Building an AI-Powered Claim Verification System with Cloud-Native Architecture
Learn how I designed ClaimGraph, an AI-powered system that verifies claims against research papers using retrieval, NLP, semantic embeddings, stance classification, and geometric verification.
With the rapid growth of information available online, verifying whether a claim is supported by reliable research has become increasingly difficult.
To address this problem, I designed ClaimGraph, an AI-powered claim verification system that retrieves relevant research papers, analyzes their evidence, classifies their stance toward a claim, and produces an explainable verification result.
The project combines artificial intelligence, natural language processing, semantic search, vector databases, and cloud-oriented architecture.
The Problem
A user may enter a claim such as:
"COVID-19 vaccines significantly reduce the risk of severe disease."
A simple keyword search can return thousands of papers, but finding the papers that actually provide evidence for or against the claim is much harder.
The system therefore needs to answer several questions:
Which research papers are relevant to the claim?
What evidence exists inside those papers?
Does the evidence support or contradict the claim?
How strong is the evidence?
Are different papers consistent with each other?
How confident should the final verdict be?
ClaimGraph Architecture
The system follows a multi-stage pipeline:
User Claim → Claim Reformulation → Research Retrieval → Document Processing → Semantic Embedding → Vector Search → Stance Classification → Geometric Verification → Confidence Calibration → Final Verdict
The frontend is designed as a React application, while the backend exposes APIs for processing claims and coordinating the verification pipeline.
The architecture can be divided into several major components.
1. Claim Processing
The user enters a natural-language claim.
An LLM-based reformulation step converts the input into a clearer declarative claim that can be used for retrieval and evidence analysis.
For example:
Input:
"Does vaccination help prevent severe COVID-19?"
Reformulated claim:
"COVID-19 vaccination reduces the risk of severe disease."
This makes downstream retrieval and classification more consistent.
2. Research Paper Retrieval
The system can retrieve research papers from multiple scholarly sources, including:
Semantic Scholar
OpenAlex
arXiv
Crossref
Using multiple sources reduces dependence on a single academic database.
The retrieved papers are filtered and processed before being added to the verification pipeline.
3. Document Processing
Research papers may be available in PDF format.
A document processing stage extracts useful textual content from papers so that individual sections and chunks can be analyzed.
The extracted content is divided into smaller evidence chunks.
These chunks can then be represented using semantic embeddings.
4. Semantic Embeddings
Instead of relying only on keyword matching, the system represents claims and evidence chunks as numerical vectors.
This allows semantically similar content to be discovered even when the exact words are different.
For example:
"Vaccination lowers hospitalization risk."
and
"Immunized individuals are less likely to require hospital treatment."
may use different words while expressing closely related information.
A vector database can be used to efficiently search these representations.
5. Stance Classification
Relevant evidence is classified according to its relationship with the claim.
The main categories are:
Support
Refute
No Stance
A transformer-based natural language inference model can be used for this stage.
In my implementation, the architecture uses a DeBERTa-based classifier for stance analysis.
This provides an important layer between simple semantic similarity and actual claim verification.
6. Geometric Verification
One of the interesting parts of the system is the geometric verification layer.
The system represents evidence in an embedding space and calculates distances between the claim representation and evidence representations.
The implementation uses dimensionality reduction and covariance estimation before calculating a Mahalanobis-based distance.
A shared PCA transformation is used to reduce dimensionality, followed by Ledoit-Wolf covariance estimation.
The resulting distances help determine which evidence is most consistent with the claim.
7. Ambiguity Detection
A verification system should not always force an answer.
If the evidence supporting and contradicting a claim is too close, the system treats the result as ambiguous.
A margin-based decision mechanism is therefore used.
If the difference between competing evidence groups is sufficiently small, the system can perform additional retrieval or analysis instead of immediately producing a confident verdict.
This is important because real-world scientific evidence is often incomplete or conflicting.
8. Confidence Calibration
The verification margin is converted into a calibrated confidence score.
The final result can therefore contain information such as:
Verdict: Supported
Confidence: High
rather than simply returning a binary answer.
Cloud-Native Architecture
A production version of the system can be deployed using cloud infrastructure.
A possible architecture is:
User
|
v
React Web Application
|
v
API Gateway
|
v
Backend API Service
/ \
/ \
v v
Task Queue Redis
|
v
Worker Services
/ | \
/ | \
v v v
Retrieval NLP Verification
| | |
v v v
Academic Models Vector DB
APIs
|
v
Research Papers
Cloud services can be introduced according to workload requirements.
For example:
Object storage for documents and generated artifacts
Managed databases for metadata
Containerized services for backend components
Queue-based processing for long-running tasks
Monitoring services for application health and performance
Load balancing for horizontally scalable APIs
The exact AWS services can be selected based on expected traffic, cost, latency, and operational requirements.
Why Cloud Architecture Matters
AI applications often contain workloads that are unsuitable for a single monolithic server.
Research-paper retrieval, PDF processing, embedding generation, vector search, and NLP inference can consume significant CPU, memory, and storage resources.
Separating these workloads allows individual components to scale independently.
For example, if document processing becomes the bottleneck, additional workers can be added without scaling the entire application.
This is one of the major advantages of designing the system as independent services.
Lessons Learned
Building this project helped me understand that an AI application is much more than a machine-learning model.
A practical AI system requires:
Data ingestion
API integration
Document processing
Model inference
Vector search
Database design
Asynchronous processing
Error handling
Monitoring
Scalability
Explainability
The model is only one component of the overall system.
Future Improvements
There are several areas where ClaimGraph can be improved.
1. Better Evidence Ranking
More advanced ranking techniques can be used to identify the strongest evidence for a claim.
2. Source Quality Scoring
Research papers could be scored based on factors such as publication venue, citation information, methodology, and recency.
3. Distributed Processing
Large-scale document processing could be distributed across multiple workers.
4. Better Explainability
The system could highlight the exact sentences or paragraphs responsible for the final verdict.
5. Production Monitoring
Cloud monitoring and observability can be introduced to track latency, failures, model performance, and resource utilization.
Conclusion
ClaimGraph demonstrates how AI, NLP, semantic retrieval, and cloud-native architecture can be combined to build a practical research verification system.
The most important lesson from the project is that building reliable AI applications requires thinking beyond the model itself.
A successful production system needs a complete pipeline covering data ingestion, processing, inference, storage, scalability, monitoring, and explainability.
This project is an ongoing exploration of how cloud technologies and AI can work together to make evidence-based information verification more accessible.
To address this problem, I designed ClaimGraph, an AI-powered claim verification system that retrieves relevant research papers, analyzes their evidence, classifies their stance toward a claim, and produces an explainable verification result.
The project combines artificial intelligence, natural language processing, semantic search, vector databases, and cloud-oriented architecture.
The Problem
A user may enter a claim such as:
"COVID-19 vaccines significantly reduce the risk of severe disease."
A simple keyword search can return thousands of papers, but finding the papers that actually provide evidence for or against the claim is much harder.
The system therefore needs to answer several questions:
Which research papers are relevant to the claim?
What evidence exists inside those papers?
Does the evidence support or contradict the claim?
How strong is the evidence?
Are different papers consistent with each other?
How confident should the final verdict be?
ClaimGraph Architecture
The system follows a multi-stage pipeline:
User Claim → Claim Reformulation → Research Retrieval → Document Processing → Semantic Embedding → Vector Search → Stance Classification → Geometric Verification → Confidence Calibration → Final Verdict
The frontend is designed as a React application, while the backend exposes APIs for processing claims and coordinating the verification pipeline.
The architecture can be divided into several major components.
1. Claim Processing
The user enters a natural-language claim.
An LLM-based reformulation step converts the input into a clearer declarative claim that can be used for retrieval and evidence analysis.
For example:
Input:
"Does vaccination help prevent severe COVID-19?"
Reformulated claim:
"COVID-19 vaccination reduces the risk of severe disease."
This makes downstream retrieval and classification more consistent.
2. Research Paper Retrieval
The system can retrieve research papers from multiple scholarly sources, including:
Semantic Scholar
OpenAlex
arXiv
Crossref
Using multiple sources reduces dependence on a single academic database.
The retrieved papers are filtered and processed before being added to the verification pipeline.
3. Document Processing
Research papers may be available in PDF format.
A document processing stage extracts useful textual content from papers so that individual sections and chunks can be analyzed.
The extracted content is divided into smaller evidence chunks.
These chunks can then be represented using semantic embeddings.
4. Semantic Embeddings
Instead of relying only on keyword matching, the system represents claims and evidence chunks as numerical vectors.
This allows semantically similar content to be discovered even when the exact words are different.
For example:
"Vaccination lowers hospitalization risk."
and
"Immunized individuals are less likely to require hospital treatment."
may use different words while expressing closely related information.
A vector database can be used to efficiently search these representations.
5. Stance Classification
Relevant evidence is classified according to its relationship with the claim.
The main categories are:
Support
Refute
No Stance
A transformer-based natural language inference model can be used for this stage.
In my implementation, the architecture uses a DeBERTa-based classifier for stance analysis.
This provides an important layer between simple semantic similarity and actual claim verification.
6. Geometric Verification
One of the interesting parts of the system is the geometric verification layer.
The system represents evidence in an embedding space and calculates distances between the claim representation and evidence representations.
The implementation uses dimensionality reduction and covariance estimation before calculating a Mahalanobis-based distance.
A shared PCA transformation is used to reduce dimensionality, followed by Ledoit-Wolf covariance estimation.
The resulting distances help determine which evidence is most consistent with the claim.
7. Ambiguity Detection
A verification system should not always force an answer.
If the evidence supporting and contradicting a claim is too close, the system treats the result as ambiguous.
A margin-based decision mechanism is therefore used.
If the difference between competing evidence groups is sufficiently small, the system can perform additional retrieval or analysis instead of immediately producing a confident verdict.
This is important because real-world scientific evidence is often incomplete or conflicting.
8. Confidence Calibration
The verification margin is converted into a calibrated confidence score.
The final result can therefore contain information such as:
Verdict: Supported
Confidence: High
rather than simply returning a binary answer.
Cloud-Native Architecture
A production version of the system can be deployed using cloud infrastructure.
A possible architecture is:
User
|
v
React Web Application
|
v
API Gateway
|
v
Backend API Service
/ \
/ \
v v
Task Queue Redis
|
v
Worker Services
/ | \
/ | \
v v v
Retrieval NLP Verification
| | |
v v v
Academic Models Vector DB
APIs
|
v
Research Papers
Cloud services can be introduced according to workload requirements.
For example:
Object storage for documents and generated artifacts
Managed databases for metadata
Containerized services for backend components
Queue-based processing for long-running tasks
Monitoring services for application health and performance
Load balancing for horizontally scalable APIs
The exact AWS services can be selected based on expected traffic, cost, latency, and operational requirements.
Why Cloud Architecture Matters
AI applications often contain workloads that are unsuitable for a single monolithic server.
Research-paper retrieval, PDF processing, embedding generation, vector search, and NLP inference can consume significant CPU, memory, and storage resources.
Separating these workloads allows individual components to scale independently.
For example, if document processing becomes the bottleneck, additional workers can be added without scaling the entire application.
This is one of the major advantages of designing the system as independent services.
Lessons Learned
Building this project helped me understand that an AI application is much more than a machine-learning model.
A practical AI system requires:
Data ingestion
API integration
Document processing
Model inference
Vector search
Database design
Asynchronous processing
Error handling
Monitoring
Scalability
Explainability
The model is only one component of the overall system.
Future Improvements
There are several areas where ClaimGraph can be improved.
1. Better Evidence Ranking
More advanced ranking techniques can be used to identify the strongest evidence for a claim.
2. Source Quality Scoring
Research papers could be scored based on factors such as publication venue, citation information, methodology, and recency.
3. Distributed Processing
Large-scale document processing could be distributed across multiple workers.
4. Better Explainability
The system could highlight the exact sentences or paragraphs responsible for the final verdict.
5. Production Monitoring
Cloud monitoring and observability can be introduced to track latency, failures, model performance, and resource utilization.
Conclusion
ClaimGraph demonstrates how AI, NLP, semantic retrieval, and cloud-native architecture can be combined to build a practical research verification system.
The most important lesson from the project is that building reliable AI applications requires thinking beyond the model itself.
A successful production system needs a complete pipeline covering data ingestion, processing, inference, storage, scalability, monitoring, and explainability.
This project is an ongoing exploration of how cloud technologies and AI can work together to make evidence-based information verification more accessible.
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