect gives your AI agents direct access to Expana’s up-to-date data and content, so their outputs can draw on an independent, transparent source. 

 

For example, say a supplier proposes an 8% price increase, citing rising ingredient costs. You ask your AI agent whether it’s justified. Left to search the web, it might rely on a months-old article or unverified sources and give you an answer you can’t check. With Expana IQ Connect, it can use Expana’s current prices for those ingredients, so you can check for yourself whether the increase is justified and negotiate with confidence. 

Pricing depends on several factors, including which Expana data and content you’d like to access through your AI tool. For more details, contact us to discuss your AI use cases and commodity intelligence needs. 

Expana IQ Connect provides access to:

 

Historical data

Forecasts 

News, analysis, commentary, updates, insights and fundamentals content (where the source is Expana) 

Data and content availability will depend on your subscription permissions. License restrictions may apply on certain data and content. 

Both give you access to Expana data and content, but they’re designed for different purposes and need different levels of setup. 

Expana IQ Connect is an MCP server, built and maintained by Expana, that plugs straight into AI tools such as Claude, ChatGPT, Copilot and Gemini, with no development work needed. Once connected, you can access and use eligible Expana data and content in conversations with your AI tool and in the AI agents you build. 

The Expana API requires your engineering team to build and maintain a data feed, typically into a data lakehouse or warehouse. The raw data and content can then be exported to spreadsheets, integrated with your own systems or explored in data visualisation platforms. 

In short, Expana IQ Connect is best for equipping AI tools and agents with on-demand Expana intelligence, while the Expana API is best for bulk, automated data feeds into your own systems. 

Expana IQ Connect follows Expana’s established security policies and controls. All customer data is treated as confidential and is encrypted in transit (TLS 1.2 or higher) and at rest (AES-256 or equivalent). Access is restricted to authorised personnel in line with Expana’s access control policies. 

Further details on Expana’s security framework and policies are available via our Trust Center. 

To access Expana IQ Connect, an administrator of your organization’s AI tool first needs to set up the connection.  

You can then toggle on the connection within your AI tool and authenticate it by signing in with your Expana credentials. 

Expana IQ Connect provides a standardized way to connect AI tools, such as Claude, ChatGPT, Copilot and Gemini, to eligible Expana data and content. Once your AI tool is connected, it can draw on that Expana data and content whenever you ask it a relevant question. 

Expana IQ Connect ensures that finding and surfacing relevant Expana intelligence is fast and efficient by using the same AI search capabilities that power the Expana platform. 

For example, if you were to ask about chicken price trends, your AI tool would be able to query Expana IQ Connect to retrieve relevant historical data, forecasts and commentary, to provide its answer. 

Expana IQ Connect is an MCP (Model Context Protocol) server that lets you connect AI tools such as Claude, ChatGPT, Copilot and Gemini, directly to Expana’s leading agrifood market intelligence. MCP is an open standard that lets AI tools securely access external data sources. 

The savings depend on commodity spend and category, but procurement teams using independent benchmark data and forecasting to inform purchasing timing and negotiations have reduced input costs by up to 3%, based on Expana client outcomes. On a lean team covering a wide portfolio, that saving compounds across every category it’s applied to, not just the one you start with. Estimate your own potential savings based on your specific commodity spend, or see how procurement and sourcing teams at a similar stage have made the move.

Without an internal benchmark, the most reliable option is an independent, third-party commodity price source that isn’t tied to any single supplier. Expana’s benchmark pricing gives procurement teams a reference point to check supplier quotes against, rather than relying on the supplier’s own framing of the market. That makes it possible to spot a pricing request that’s drifted away from where the broader market actually sits.

A lean team can move from data-restricted to data-informed by adopting a single independent commodity benchmark rather than building internal data capability. The highest-leverage first step is replacing free, unverified data with one credible source for your core commodities, so every decision starts from the same reference point. No dashboard build. No new headcount. Just one number the whole team agrees to trust and use.

Data-restricted teams rely on free, unverified sources and supplier-provided pricing with no independent benchmark. Data-informed teams have introduced at least one trusted, independent data source and use it proactively: checking benchmark prices before a negotiation rather than relying only on what a supplier shares. The difference isn’t the amount of data available. It’s whether the team reaches for it before a decision or only after one has gone wrong.

The procurement data maturity curve describes how a team’s use of commodity data evolves over time: from Stage 1 (Data-Restricted), through Stage 2 (Data-Informed) and Stage 3 (Data-Driven), to Stage 4 (Data-Optimised). Each stage reflects not just what data a team has access to, but whether that data is used proactively and consistently in decision-making. A team can technically have access to good data and still be data-restricted in practice, if that data only gets pulled out after something has already gone wrong.

It means a person reviews and, where needed, guides or extrapolates the model’s output, rather than publishing the raw model result unchecked. The model still does the core analytical work.

By adding more relevant variables to the model and by keeping a human in the loop who understands the market well enough to question a result that does not make sense.

It is a statistical relationship between two variables that has no real causal connection, such as cheese consumption and bedsheet-related deaths. A model can find these patterns, but they will not hold up as genuine market drivers.

Chicken thigh prices follow a repeatable seasonal pattern tied to summer grilling demand. Red meat prices respond to a wider, less predictable mix of supply, demand, and speculative factors, which requires human judgement to interpret correctly.

No. Commodities with stable, seasonal demand patterns forecast well with AI alone. Commodities driven by shifting supply, demand, and speculation need human oversight alongside the model.

The clearest cases are harvest-driven. Agricultural supply concentrates in specific months, pushing prices down at harvest as supply peaks and up as stocks draw down in the months that follow. Weather shapes this further, affecting yields, timing, sometimes both in the same year.

Demand follows its own calendar. Heating fuel spikes in winter. Grilling season reliably drives beef prices. These cycles hold because the behaviour behind them (how people heat their homes, when they buy for summer) doesn’t change quickly.

Inventory connects the two. Stocks build when supply runs ahead of seasonal demand and draw down when demand outpaces supply. That cycle reinforces the price rhythm. It also makes a shifting pattern visible early. When inventory is building or drawing differently than usual, the pattern is already moving.

The cost gap between materials has moved more than the absolute prices. The strategic question is less about timing a single purchase and more about reassessing material mix in light of how differently each category has responded.

For test liner, Expana’s forecasting team estimates roughly 90% of the increase has already happened, with limited upside and a seasonal decline expected into year-end. The same fundamentals approach is applied across other packaging commodities.

That is still uncertain. The main variables are how quickly crude and shipping flows resume through the Strait of Hormuz and whether shippers trust the route. Estimates for Gulf aluminum supply alone returning to pre-war levels run to one or two months, with broader normalization harder to pin down.