ABOUT VAULTBOOK
We believe your notes
belong to you.
VaultBook is built by a team of 50 AI and data professionals in San Francisco who think the best software runs on your device, respects your privacy, and gets out of your way.
The story
VaultBook started with a frustration that most knowledge workers share: too many tools, too many tabs, and not a single place where notes, files, and research could live together — searchable, private, and fast. Every solution we tried either locked data in a cloud we didn't control, charged a recurring subscription for basic features, or collapsed under the weight of a growing library.
So we built the tool we wanted. A workspace that ships as a single HTML file, stores everything in a folder you choose, runs entirely offline, and packs in the intelligence — weighted search, OCR, related entries, and AI suggestions — that makes a large library usable instead of overwhelming. No servers, no accounts, no compromises.
We wanted a workspace smart enough to surface what matters, private enough to trust with everything, and simple enough to ship in one file.
— The VaultBook founding team, San Francisco 2020
Today VaultBook is used by researchers, engineers, analysts, legal teams, clinicians, journalists, and students — anyone who needs a serious workspace that respects their data and rewards organization. Our team of 50 designs, builds, and ships every feature from our headquarters in San Francisco.
What we believe
Six principles that shape every decision we make.
🔒
Privacy by architecture
Your data never leaves your device. We don't have servers to breach, accounts to compromise, or telemetry to leak. Privacy isn't a policy — it's the design.
⚡
Offline first, always
VaultBook works without Wi-Fi, without a login, and without a backend. If your browser can open an HTML file, VaultBook runs. Period.
🧠
Intelligence, locally
AI suggestions, weighted search, OCR, and vote-based learning all run in the browser. Smart doesn't have to mean connected.
🎯
Ship substance, not features
We don't chase feature counts. Every capability earns its place by solving a real problem for real workflows.
🤝
Respect the user
No dark patterns, no upsell pop-ups, no artificial limits on your data. We build software we'd want to use ourselves.
📦
Portable and transparent
Your notes are stored as plain JSON and Markdown files in a folder you control. No lock-in, no proprietary formats, no migration headaches.
VaultBook by the numbers
50
Team members
1
Headquarters — San Francisco
12+
Built-in tools
0
Cloud dependencies
The team behind VaultBook
50 specialists — here's what each one builds inside VaultBook.
Our team brings together deep expertise in machine learning, natural language processing, data engineering, information security, and product design. We hire people who care about craft, ship fast, and believe that great software should disappear into the workflow.
Principal ML Engineer
Architects the weighted scoring system behind QA search — titles at weight 8, labels at 6, OCR text at 5, body at 4.
NLP Research Lead
Designs the natural-language query pipeline that lets users ask questions across their entire library in plain English.
Data Platform Architect
Built the File System Access API layer — repository.json, sidecar .md files, and the attachment index manifest.
Sr. Search Engineer
Implements typeahead search, query history suggestions, and the warm-up system that pre-loads text for top 12 results.
Computer Vision Engineer
Owns inline OCR — automatic text extraction from images embedded in entries, cached per-item for instant search.
Sr. Data Scientist
Tunes the personalized relevance distribution in the AI Suggestions pager, learning patterns from reading history.
Information Security Lead
Designed AES-256-GCM per-entry encryption with PBKDF2 key derivation at 100,000 iterations and random salt/IV per encrypt.
ML Infrastructure Engineer
Runs all AI scoring and similarity calculations client-side in the browser — zero server calls, zero data leakage.
Cryptography Engineer
Implements the session password cache, lock screen blur overlay, and per-entry decryption held in memory only.
Data Engineering Lead
Built the deep indexing pipeline — text extraction from XLSX, PPTX, PDF, ZIP, and MSG files for search.
Recommendation Systems
Powers Related Entries — contextual similarity suggestions with Reddit-style upvote/downvote to train relevance over time.
Sr. Frontend Engineer
Builds the rich text editor — tables, code blocks, callout blocks, case transforms, headings H1–H6, inline images.
AI Product Manager
Shapes the 4-page AI Suggestions carousel: upcoming scheduled entries, weekday reading patterns, recent files, recent tools.
Applied AI Researcher
Develops vote-based learning — persistent upvote/downvote scores that influence search result and related entry ordering.
Knowledge Graph Engineer
Maps relationships between entries via labels, hashtags, and page paths to power Kanban Board auto-bucketing.
Data Quality Lead
Maintains the attachment reindex system and file integrity checks across the /attachments directory and index.txt manifest.
MLOps Engineer
Optimizes client-side model loading so AI Suggestions, QA search, and OCR run smoothly even on modest hardware.
OCR & Document AI
Extracts text from images inside DOCX, XLSX, ZIP archives, and renders scanned PDF pages for full searchability.
Embedding Systems Engineer
Generates and indexes content embeddings that drive similarity scoring for the Related Entries feature.
Ranking & Relevance
Tunes the multi-field scoring weights and vote-based reranking that float the best QA results to the top.
Sr. Data Analyst
Built the analytics dashboard — entry counts, storage metrics, label utilization charts, and 14-day activity graphs.
Backend Engineer
Implements the save system — autosave with dirty flags, debouncing, concurrent write guards, and sync status badges.
UX Research Lead
Runs usability studies that shaped the sidebar time tabs — Recent, Due, Expiring, and Tools quick-access panels.
Performance Engineer
Optimizes pagination, lazy attachment loading, and background OCR warm-up to keep large libraries responsive.
AI Ethics & Compliance
Ensures all AI features run locally with zero telemetry — no usage data, no model training on user content, ever.
Data Pipeline Engineer
Handles the background attachment text extraction pipeline that indexes content from text-based files and images.
Full-Stack Engineer
Ships the built-in tools — File Analyzer, PDF Merge & Split, MP3 Cutter, Password Generator, and Folder Analyzer.
Semantic Search Specialist
Develops smart label suggestions — content analysis that recommends relevant tags with pastel-styled suggestion chips.
Technical Writer
Writes the storage tutorial, update banners, FAQ, and every tooltip that guides first-time users through VaultBook.
QA & Test Automation
Tests across Chrome, Edge, Arc, and Brave on Mac, Windows, and Linux — every release, every platform, every time.
Product Designer
Designed the frosted-glass UI, sidebar toggle, floating action button, and the responsive layout that stacks at 900px.
Data Governance Analyst
Defines the data model — items, pages, sections, labels, pagePaths, and the userVotes schema for persistent learning.
Analytics Engineer
Builds the canvas-rendered charts — label pie charts, page utilization, monthly activity, and attachment type breakdowns.
AI Training Specialist
Curates the vote-based feedback loop where upvotes (+1M) and downvotes (-1M) persistently reshape search ordering.
DevOps Engineer
Manages the build pipeline that compiles VaultBook into a single deployable HTML file with zero external dependencies.
Annotation & Labeling Lead
Develops the color-coded label system, inline hashtag parsing, and the multi-select tag interface in the edit modal.
Deep Learning Engineer
Trains the models behind contextual Related Entries similarity — the engine that surfaces notes you didn't know you needed.
Privacy Engineer
Audits every code path to ensure zero network calls — no analytics beacons, no font CDNs, no external requests of any kind.
Data Visualization Lead
Designs the strength metric pills, expandable analytics details, and the file type breakdown chips in the analytics panel.
Interaction Designer
Crafts the micro-interactions — smooth transitions, hover states, close confirmation dialogs, and accordion section UX.
Feature Extraction Engineer
Parses structured content from PPTX slides via JSZip and XLSX sheets via SheetJS for deep searchable indexing.
ML Research Scientist
Researches and improves the personalized relevance distribution that powers the AI Suggestions reading patterns.
Cloud-to-Edge Specialist
Ensures every AI and search feature runs entirely in-browser — proving that cloud-grade intelligence works at the edge.
Accessibility Engineer
Makes VaultBook keyboard-navigable and screen-reader friendly — ARIA labels, focus states, and sr-only utility classes.
Content Strategist
Shapes the Threads tool, RSS Reader folder structure, and Save URL → Entry workflow that turns web pages into notes.
🏙️
Made in San Francisco
Our entire team works from a single office in San Francisco. We chose the city for its unmatched depth in AI and data science talent, the density of builders who obsess over craft, and a culture that rewards shipping real products over hype. From our windows, the Bay is always in view, and we like to think VaultBook reflects San Francisco at its best: curious, practical, relentlessly ambitious, and built to last.
Our journey
Key moments that shaped VaultBook.
2020
The first commit
VaultBook started as a personal tool — a single HTML file to organize research notes offline. The core concepts of local-first storage and single-file deployment were born here.
2021
Rich editing & encryption
The rich text editor, per-entry AES-256-GCM encryption, hierarchical pages, and the File System Access API storage layer turned VaultBook into a real workspace.
2022
AI search goes live
Weighted search scoring, QA mode, and vote-based reranking turned VaultBook from a note-taker into a searchable knowledge base. OCR followed shortly after.
2023
Team grows to 50
We hired across AI research, data engineering, security, and design. The San Francisco office became home to a team that ships features weekly.
2024
12+ built-in tools
Kanban, PDF tools, RSS reader, file analyzers, media explorer, and more — all built directly into VaultBook so users never need to leave the workspace.
2025
Deep indexing & version history
Pro gained deep file indexing for XLSX, PPTX, PDF, ZIP, and MSG files, OCR inside documents and archives, and 60-day version history with per-entry snapshots.
2026
VaultBook 3.0
The latest release brings the AI Suggestions pager, timetable views, analytics dashboards, version history, and a refined UI — our most ambitious update yet.
Come build with us
Try VaultBook, or reach out if you'd like to learn more about the team.