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KAI vs General-Purpose AI

Your AI can only answer from what it knows. KAI is different

KAI combines AI with the specialized market intelligence Kemiex has built since 2017, so your answers start with the market, not just the public internet.

The model isn’t the problem

General-purpose AI is incredibly powerful. That’s not the problem.

ChatGPT, Claude, Gemini and other general-purpose AI tools have transformed how professionals work. They can research topics, analyse documents, reason through complex questions and turn hours of work into minutes.

And you can absolutely ask them about raw-material markets. The difference appears when the answer depends on information they don’t have.

01

Research & reasoning

Explore topics, explain concepts, analyse information and work through complex questions.

02

Writing & productivity

Draft emails, summarise documents, structure ideas and accelerate everyday work.

03

Your own information

Upload reports, analyse spreadsheets or connect AI to information available within your own workflows.

But a powerful AI cannot analyse information it doesn’t have.

Raw-material decisions depend on intelligence spread across pricing, suppliers, trade activity, market developments, historical trends and specialised industry sources. That intelligence does not automatically come with a general-purpose AI subscription.

With KAI, that’s where the answer starts.

Price Benchmarks Supplier Intelligence Trade Flows Market News Price Trends Market Reports Agri Explorer Market Chatter
KAI
Put it to the test

Ask the same market question.
See the difference.

A general-purpose AI can reason about raw material markets. The real difference is how much market intelligence you need to bring to the conversation yourself.

Both get the same question

“What’s happening with DL-Methionine prices in Europe, and what should I know before my next negotiation?”

General-purpose AI

A strong general model can research public information, reason about market dynamics and analyse any relevant information you provide.

What it can do

  • Research published market information
  • Identify reported market developments
  • Reason about supply and demand dynamics
  • Analyse documents, reports and data you upload

What you may still need to provide

  • Your current supplier quotations
  • Historical pricing information
  • Supplier and sourcing context
  • Specialist market reports or subscriptions
  • Non-public intelligence your team has collected
You bring the market intelligence to the AI.
KAI

KAI starts with Kemiex market intelligence already behind the conversation, allowing the question to be analysed in the context of the market you’re actually operating in.

KAI can bring together

So the answer can include

  • Current pricing context
  • Recent price movements
  • Supplier and sourcing context
  • Relevant market developments
  • Trade and supply signals around the ingredient
KAI brings the market intelligence to the answer.

With general AI, you bring the market intelligence.
With KAI, it’s already there.

The intelligence gap

The information that matters
isn’t always on the open web.

Decisions on raw materials depend on signals that are often fragmented, specialised or unavailable through conventional web research. Knowing the market requires more than knowing what has been published about it.

01

What the market is actually paying

Published prices and market commentary do not necessarily reflect the levels buyers and sellers are currently discussing. Market context improves when pricing observations can be compared across time, regions and transactions.

The question behind the question

“Is this quotation competitive, or is the market already trading lower?”

02

Who is actually supplying the market

Knowing that a producer exists is different from understanding where supply is coming from, which origins matter, what alternatives buyers have and how the supplier landscape is evolving.

The question behind the question

“Which suppliers or origins should I realistically be considering?”

03

What has changed recently

Raw-material markets can move faster than conventional research cycles. A change in pricing, production, logistics, trade activity or supplier behaviour may matter before it becomes broadly visible.

The question behind the question

“What has changed since the last time I bought?”

04

What the signals mean together

A price move, supplier announcement or trade-flow change rarely tells the whole story alone. The real value comes from connecting multiple signals and understanding what they mean for the decision in front of you.

The question behind the question

“Should I buy now, wait, negotiate harder or look for another source?”

You don’t need AI to know more about everything.
You need it to know more about your market.

Already using AI?

See how KAI compares
to the tools you already use.

General-purpose AI tools have different strengths. Explore the comparison that matters most to your current workflow.

ChatGPT
ChatGPT
vs KAI

KAI vs ChatGPT

ChatGPT is an exceptionally capable general-purpose assistant. KAI starts from a different place: specialised raw-material market intelligence already built into the conversation.

The key question

What changes when the AI already knows the market you’re asking about?

Explore the comparison
Claude
Claude
vs KAI

KAI vs Claude

Claude is highly capable at reasoning through complex information and documents. KAI adds the specialised market intelligence needed to reason about raw-material decisions in context.

The key question

What if the intelligence you need doesn’t have to be uploaded first?

Explore the comparison
Gemini
Gemini
vs KAI

KAI vs Gemini

Gemini can work closely with the information already inside your Google ecosystem. KAI is designed to complement that internal context with intelligence about the wider raw-material market.

The key question

Your data can tell you what happened internally. But what happened in the market?

Explore the comparison

You don’t have to replace the AI tools you already use. KAI is built for the market questions those tools were never designed specifically to answer.

Could you recreate it?

Could you give a general AI
the same intelligence?

ChatGPT, Claude, Gemini and other capable models can analyse specialised market intelligence when given access to it. The harder question is what it takes to build and maintain that intelligence layer.

Absolutely, but the model is the easy part.

The real work is continuously sourcing, structuring, validating and updating the market intelligence the model needs to produce a useful answer.

To recreate the intelligence layer, you need to
Source it
Structure it
Clean it
Verify it
Update it
Connect it
Maintain it

Data doesn’t arrive ready for AI

Market intelligence comes from different formats, sources, geographies and levels of reliability. Someone has to turn it into information that can actually be compared and analysed.

Yesterday’s intelligence is not enough

Prices move, suppliers change, trade patterns shift and new market developments appear continuously. The intelligence layer has to keep moving too.

The work never really stops

Connecting the model is only the beginning. The real commitment is maintaining the data, integrations, quality controls and market coverage behind it over time.

The AI model is the visible part.
The intelligence infrastructure is the hard part.

KAI sits on top of the market intelligence infrastructure Kemiex has been building and refining since 2017, so your team can use the intelligence instead of building and maintaining the system required to create it.

Thinking about building it yourself? See KAI vs building your own agent

Ready to try KAI?

Give KAI a real market question.

See how Kemiex market intelligence becomes conversational, and how quickly a complex market question can turn into an answer you can actually use.

Get in Touch See what KAI can do

Bring the question you would normally spend hours researching.