Boer is an AI research platform where scientists search literature by intent, follow cross-disciplinary knowledge, subscribe to new work and pre-screen experimental plans.
Boer: An AI Research Platform for Scientists
Boer is an AI research platform built for researchers. It puts a self-developed scientific language model, more than 160 million academic documents, a multidisciplinary knowledge base, experimental capabilities and large-scale computing resources into one working environment, so literature discovery, experiment design and the path from result to application stop being separate chores. Where a classic search box matches keywords, Boer tries to understand the research question first and then returns usable literature leads and candidate directions.
Main capabilities
- Intent-aware search: ask a full research question such as how quantum computing is used in drug discovery, and results are organised around the underlying intent instead of literal word hits.
- Multimodal input: combine text with images and molecular structures. Upload a chemical structure and the platform looks for related papers and studies.
- Cross-disciplinary matching: the knowledge base links material across fields, so a biomedical question can surface chemistry or physics work and expose intersections between disciplines.
- Daily updates and personalised recommendations: new results are tracked continuously and pushed according to a research profile.
- Literature subscriptions: follow papers, authors and journals and get notified when something new appears.
- Knowledge management: papers, notes and conversations live in one place, and the assistant can extract and merge key points from several documents, for example collecting every description of one experimental method.
- Research assistance: experiment design and simulation support backed by experimental tooling and computing capacity, so weak options are dropped before lab time is spent.
- Translation support: help moving from a finding towards application, including market context and possible collaboration routes.
- Resource hub: research apps, courses and competitions collected for different career stages.
- Researcher exchange: a space to share experience, discuss open problems and run online seminars.
How to use it
- Sign up and fill in your research profile: fields, keywords, journals and authors you follow. The more specific the profile, the closer the recommendations land.
- Start a search with a complete research question. If the question involves a structure or a figure, upload the image or molecular structure as input.
- Filter the result list for highly relevant work, then add it to subscriptions or to the knowledge base. When you need a comparison, ask the assistant to extract methods and findings across several papers.
- Move to experiment design and use simulation to estimate feasibility and key parameters before committing to a real run.
- Return to the daily feed, check new recommendations and file the leads into project notes so the research record keeps growing.
Where it fits
- Scoping a topic or writing a review: judge how crowded a direction is, who leads it and which papers matter.
- Cross-disciplinary ideation: borrow methods from a neighbouring field and spot problems nobody has solved yet.
- Pre-screening experimental plans: discard parameter combinations that simulation shows to be unworkable.
- Lab knowledge retention: keep the papers, notes and discussions of a group in one searchable place.
- Technology scouting: understand where a result can be applied and which partners might be relevant.
Limitations
- The platform assists research; simulation and recommendations do not replace experimental validation, and key conclusions still have to rest on measured data.
- Search and recommendation quality depends on how complete the research profile is. A narrow profile tends to miss cross-field leads.
- Coverage follows the sources actually connected to the platform, so paywalled or unpublished work may be missing.
- Simulation, computing capacity and advanced features are usually limited by quotas, institutional permissions and compliance rules; check the in-product notes for the exact terms.
- AI summaries and merged notes can contain errors, so verify against the original paper before citing.
FAQ
Who is Boer meant for?
Researchers who follow literature over time: graduate students, lab leads and industrial R&D staff. Reading papers, drafting reviews, finding methods in neighbouring fields or pre-screening an experimental plan all happen in one place.
How is it different from a normal academic search engine?
A normal engine matches keywords. Boer works out the research intent first, matches across disciplines, and keeps subscriptions, notes and experiment simulation beside the results, which makes it a workbench rather than a search box.
Can simulation output be treated as an experimental result?
No. Simulation narrows the space and estimates trends before resources are committed; conclusions still need real experiments and measured data.
Frequently asked questions
Who is Boer meant for?
Researchers who need to follow literature over time: graduate students, lab leads and industrial R&D staff. Reading papers, drafting reviews, looking for methods in neighbouring fields or pre-screening an experimental plan all happen in one place.
How is it different from a normal academic search engine?
A normal engine matches keywords. Boer first works out the research intent, then matches across disciplines and keeps subscriptions, reference notes and experiment simulation beside the search results.
Can simulation output be used as an experimental result?
No. Simulation narrows the space and estimates trends before you commit resources; conclusions still need real experiments and measured data.
