We get this question regularly now. A finance leader or procurement director comes into a conversation and asks some version of: "We're already using Claude for a lot of things. How is SpendCraft different?"
It's a fair question. Both involve AI. Both have a chat interface. Both promise to help you work faster and make better decisions. From the outside, the category lines look blurry.
They're not. And understanding where they diverge is worth the ten minutes it takes to read this.
What General-Purpose AI Tools Actually Are
ChatGPT, Claude, Gemini, Microsoft Copilot. These are large language models. They are trained on enormous amounts of text and are exceptionally good at reasoning, writing, summarizing, explaining, and generating content based on that training.
They are general-purpose tools. That's their strength and their limitation. General-purpose means they can do a remarkable number of things across a remarkable number of domains. It also means they are not built specifically for any one domain, and they do not have access to your data unless you explicitly provide it.
When you ask Claude to help you understand your spend, Claude works with whatever you give it in the prompt. A pasted table. A summarized report. A question about procurement best practices. Claude is genuinely useful for all of those things.
What Claude does not have is your vendor master. Your two years of AP transaction history. Your taxonomy. Your supplier relationships. Your classified spend data. Claude cannot normalize your vendor records, run a savings scan against your actual transactions, or tell you that you have seventeen active suppliers in a category where you should have two. It has no access to the data that would reveal that.
This is not a criticism of Claude or any other general-purpose model. It is a description of what they are designed to do.
How SpendCraft Actually Uses AI
Before going further, it's worth being transparent about something. SpendCraft uses LLMs internally. General-purpose models play a role in parts of our product, including natural language understanding in Ask Crafter and certain narrative generation tasks.
The difference is what those models are working with. When Ask Crafter answers a question about your spend, it is querying a structured dataset that SpendCraft has already normalized and classified. The LLM is the interface. The structured spend data is the substance. Without the classification layer underneath it, the LLM would have nothing reliable to work with.
We've written about how we think about AI at SpendCraft in more depth. If you want to understand our approach to where AI earns trust in a financial context and where it doesn't, that piece is worth reading alongside this one.
The Data Problem That General-Purpose AI Cannot Solve
Here is the core issue. Spend intelligence is not a reasoning problem. It is a data problem.
The most sophisticated AI model in the world cannot tell you where your savings opportunities are if it cannot see your spend data. And it cannot make sense of your spend data if that data is not structured. If vendors appear under dozens of aliases, if transactions are unclassified, if item descriptions are inconsistent, if spend from different business units lives in different systems and has never been consolidated, no AI model produces reliable output on top of it.
General-purpose AI tools take inputs and produce outputs. They do not build the input. They do not normalize your vendor master before they analyze it. They do not classify your transactions into a taxonomy so that category-level analysis is possible. They do not deduplicate suppliers, resolve subsidiaries, or maintain a clean spend foundation that stays current as new transactions arrive.
That work is what has to happen before any AI can produce reliable spend intelligence. And it requires purpose-built infrastructure, not a general-purpose chat interface.
What Happens When You Try to Use General-Purpose AI for Spend Analysis
The limitations become concrete quickly.
You export a spreadsheet from your ERP and paste it into ChatGPT. ChatGPT can summarize it, identify some patterns, and answer questions about what's in the file. That's useful for a quick read of a single dataset.
What it cannot do is tell you that "Microsoft Corp," "Microsoft Corporation," "MSFT," and "Microsoft Redmond" in that spreadsheet are all the same supplier, and that your total Microsoft spend across all four entries is $2.3M, which would qualify you for a volume discount tier you're currently not accessing. That normalization requires a system that understands vendor identity, not just text patterns in a spreadsheet.
It cannot tell you that the category tagged as "Professional Services" in your ERP contains $400K of spend that should be in "IT Consulting" under your taxonomy. Spend that, once correctly classified, would reveal you have three overlapping contracts in the same category with different vendors. That requires a classification engine built for your taxonomy, not a language model reasoning about category names.
It cannot run a tail spend scan, because it has no persistent view of your supplier base. Every conversation starts fresh. There is no accumulated understanding of your spend patterns, your supplier relationships, or your category structure.
Where General-Purpose AI Tools Are Genuinely Useful in Procurement
Being clear about the limitations doesn't mean dismissing the tools. ChatGPT, Claude, Gemini, and Copilot are useful in procurement contexts, just not for the core data work.
They are useful for drafting RFP documents, summarizing supplier responses, writing category strategy documents, preparing for supplier negotiations, researching market conditions, and answering general procurement questions. Tasks where the input is text and the output is text, and where the quality of the answer depends on reasoning rather than on access to your specific transaction data.
These are real productivity gains. Teams that use general-purpose AI for document work, communication, and research are faster than teams that don't. That value is legitimate.
It is just not the same value as structured spend intelligence. And conflating the two leads to a specific failure mode: believing that because you have access to a capable general-purpose AI, you have addressed your spend visibility problem. You haven't. You have a faster way to work with the same incomplete data.
The Structured Data Prerequisite
There is actually a version of this where general-purpose AI and purpose-built spend intelligence work together rather than compete.
Once your spend data is normalized, classified, and structured, once you have a clean vendor master, consistent taxonomy coverage, and a reliable spend foundation, general-purpose AI tools become significantly more useful when applied to that data. The quality of AI output is a function of the quality of AI input. Structured spend data is better input than raw transaction exports.
Some organizations are already building toward this: structured spend data as the foundation, with general-purpose AI tools as the interface for certain workflows on top of it. That combination makes sense. What doesn't make sense is using general-purpose AI as a substitute for building the structured foundation in the first place.
This is exactly how SpendCraft is designed. The foundation does the structural work. The AI, including the LLMs we use internally, operates on top of it.
The Honest Answer to "How Are You Different From Claude?"
SpendCraft and Claude are not in the same category. They solve different problems.
Claude is a general-purpose reasoning engine. It is excellent at tasks that require language understanding, reasoning, and generation. It has no access to your spend data, no ability to normalize your vendor master, and no mechanism for maintaining a structured view of your supplier relationships over time.
SpendCraft is purpose-built spend infrastructure. It normalizes vendor data, classifies transactions into your taxonomy, and runs savings scans against your actual AP history. Ask Crafter, SpendCraft's chat interface, lets you query that structured data in natural language. The chat works because the data underneath it is clean, classified, and current. Without that foundation, the chat would have nothing reliable to work with.
The analogy that makes this concrete: asking how SpendCraft differs from Claude is similar to asking how Bloomberg Terminal differs from ChatGPT. ChatGPT can discuss financial markets fluently. Bloomberg Terminal has your actual market data, structured and queryable in real time, with decades of history behind it. The chat capability is superficially comparable. Everything underneath it is not.
If your organization is evaluating AI tools for procurement and finance, the right question is not which AI is smarter. The right question is which problem you are actually trying to solve. If the problem is document drafting, summarization, and research, general-purpose AI tools are excellent. If the problem is spend visibility, savings identification, and supplier intelligence grounded in your actual transaction data, that requires a structured spend foundation that general-purpose AI is not designed to build.
Both problems are worth solving. They require different tools.
SpendCraft normalizes vendor data, classifies spend using proprietary AI, and runs on-demand savings scans against your actual transaction history. Ask Crafter lets you query that structured data in natural language because the foundation makes the answers reliable.
Enabling Business Users.