R&D & CAPABILITY DEMONSTRATIONS

Systems we've built — the same architectures we bring to clients

From RAG-based AI agents to production ML pipelines with full CI/CD — these are working systems, not concepts.

Open Technical Proof: Working code, MLOps setups, and architectural benchmarks engineered by Senior AI Engineer Rinku Diwakar. Access all outsource & team repos on the Pradrix GitHub Organization ↗.

AI Agents / RAGView on GitHub

SkillGap AI

Problem

Manually comparing a resume against a job description to gauge true fit is slow and inconsistent.

Approach

Built a retrieval-augmented generation (RAG) system using semantic embeddings and vector search to measure candidate-role fit, identify skill gaps, and generate tailored recommendations.

Client Capability Proven

Demonstrates the same RAG + vector-search architecture Pradrix uses to build internal knowledge agents and AI-assisted decision tools for clients.

LLMsRAGLangChainVector DBPython

AI Deployment / MLOpsView on GitHub

MovieSentiment (Production ML System)

Problem

AI models that work in a notebook often fail to reach production reliably.

Approach

Built a full-stack ML system with a complete MLOps pipeline — experiment tracking, containerization, CI/CD, and deployment to AWS EKS with live monitoring.

Client Capability Proven

Proves the ability to take an AI system from prototype to a monitored, production-grade deployment — the same rigor applied to client AI integrations.

MLflowDVCDockerKubernetesAWS

Workflow AutomationView on GitHub

Vehicle Insurance ML Pipeline

Problem

Insurance data workflows involve repetitive manual steps across intake, scoring, and reporting.

Approach

Built an end-to-end automated data pipeline with model tracking and versioning to standardize and streamline the workflow.

Client Capability Proven

Mirrors the type of workflow-automation architecture Pradrix designs for operations-heavy businesses — the same category as Purchase Orders, Claim Audits, and Inventory Sync automations.

Scikit-learnMLflowDVCMongoDB

AI Integration / APIView on GitHub

Bike Price Prediction System

Problem

Predictive models are only useful once they're accessible to the systems that need them.

Approach

Built a regression model with feature engineering, served through a Flask API for real-time predictions.

Client Capability Proven

Shows the API-first delivery pattern Pradrix uses to integrate AI predictions directly into clients' existing custom software.

XGBoostFlaskAWS

Industrial AutomationView on GitHub

Fault Detection Pipeline

Problem

Identifying faults from raw sensor/oscilloscope data manually doesn't scale.

Approach

Built an end-to-end ML pipeline that cleans raw signal data, extracts features, and classifies faults automatically.

Client Capability Proven

Demonstrates applicability to predictive-maintenance and industrial-automation use cases.

Signal ProcessingPythonML Pipeline

Smart AutomationView on GitHub

Kavach

Problem

Physical access systems are often static and unintelligent.

Approach

Built a voice-detection-triggered smart door unlock system.

Client Capability Proven

Demonstrates automation thinking extending beyond software into connected/IoT systems for custom internal systems.

PythonVoice DetectionAutomation

Need a similar AI system deployed on your infrastructure?

Schedule a 1-on-1 architecture call with Senior AI Engineer Rinku Diwakar to bring these exact production patterns to your organization.

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