WizJobs Enterprise

Production-Scale Hybrid AI Job Recommendation & Talent Intelligence Platform

Project Overview

WizJobs Enterprise is a production-grade AI-powered job recommendation platform designed to intelligently match candidates with relevant job opportunities using hybrid recommendation algorithms and semantic understanding.

Unlike traditional keyword-based job portals, this system leverages deep learning embeddings, collaborative behavior modeling, and advanced ranking strategies to deliver highly personalized, context-aware, and scalable recommendations.

Problem Statement

• Keyword-based matching produces irrelevant results.
• Traditional systems fail to understand resume semantics.
• Cold-start problem for new users and new job listings.
• No intelligent ranking combining behavior + content.
• Lack of personalization at scale.

Enterprise Solution Architecture

The system is architected as a modular AI platform with separate pipelines for data ingestion, feature engineering, embedding generation, collaborative modeling, and hybrid ranking.

Core Components

• Resume Parsing & Skill Extraction Engine
• SBERT-based Semantic Embedding Pipeline
• Vector Similarity Search Engine
• User-Interaction Matrix for Collaborative Filtering
• Hybrid Weighted Ranking Module
• Analytics & Feedback Loop Optimization

AI & Machine Learning Engine

Content-Based Filtering

• Sentence-BERT Embeddings
• Cosine Similarity Scoring
• TF-IDF Baseline Comparisons
• Semantic Job-Resume Matching

Collaborative Filtering

• User-Job Interaction Matrix
• Implicit Feedback Modeling (clicks, saves, applications)
• Similarity-Based User Clustering

Hybrid Ranking Strategy

Final ranking score is computed using a weighted combination of:

• Semantic Similarity Score
• Collaborative Behavior Score
• Recency Boost
• Popularity Score
• User Preference Weighting

Production-Level Engineering

• Django-based RESTful backend APIs
• PostgreSQL optimized relational database
• Vector indexing for fast similarity search
• Docker containerization
• Nginx reverse proxy configuration
• AWS EC2 cloud deployment
• Secure environment configuration

Real-World Use Case

A Data Analyst uploads a resume containing SQL, Power BI, ETL, and Python skills.

Instead of simple keyword matching, the system:

1. Generates semantic embeddings of the resume.
2. Compares against vectorized job descriptions.
3. Analyzes behavioral data from similar users.
4. Applies hybrid ranking logic.
5. Returns personalized, context-aware job suggestions.

Scalability & Enterprise Impact

• Handles large job datasets efficiently using vector indexing.
• Supports real-time recommendation generation.
• Cold-start mitigation strategies implemented.
• Designed for integration with enterprise HR systems.

Technology Stack

Python • Django • PostgreSQL • SBERT • Scikit-learn • NumPy • Pandas • Docker • AWS EC2 • Nginx • HTMX • Alpine.js • Tailwind CSS

Project Impact

This project demonstrates the integration of advanced recommendation algorithms with full-stack production engineering, showcasing expertise in AI system design, backend architecture, cloud deployment, and intelligent ranking systems.

MYSH Unix Shell

POSIX-Style Command Line Shell Built in C

Summary

MYSH is a compact POSIX-style Unix command-line shell implemented in C. It provides a functional set of shell capabilities including command parsing, pipelines, input/output redirection, job control, and builtin commands.

The shell was designed to demonstrate core systems programming concepts such as process creation, signal handling, process groups, and file descriptor management. MYSH closely follows the behavior of traditional Unix shells, making it useful both as an educational tool and as a practical demonstration of low-level operating system concepts.

Key Features

• Command Parsing with tokenization and basic quoting support.
• Pipelines supporting multi-stage command chaining using the pipe operator |.
• Redirection support including <, >, >>, numeric file descriptor redirection (2>file) and descriptor duplication (2>&1).
• Job Control for background processes using & with job tracking and control commands.
• Built-in Commands including cd, pwd, exit, echo, alias, unalias, type, help, export, set, unset, and history.
• Execution Engine implementing fork/exec with correct process-group handling and foreground/background execution semantics.

Design & Architecture

MYSH follows a classic Unix shell architecture with modular components responsible for parsing commands, executing processes, and managing jobs.

• REPL Layer collects logical command lines and supports multi-line quoted inputs.
• Tokenizer & Parser convert raw input into a small command pipeline AST.
• Executor manages pipes, process creation, file descriptor redirection, and builtin execution.
• Job Control System tracks background jobs and safely reaps child processes using SIGCHLD.

Runtime Execution Flow

1. Read input from the user and construct a logical command line.
2. Tokenize the command while respecting quoted strings and escape sequences.
3. Build pipeline structures where each stage represents a command execution unit.
4. Create pipes, fork processes, configure redirections, and execute commands.
5. Parent process manages foreground/background execution and updates the job table.

Core Data Model

Internally, MYSH relies on structured representations for commands and job management.

• command_t — represents a pipeline stage containing arguments, redirection settings, and pipeline links.
• job_t — tracks job metadata including job ID, process group ID, command string, child PIDs, state, and exit status.

Built-in Commands

MYSH implements several built-in commands commonly found in Unix shells. Commands that modify shell state such as cd or export are executed in the parent process, while other builtins can run inside pipeline child processes when required.

Redirections are applied transparently so builtins behave consistently with external commands.

Example Commands

# basic command
echo hello

# pipeline example
/usr/bin/printf "a\nb\nc\n" | /usr/bin/grep b | /usr/bin/wc -l

# file redirection
echo redir_test > /tmp/mysh_test1

# background job
sleep 10 &
jobs
fg %1

Build & Execution

# build project
make

# start interactive shell
./bin/mysh

# run scripted commands
printf 'echo hello' | ./bin/mysh

Future Improvements

• Enhanced variable and word expansion.
• Persistent command history with line editing.
• Support for here-documents.
• Improved terminal foreground control using tcsetpgrp.

Detailed documentation, build instructions, and technical examples are provided in the project documentation.

DocumentBot

Enterprise Information Retrieval & AI Question Answering Platform

Summary

DocumentBot is an enterprise-grade information retrieval and question-answering system designed to help users search, explore, and extract answers from large collections of documents including PDFs, Word files, HTML pages, and scanned documents.

The platform combines traditional lexical search techniques such as BM25, TF-IDF, Boolean search, and fuzzy matching with modern dense retrieval using semantic embeddings and vector similarity search. Optional neural rerankers and Retrieval-Augmented Generation (RAG) allow the system to generate contextual answers with citations.

Users can explicitly choose their preferred retrieval strategy or allow the system to dynamically select a hybrid retrieval method that maximizes both precision and recall.

Real-World Use Cases

• Support Knowledge Base — helpdesk agents instantly locate procedures, troubleshooting steps, and code snippets within large product manuals.
• Legal Discovery — identify relevant clauses across contracts using boolean filters and fuzzy matching combined with semantic search.
• Research Assistant — extract summaries, citations, and passages from research papers for literature reviews.
• Enterprise Document Search — perform unified semantic and metadata search across manuals, SOPs, incident logs, and internal documentation.

Key Platform Features

• Multi-Method Retrieval — lexical search (BM25, TF-IDF), boolean queries, fuzzy search, and semantic embedding-based retrieval.
• Vector Indexing using high-performance vector databases such as FAISS, Milvus, and Pinecone with ANN indexing strategies.
• Hybrid Search Engine combining lexical and semantic similarity scores with configurable weighting.
• Neural Reranking using cross-encoder models to reorder top candidate results for higher precision.
• Answer Extraction & RAG retrieving relevant passages and generating concise answers with source citations.
• Query Intelligence including autocomplete, spell correction, synonym expansion, and query suggestions.
• User Profiles & Retrieval Modes allowing users to select fast, semantic, hybrid, or advanced boolean search modes.
• Robust Document Ingestion pipeline supporting PDFs, DOCX, HTML, and OCR for scanned documents.
• Monitoring & Analytics including query latency tracking, search analytics, and relevance feedback loops.

Algorithms & Retrieval Methods

The platform integrates a wide spectrum of classical and modern information retrieval techniques.

• Lexical Retrieval — TF-IDF vectorization, BM25 ranking, boolean queries, and fuzzy matching using edit distance.
• Dense Retrieval — sentence and paragraph embeddings using SBERT and sentence-transformer models.
• Approximate Nearest Neighbor Search using HNSW, IVF, and PQ indexing techniques.
• Neural Rerankers — cross-encoder models improving ranking precision for top candidate passages.
• Hybrid Ranking combining lexical and semantic scores using configurable weighting strategies.
• Answer Extraction — deterministic span extraction or LLM-generated responses with provenance citations.
• Query Expansion through synonym dictionaries, pseudo-relevance feedback, and semantic expansion.

System Architecture

DocumentBot follows a modular pipeline architecture that separates ingestion, indexing, retrieval, and serving components.

1. Ingestion — extract text and metadata from PDFs, DOCX, and HTML documents with optional OCR for scanned files.
2. Indexing — build both lexical indexes (Elasticsearch/Whoosh) and dense vector indexes (FAISS/Milvus/Pinecone).
3. Query Processing — normalize and preprocess queries before executing lexical and dense retrieval methods in parallel.
4. Ranking & Extraction — merge results, optionally rerank using neural models, and extract answer passages.
5. Serving Layer — REST APIs and interactive UI displaying highlighted snippets with source document references.

Indexing Strategy

• Documents are split into 200–800 token chunks with overlapping context windows.
• Both paragraph-level and sliding-window chunk representations are stored for better semantic coverage.
• Metadata fields such as title, author, date, and tags are indexed for filtering and faceted search.

User Experience

• Users can search using natural language questions.
• Select retrieval mode: Fast (Lexical), Semantic, Hybrid, or Advanced Boolean.
• View highlighted passages with relevance scores and citations linking back to the source document.
• Provide feedback to improve future ranking performance.

Example Query

Q: How do I reset the device to factory settings?

Top Result:
"To perform a factory reset, press and hold the reset button for 10 seconds..."

Source: manual_v2.pdf — Page 12
Score: 0.92

Evaluation & Metrics

• Offline metrics such as MRR, nDCG@K, and Precision@K on annotated evaluation datasets.
• Online monitoring including click-through rate, user satisfaction signals, and latency metrics.
• A/B testing comparing hybrid ranking against purely lexical retrieval baselines.

Build & Run

# Setup environment
python -m venv .venv
source .venv/bin/activate
pip install -r requirements.txt

# Run ingestion pipeline
python ingest.py --source ./data/manuals --index dense --chunksize 500

# Start API server
uvicorn app.main:app --reload --host 0.0.0.0 --port 8000

Production Considerations

• Vector databases can scale independently using sharding and replicas.
• Lexical search results can be cached to reduce query latency.
• Secure authentication and role-based access control protect private document collections.
• Access logs and provenance tracking support compliance and auditing requirements.

Future Improvements

• Multilingual embeddings and cross-language retrieval.
• Online learning using user feedback to optimize ranking models.
• Automatic summarization and exportable citations for downstream workflows.

DocumentBot is designed as a modular, production-ready information retrieval platform suitable for enterprise search, legal discovery, compliance analysis, and support automation systems.

AI-Powered Lead Generation & Outreach Automation System (n8n + Python + GHL)

End-to-End Automated Pipeline for Lead Scraping, Enrichment, Qualification & Multi-Channel Outreach

Project Overview

This project is a fully automated AI-powered lead generation and outreach system built using n8n, custom Python data pipelines, and GoHighLevel (GHL) CRM integration.

The system continuously discovers high-quality business leads, enriches them with actionable intelligence, qualifies them using AI scoring models, and automatically launches personalized outreach campaigns across email and CRM pipelines — all without manual intervention.

Problem Statement

• Manual lead generation is slow, inconsistent, and non-scalable.
• Raw scraped data lacks context (emails, decision-makers, company insights).
• No intelligent filtering → low-quality leads waste outreach efforts.
• Cold outreach lacks personalization → low response rates.
• No centralized automation connecting scraping → AI → CRM → outreach.

System Architecture (End-to-End Flow)

1. Lead Source Discovery → Google Maps, LinkedIn, directories, APIs
2. Data Scraping → Automated extraction using Python services
3. Data Enrichment → Emails, company info, tech stack, decision-makers
4. AI Qualification → LLM-based scoring & filtering
5. CRM Sync (GoHighLevel) → Lead storage & pipeline automation
6. Automated Outreach → Personalized email + follow-ups

Lead Scraping & Data Acquisition

We built custom Python scraping services integrated with n8n workflows to collect business leads from multiple sources:

• Google Maps scraping (business name, ratings, location, category)
• LinkedIn company & profile extraction
• Business directories & niche listing platforms
• Public APIs for structured lead data

n8n orchestrates scraping jobs using HTTP Request nodes and triggers Python microservices via REST APIs or webhook nodes.

Data Enrichment Pipeline (Python Services)

Raw leads are enriched using custom-built Python scripts and APIs:

• Email discovery (pattern-based + enrichment APIs)
• Domain extraction & validation
• Company metadata (size, niche, tech stack)
• Decision-maker identification
• Data cleaning, deduplication & normalization

The enrichment pipeline ensures every lead is actionable before entering the AI qualification stage.

AI-Powered Lead Qualification (n8n + LLM)

We integrated AI decision-making directly into n8n workflows using LLM-powered nodes:

• Lead scoring based on business relevance
• Intent classification (high/medium/low value leads)
• Industry & use-case matching
• Custom prompts for filtering ICP (Ideal Customer Profile)

Only high-quality leads pass through the pipeline, ensuring maximum ROI for outreach campaigns.

GoHighLevel (GHL) CRM Integration

• Automated lead creation via GHL APIs
• Pipeline stage assignment (New → Qualified → Contacted)
• Tag-based segmentation
• Custom fields mapping (industry, score, source)
• Trigger-based workflows inside GHL

n8n connects seamlessly with GHL to ensure leads are instantly actionable inside CRM workflows.

Automated Outreach Engine

The system automatically launches personalized outreach campaigns:

• Dynamic email personalization using AI-generated content
• Cold email sequencing with follow-ups
• Conditional logic (open/click/reply triggers)
• Multi-channel support (email + CRM + webhook triggers)

n8n Workflow Design (Core Nodes)

• Webhook triggers for pipeline execution
• HTTP Request nodes for APIs & Python services
• Function nodes for transformation logic
• IF / Switch nodes for decision branching
• AI/LLM nodes for scoring & content generation
• GHL API integration nodes
• Email nodes (SMTP / third-party providers)

Scalability & Production Engineering

• Asynchronous workflow execution
• Batch processing for large datasets
• Error handling & retry mechanisms
• Rate-limiting for APIs
• Modular microservice-based architecture

Project Impact

This system demonstrates deep expertise in automation engineering, AI integration, backend architecture, and real-world business workflow design. It transforms lead generation from a manual, inefficient process into a fully automated, intelligent, and scalable pipeline capable of generating high-quality leads and driving measurable business growth.

AI Customer Support Automation Platform

Intelligent Multi-Channel Support System using n8n, LLMs & CRM Integration

Overview

This project is a fully automated AI-powered customer support system built using n8n workflows, designed to handle incoming customer queries across multiple channels (email, web forms, chat) and respond intelligently using Large Language Models (LLMs).

The system eliminates manual support overhead by automatically classifying, responding, and routing queries while seamlessly integrating with CRM systems (GoHighLevel) to maintain customer context and interaction history.

Real-World Problem Solved

• Delayed customer responses due to manual handling
• Lack of intelligent query classification
• High support team workload
• No centralized tracking of customer interactions
• Inconsistent support quality across channels

Automation Workflow Architecture

The system is built using modular n8n workflows that orchestrate multiple services:

• Trigger Layer: Webhooks, Gmail API, or chat integrations capture incoming queries
• Processing Layer: AI classification and intent detection using OpenAI
• Enrichment Layer: Python services fetch additional context (user history, FAQs)
• Decision Layer: Conditional routing based on query type
• Action Layer: Automated replies, escalation, or CRM updates

AI Processing & Query Handling

• Intent Classification using LLM prompts (billing, technical, general inquiries)
• Entity Extraction (order IDs, names, product references)
• Context-Aware Responses generated dynamically
• Confidence Scoring to decide automation vs escalation

If confidence is high → automated response is sent. If low → query is escalated to a human agent with full context.

Python-Based Data Enrichment

Custom Python microservices were developed to enhance AI decision-making:

• Fetch customer history from database
• Retrieve relevant FAQ answers
• Normalize and clean incoming text
• Perform similarity search for known issues

CRM Integration (GoHighLevel)

• Automatically create/update contacts
• Log all interactions as activities
• Trigger follow-up workflows
• Segment users based on query type

This ensures complete visibility of customer interactions and enables long-term relationship management.

Automation Capabilities

• Instant AI-powered email replies
• Automatic ticket categorization
• Escalation to Slack or internal teams
• Follow-up email automation
• Multi-channel support orchestration

System Flow (Example)

1. User sends support email
2. n8n webhook triggers workflow
3. LLM classifies intent
4. Python service enriches data
5. AI generates contextual response
6. Email reply is sent automatically
7. Interaction logged in GoHighLevel CRM

Impact & Results

• Reduced support response time to near real-time
• Automated handling of repetitive queries
• Improved customer satisfaction
• Scalable support system without increasing team size

This project showcases deep expertise in workflow automation, AI integration, backend services, and CRM orchestration — delivering a production-ready intelligent support system.