Evidence-grounded research assistant that indexes academic documents, retrieves relevant passages, generates cited answers, and preserves source traceability.
How the solution is organized and why the structure matters.
Evidence-grounded research assistant that indexes academic documents, retrieves relevant passages, generates cited answers, and preserves source traceability. The technical blueprint separates responsibilities into clear layers so the solution can be tested, explained, extended, and handed over confidently.
Four clear layers keep responsibilities understandable and maintainable.
Collects structured inputs, validates quality, and prepares consistent features for training or inference.
Separates preprocessing, model execution, evaluation, and versioned experiment artifacts.
Exposes predictions through a controlled service boundary that the interface can consume safely.
Tracks accuracy, failure cases, latency, drift indicators, and reproducibility across model versions.
The practical role of each technology in this project.
Core implementation language for analysis, automation, services, and models.
Attention-based language models for semantic and generative tasks.
Orchestration layer for retrieval, language-model prompts, tools, and workflows.
Similarity search over embeddings for retrieval-augmented AI applications.
Supports the project's AI/ML implementation, integration, or evaluation requirements.
Reproducible application packaging for consistent development and deployment.
A reviewable path from requirements to tested handover.
Confirm users, inputs, outputs, acceptance criteria, platform limits, and delivery scope.
Validate the highest-risk technical decisions with a small working baseline before full implementation.
Build in reviewable modules, connect the selected technologies, and document key decisions.
Test expected and failure paths, review outcomes, and prepare technical documentation and guidance.
Explore more work with similar technical foundations.
Research prototype combining clinical text and medical images to prioritize review queues with uncertainty estimates and explainable model outputs.
Technical Details
Time-series machine learning system that detects equipment anomalies, estimates failure risk, and schedules maintenance from vibration and temperature data.
Technical Details
Streaming risk engine that scores transactions, identifies behavioral anomalies, explains alerts, and supports analyst feedback for continuous improvement.
Technical Details