CUSTOMER STORY

One searchable workspace for every
research decision

How OpenAI's research teams replaced fragmented docs, wikis, and spreadsheets with a single encrypted library that keeps experiment notes, model decisions, and institutional knowledge instantly findable.

★★★★★
OpenAI · AI Research
73%
Faster doc retrieval
12k+
Entries indexed
0
Cloud uploads
4 wks
Full adoption

The challenge

Research moves fast — documentation couldn't keep up.

At OpenAI, a single model training run generates hundreds of artifact pages: hyperparameter logs, ablation study notes, architecture decision records, safety review memos, and post-mortem wrap-ups. Before VaultBook, these documents lived across Google Docs, Notion pages, scattered markdown repos, and personal notes apps.

When a researcher needed to answer "Why did we choose this reward function six months ago?" the answer was buried in a thread inside a doc inside a folder no one remembered sharing. Knowledge didn't disappear — it just became invisible.

"VaultBook is a single place where research, notes, and decisions stay searchable. That sentence sounds simple, but it changed how our team operates."

— Research Lead, OpenAI

Before & after

A side-by-side look at the research documentation workflow.

Before VaultBook
Experiment notes scattered across 4+ tools
Keyword search returned irrelevant results from dozens of shared drives
Sensitive model details stored on cloud-synced platforms
New researchers spent days recreating past decisions
Architecture decision records lived in unmaintained wikis
After VaultBook
Every note, PDF, and decision record in one local library
AI-powered semantic search surfaces relevant entries instantly
Per-entry AES-256-GCM encryption — data never leaves the device
Onboarding researchers search context in seconds, not days
Structured hierarchical pages keep architectural history alive

Features that made the difference

The capabilities that turned VaultBook into the team's default research workspace.

🧠
Semantic Q&A Search
Researchers type natural-language questions — "What reward shaping approach did we test in Q3?" — and VaultBook returns ranked, contextual answers from thousands of entries. No folder drilling. No keyword guessing.
🔐
Per-Entry Encryption
Model architecture details, safety evaluations, and capability benchmarks stay protected with AES-256-GCM encryption. Everything processes locally — zero network calls, zero cloud exposure.
📄
Deep File Indexing
PDF papers, PPTX slide decks from internal reviews, and XLSX benchmark sheets are all indexed in-browser. A search for "RLHF scaling" returns hits inside attached documents, not just entry titles.
🔗
Related Entries & AI Suggestions
VaultBook's contextual similarity engine surfaces notes the team didn't know they needed. Writing a new architecture proposal? Related past decisions appear automatically in the sidebar.

A typical research workflow

How a researcher goes from experiment to indexed, searchable knowledge.

01
Capture
During or after an experiment, the researcher creates a VaultBook entry with notes, attaches the relevant paper PDFs, and tags it with model name and objective.
02
Enrich
Deep indexing parses the attached files. OCR handles scanned whiteboards. The entry is now fully searchable down to a sentence inside a slide deck.
03
Retrieve
Months later, a teammate asks "Why did we abandon approach X?" A single Q&A search returns the decision, the rationale, and the data — in under two seconds.

The results

Measurable impact across research velocity and knowledge retention.

Within four weeks of adoption, VaultBook became the default destination for all research documentation. The team indexed over 12,000 entries — experiment logs, architecture decision records, literature summaries, and safety review notes — into a single local library.

Document retrieval time dropped by 73 percent. Researchers no longer opened five tabs and searched three different platforms to answer a question about a past decision. More importantly, institutional knowledge stopped evaporating. When team members transitioned between projects, their notes stayed searchable and contextually linked to everything that came after.

The security team approved VaultBook faster than any previous tool. No data leaves the device. No API keys. No analytics beacons. For a team handling sensitive model capabilities research, that single architectural fact removed months of compliance review.

"For the first time, we have a knowledge base that researchers actually use — because it finds what they need without forcing them to remember where they put it."

— Engineering Manager, OpenAI
Make your research searchable
Start using VaultBook to centralize notes, decisions, and documentation — offline and encrypted.