NexusDoc is a Document Intelligence platform currently under active development that leverages the Gemini AI engine to provide semantic search, automated summarization, and vector-based extraction for PDFs and text files. It is built with a "Security-First" and "QA-First" philosophy, designed to demonstrate senior-level full-stack and AI engineering competence.
In a world of subscription-based AI services, NexusDoc stands out as a Sovereign Intelligence Vault.
- Data Sovereignty: Unlike standard SaaS products, NexusDoc uses a "Bring Your Own Key" (BYOK) architecture, ensuring that the user remains the absolute owner of their data and AI costs.
- Privacy-First: Every document and API key is shielded by AES-256-GCM encryption, ensuring that even in a multi-tenant environment, intelligence remains private.
- Infinite Scalability: By removing the middleman, NexusDoc allows power users and students to scale their document research to thousands of pages with zero service markups.
NexusDoc is designed for the modern "Knowledge Professional" who manages high volumes of unstructured data:
- Academic Researchers: Students and researchers who need to synthesize hundreds of pages of literature into actionable intelligence.
- Legal & Compliance Officers: Professionals who require secure, private indexing of sensitive documents without leaking data to public LLM training sets.
- Technical Writers: Engineers who need to semantically search through large documentation repositories to find technical relationships instantly.
- AI-Powered Summarization: Near-real-time, rich-text insights from complex documents using your private AI configuration.
- Semantic Vector Search: Find information based on meaning, not just keywords, using high-dimensional embeddings.
- Security Hardened: Multi-tier rate limiting, AES-256 encryption for API keys, and JWT-based session management.
- Adaptive Responsive Design: A "True Responsive" UI that restructures itself across Mobile, Tablet, and Desktop viewports.
- DevOps Integrated: Automated CI/CD pipelines with Playwright E2E testing for automated layout verification.
| Landing Page | Authentication Portal |
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| Account Registration |
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Building a functional AI prototype is simple, but building a SaaS-ready intelligence platform requires solving deep architectural frictions. NexusDoc was developed to tackle three specific engineering hurdles:
Challenge: Modern AI models (like Gemini) can output embeddings in varying dimensions (768, 1536, 3072). Mismatching these with a fixed-dimension PostgreSQL vector column causes fatal database crashes.
Solution: Developed an Adaptive Dimension Normalizer in the backend. It utilizes Matryoshka-Style Truncation or padding to align vectors to exactly 768 dimensions, ensuring database stability regardless of the model used.
Challenge: Complex dashboards often "break" on mobile devices, leading to horizontal overflow and "clipping" that frustrates users. Solution: Enforced a strict Restructuring over Shrinking law. Using Vanilla CSS modules and flexbox restructuring, the UI transforms from a multi-pane desktop view into a touch-optimized mobile stack, verified via automated Playwright regression tests.
Challenge: AI summaries are often "locked" in the UI, making it difficult for users to extract and use the intelligence. Solution: Integrated a Summary Utility Toolbox featuring asynchronous "Copy-to-Clipboard" and "Download as TXT" functionality, maximizing user productivity and data portability.
Challenge: Handling AI operational costs for a growing user base while managing the extreme security liability of user-provided API keys. Solution: Implemented a "Bring Your Own Key" (BYOK) architecture. To mitigate security risks, I engineered a Security Vault using AES-256-GCM Encryption. API keys are encrypted the moment they reach the server, stored as ciphertext in the database, and only decrypted in-memory during active AI sessions—ensuring zero plain-text exposure even in the event of a database leak.
Challenge: Document processing platforms are vulnerable to malicious file uploads containing embedded malware (e.g., PDF active scripting or executable macros) that can attack server indices or download-facing host computers. Solution: Engineered a Security Preflight Warning Gate intercepts. Every time an original document download or AI summary text file export is triggered, the system halts execution and forces a security warning modal overlay instructing the developer to carry out the download inside an isolated Virtual Machine (VM/VirtualBox) or sandbox. This protects local host machines from file-embedded payloads.
Challenge: Attackers embedding hidden system-override instructions within document uploads to manipulate downstream LLMs (Indirect Prompt Injection) or inject database parsing commands.
Solution: Enforced a Sterilization boundary layer. The ingestion pipeline strictly cleans extracted strings and wraps retrieved vector chunks within isolated, marked tags (<document_context>) inside LLM prompts, explicitly instructing the AI engine to treat the text as untrusted raw data and block instruction execution. All text content is similarly sanitized to prevent backend SQL/NoSQL command parsing.
Challenge: The rapid pace of AI evolution makes hard-coded model integrations obsolete within months. Solution: Architected a Model-Agnostic Engine. Instead of locking the system to a specific version, NexusDoc allows users to dynamically specify their preferred Gemini generation and embedding models via the UI. The backend's adaptive layer ensures that as Google releases newer, more powerful models, NexusDoc "levels up" instantly without requiring a single line of code change or redeployment.
Challenge: Generating vector embeddings for long documents can introduce significant latency. Processing chunks sequentially results in substantial wall-clock delay due to repeated API round-trips and individual SQL database inserts.
Solution: Refactored the ingestion pipeline to generate embeddings in parallel batches (concurrency level of 5) using Promise.allSettled, followed by a single SQL bulk insert using Drizzle ORM. This optimized pipeline reduced RAG ingestion latencies by up to 80%.
Challenge: Dynamic Next.js client interfaces suffer from page loading and TTFB delays when serving static assets (CSS, icons, font assets) directly from serverless execution instances. Solution: Leveraged Vercel's global edge network (Edge CDN) by implementing strict cache-control header policies for all static assets and pre-rendering static routes. This guarantees sub-10ms delivery of resources and eliminates cold-start overhead for static asset requests.
- Framework: Next.js 16+ (App Router)
- Styling: Vanilla CSS with custom Design Tokens (Modular & Performance-focused)
- State Management: React Hooks + Context API
- QA: Playwright (Cross-browser verification)
- Engine: Node.js / Express / TypeScript
- Database: PostgreSQL via Drizzle ORM
- AI Integration: Google Gemini API (Embeddings & Generative Chat)
- Security: Helmet, CORS, Rate-Limiter, BCrypt, JWT
For senior-level evaluation, I have engineered an Indestructible E2E Showcase using Playwright. This is not a simple unit test; it is a full-scale demonstration of the platform's stability, security, and feature depth.
- Zero-Friction Auth: Automated registration and session persistence.
- AI Vaulting: Securely anchoring and validating the Gemini API key.
- RAG Pipeline: Real-time document ingestion, embedding generation, and vector-based analysis.
- Intelligent Search: Semantic retrieval across the document repository.
- Security Audit: A final, complete data purge (deleting the demo account) to ensure system integrity.
To witness the platform demonstrate itself across Desktop, Tablet, and Mobile views, run the following:
# Ensure servers are running (npm run dev)
cd frontend
npx playwright test tests/e2e/recording.spec.ts --project=chromium --workers=1 --headedNexusDoc implements a sophisticated adaptive layout engine. Instead of simply scaling down, the UI intelligently transforms (e.g., sidebar-to-toggle transitions) to ensure peak accessibility and ergonomics on all devices.
Every core flow—from Authentication to Document Intelligence extraction—is protected by an exhaustive Playwright E2E Suite. We validate the platform across a matrix of devices and themes to ensure absolute technical integrity.
- Multi-Viewport Matrix: Automated testing across Desktop (PC), Mobile (Pixel 5), and Tablet (iPad Mini).
- Intelligence Lifecycle: Validates the complete pipeline: Registration → Document Upload → AI Summarization → Semantic Search → Sovereign Purge.
# IMPORTANT: Ensure the Backend Server is running before executing E2E tests
cd backend && npm run dev
# Run the Exhaustive Recording Matrix (All viewports + Headed for Capture)
cd frontend
npx playwright test --project=chromium --project=mobile-chrome --project=tablet-safari --headed- Full build verification.
- TypeScript type-checking (
tsc --noEmit). - Automated Playwright browser tests on every push.
NexusDoc is designed for high-transparency evaluation. Clients and stakeholders can choose their preferred depth of review:
The platform is currently in Frictionless Client Demo Mode. Email verification has been intentionally bypassed to allow you to experience the full AI pipeline in under 60 seconds.
- Access: Visit the Registration Page.
- Credentials: Use any dummy email (e.g.,
client@demo.test) to instantly access the dashboard. - Privacy: All demo accounts are eligible for the Sovereign Purge (Account Deletion), which mathematically erases all your metadata and vector embeddings from the server.
If you wish to see the system test itself across every feature (Upload, Summary, Search, Purge), you can run our professional Playwright suite.
Detailed Step-by-Step Execution:
- Prepare Environment: Ensure your
.envfile contains a validNEXT_PUBLIC_GEMINI_API_KEY. - Start Services: Open two terminals and run
npm run devin both thebackendandfrontenddirectories. - Execute the Suite: Open a third terminal in the
frontenddirectory and run:npx playwright test tests/e2e/recording.spec.ts --project=chromium --workers=1 --headed - The "Sequential Lifecycle":
- Desktop Perspective: The browser opens and registers a demo account.
- Manual Pause: The script pauses for 30 seconds at each stage to allow you to initiate a screen recorder (like Cursorful).
- Exhaustive Testing: The robot physically uploads a document, waits for AI embeddings, performs a semantic search, and cleans up.
NexusDoc is architected as a Unified Monorepo designed for Single-Cloud Performance on Vercel:
- Deployment: Both the Next.js frontend and Node.js backend are deployed as distinct projects within a single Vercel team.
- Frontend Config: Root Directory set to
frontend. Deployed as a high-performance Next.js application at the Edge. - Backend Config: Root Directory set to
backend. Deployed as a Serverless API hub, ensuring sub-millisecond cold starts and absolute scalability. - Synchronization: The frontend and backend communicate over Vercel's private network, minimizing latency and maximizing intelligence throughput.
- Engine: Serverless PostgreSQL (v16+).
- Features: Utilizes Neon's database branching for zero-risk schema migrations and point-in-time recovery for absolute data integrity.
- Node.js 20+
- PostgreSQL Database
- Google Gemini API Key
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Clone the repository:
git clone https://github.com/MrDoVersaworks/nexusdoc.git cd nexusdoc -
Setup Backend:
cd backend npm install # Create .env based on .env.example npm run db:push npm run dev
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Setup Frontend:
cd ../frontend npm install # Create .env based on .env.example npm run dev
NexusDoc is part of a high-innovation portfolio series.
Architected by Oyewole Favour
📧 Contact via the in-app Contact Form (accessible from the dashboard sidebar)
💼 LinkedIn
🌐 GitHub
Note
This project is under active development and is being continuously improved.