Getting the Right Answer from AI: How AX Trace Leverages GraphRAG
Most organisations don’t struggle to get answers from AI.
They struggle to get the right answer they can trust.
This is one of the hidden benefits of AI that many SMEs overlook: accuracy with context, not just speed or automation. And it’s also where many AI projects quietly fail—especially when they rely on expensive model fine-tuning that SMEs cannot justify.
This is where GraphRAG changes the equation, and why AX Trace is built around it.
The Hidden Problem: AI Answers Without Context
Large language models (LLMs) are powerful, but on their own they:
Guess when context is missing
Mix unrelated information
Produce answers that sound right but cannot be verified
To fix this, many enterprises turn to LLM fine-tuning.
For SMEs, that approach is often too costly, too slow, and too risky.
Fine-tuning requires:
Large labelled datasets
Ongoing retraining
Specialist AI talent
Continuous cost as data changes
For most SMEs, this simply isn’t economically viable.
Why “Better Models” Are Not the Answer
A common misconception is:
“If we use a bigger or more fine-tuned model, the answers will improve.”
In practice, most wrong AI answers come from missing or disconnected context, not model quality.
If AI doesn’t understand:
How orders relate to customers
How documents connect to decisions
How data points link across systems
Then even the best model will still hallucinate.
The hidden benefit SMEs miss is this:
Better structure beats better models.
How GraphRAG Solves This (Without Fine-Tuning)
GraphRAG (Graph-based Retrieval Augmented Generation) works by grounding AI responses in a knowledge graph instead of retraining the model itself.
With GraphRAG:
Business data is organised as connected entities (orders, documents, events, decisions)
Relationships provide context automatically
AI retrieves only relevant, connected facts before answering
This means:
The LLM stays general-purpose
Context comes from your data graph
Answers are grounded, not guessed
No fine-tuning required.
How AX Trace Uses GraphRAG for Trusted Answers
AX Trace applies GraphRAG to real business workflows:
Orders → production → shipment
Documents → approvals → decisions
Data → context → explainable outcomes
When a user asks a question, AX Trace:
Retrieves the relevant graph context
Passes structured, traceable facts to the LLM
Produces an answer that can be explained and proven
The result is AI that:
Understands your business logic
Answers with evidence
Avoids hallucination by design
Why This Is Economically Viable for SMEs
GraphRAG delivers enterprise-grade results without enterprise-grade cost.
Compared to fine-tuning:
No model retraining cycles
No massive datasets required
No ongoing tuning cost as data changes
SMEs get:
Accurate, contextual answers
Traceable decision paths
Lower operational risk
Predictable AI cost
This is one of the most overlooked AI benefits:
AI that scales economically because structure, not model size, does the heavy lifting.
The Real Takeaway
Getting value from AI isn’t about buying the most advanced model.
It’s about ensuring AI has the right context at the right time.
GraphRAG enables that.
AX Trace operationalises it.
👉 Explore how AX Trace uses GraphRAG to deliver trusted, explainable AI—without the cost of fine-tuning.
https://www.axtrace.ai
AI doesn’t need to be retrained to give better answers.
It needs to be connected.
What is GraphRAG?
GraphRAG (Graph-based Retrieval Augmented Generation) is an approach that improves AI answers by grounding them in a knowledge graph, ensuring responses are based on connected, verified business context rather than guesses.
How is GraphRAG different from traditional RAG?
Traditional RAG retrieves chunks of text. GraphRAG retrieves connected entities and relationships, giving AI structured context such as how data, documents, and decisions relate to each other.
Why does GraphRAG produce more accurate AI answers?
GraphRAG reduces hallucination by providing AI with relevant, linked facts before generating an answer, so responses are grounded in real business data instead of probabilities.
Do SMEs need to fine-tune large language models when using GraphRAG?
No. GraphRAG improves answer quality through better context, not model retraining. This avoids the high cost and complexity of fine-tuning large language models.
Why is GraphRAG more cost-effective for SMEs?
GraphRAG avoids ongoing retraining costs, large labelled datasets, and specialist AI teams. SMEs get accurate, explainable AI using existing data structures.
How does AX Trace use GraphRAG?
AX Trace uses GraphRAG to connect business data, documents, and decisions into a traceable graph, enabling AI to deliver answers that can be explained and proven.