An AI-powered medical support platform that combines Retrieval-Augmented Generation (RAG), validated memory, local LLMs and evaluation tooling to help healthcare professionals troubleshoot medical equipment faster and more accurately.
Built an AI-powered troubleshooting assistant that combines Retrieval-Augmented Generation, local LLM inference, validated memory and structured evaluation tooling.
A glimpse of the platform
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Upload PDF manuals and process them into searchable knowledge.
Ask technical questions with configurable retrieval settings.
Store expert-approved answers for future reuse.
Configure evaluation runs comparing Plain LLM, RAG and RAG Memory.
Review generated answers, latency and evaluation metrics.
Compare different AI strategies side by side.
Everything in one platform
Upload technical manuals and process them into structured searchable knowledge.
Long manuals are transformed into retrieval-friendly chunks for grounded answers.
Questions are matched against the most relevant technical content using embeddings.
Answers are generated using retrieved evidence instead of relying on raw model memory.
Previously confirmed technical solutions can be stored and reused across future queries.
The platform supports local inference through LM Studio for private deployment setups.
Alternative answering flows can be compared when local and remote strategies differ.
Structured evaluation compares Plain LLM, RAG and memory-enhanced pipelines.
Experiments can be inspected side by side for latency, quality and grounding behavior.
Generated answers can be reviewed with confidence-oriented signals and traceability.
Groundedness, hallucination and relevance can be assessed through human review.
The platform supports experiment tracking and benchmark-driven iteration.
The system needed to minimize hallucinations while still retrieving enough context for accurate technical guidance.
Previously confirmed troubleshooting solutions had to remain reusable without polluting future answers.
Retrieval confidence needed careful tuning to preserve both response quality and groundedness.
The platform had to work with local models through LM Studio instead of relying only on cloud providers.
Grounded document retrieval improves reliability for medical technical troubleshooting.
Previously confirmed answers can be stored and reused to improve future assistance.
Technical manuals become easier to query and navigate through semantic retrieval.
The platform supports dissertation-style comparisons across multiple AI answer strategies.
The system is ready to extend toward larger medical datasets and future experiments.
Let's build intelligent applications using RAG, LLMs and modern AI architectures.
Built for real users and real-world impact.
Designed around evaluation, benchmarking and grounded iteration.
Structured backend logic for reliable AI workflows.
RAG, embeddings and local LLMs combined in one workflow.