A research initiative for evaluating the reliability, accuracy, and performance of LLMs against real-world business data.
Interface studyA framework for evaluating reliability and performance in enterprise-facing LLM systems.
Overview
A useful system starts with a clear reason to exist.
01 / What was built
An evaluation direction for testing LLM reliability against real-world business data.
02 / Who it was for
Enterprise teams that need evidence before putting AI in front of customers.
03 / Why it mattered
Confidence, relevance, and hallucination risk need a measurable framework, not a promise.
Business challenge
The question was not what technology to use. It was what needed to work better.
Enterprise teams need stronger ways to assess model accuracy, hallucination risk, and retrieval quality before deploying customer-facing AI systems.
Plumfind role / research, strategy, design, engineering, deployment, and support planning.
The biggest challenge in AI is not intelligence.It is confidence.
Discovery & research
Reduce uncertainty before production.
01
Business context
Enterprise teams need stronger ways to assess model accuracy, hallucination risk, and retrieval quality before deploying customer-facing AI systems.
02
Research
We reviewed evaluation literature, RAG assessment approaches, hallucination methods, and the gaps between academic benchmarks and commercial product data.
03
Experience direction
The framework treats validation as a clear evidence trail, combining metrics, source data, enriched inputs, and repeatable review paths.
04
Production plan
The work remains in active research, with iterative expansion of datasets, benchmarking protocols, and scalable evaluation pipelines.
Research artefact
Clarity lives in the details.
We reviewed evaluation literature, RAG assessment approaches, hallucination methods, and the gaps between academic benchmarks and commercial product data.
Evaluation matrix
Dimension
Question
Evidence
Answer relevance
Does the answer match the intent?
Retrieved context
Hallucination
Is the claim supported?
Source comparison
Context use
Was available data used well?
RAG assessment
Solution
What Plumfind designed and built.
The framework treats validation as a clear evidence trail, combining metrics, source data, enriched inputs, and repeatable review paths.
01
Evidence trail
The framework combines source data, enriched inputs, metrics, and repeatable review paths for validation.
02
Multimodal product records
OCR-enriched e-commerce data gives models more grounded material to evaluate against.
03
Benchmarking foundation
Baseline tests and retrieval-focused measurements establish a direction for scaled evaluation.
Architecture
The system is a set of deliberate connections.
Each layer was selected to support the business workflow, not to make the technical picture more complicated.
01Business data
02Evaluation protocol
03Evidence comparison
04Research pipeline
01
Business data
Catalog records, source content, and OCR enrichment form the testable input layer.
02
Evaluation protocol
Baseline LLM tests and RAG metrics create a repeatable review path.
03
Evidence comparison
Outputs are checked for relevance, context use, and unsupported claims.
04
Research pipeline
AWS-backed experimentation supports iterative datasets and benchmarking over time.
Implementation
From considered direction to a production system.
01
Experience and system design
The framework treats validation as a clear evidence trail, combining metrics, source data, enriched inputs, and repeatable review paths.
02
Engineering
We built an evaluation-ready e-commerce dataset, baseline LLM tests, OCR-enriched product records, and retrieval-focused measurement layers.
03
Deployment and ownership
The work remains in active research, with iterative expansion of datasets, benchmarking protocols, and scalable evaluation pipelines.
Business impact
What changed because the system became clearer.
01
Established an enterprise-relevant evaluation direction
02
Created multimodal product representations
03
Documented baseline model behavior
04
Built foundations for scaled benchmarking
Lessons learned
The strongest AI systems are validated before production
More context can improve evaluation fidelity
Confidence needs a measurable framework
Technologies used
Selected for the work they needed to do.
Technology choices were made around fit, maintainability, and the operating reality of the project.
01
Evaluation layer
LLM evaluation, RAG metrics, benchmarking
Selected to turn reliability, relevance, and hallucination risk into evidence that can be reviewed.
02
Data enrichment
OCR enrichment, Shopify data
Selected to test how richer real-world product representations affect evaluation quality.
03
Research infrastructure
AWS EC2
Selected to support repeatable experiments and the next stages of scaled benchmarking.
