Every research team needs a librarian.
Most small research teams and science-driven startups work without one. We change that — by connecting you with vetted research librarians, helping you set up your own literature systems, and supporting ongoing research work.
A collaborator most labs don't have.
In large research institutions, the pattern is decades old. Senior investigators routinely collaborate with credentialed research librarians — specialists who design search strategies, document methodology, organize literature databases, and often appear on papers as co-authors. At major universities, in Cochrane reviews, in well-funded labs, the role is understood and the value is measured. The practice is standard.
Walk into a small research team — a Series A startup, an early-stage deep tech company, an academic lab without dedicated library support — and the pattern is absent. Literature work falls to whoever is available: a PhD between experiments, a founder writing grants at 2 am, or, increasingly, an AI tool producing a landscape review overnight. The work gets done. What goes missing is the methodology, the rigor, the defensibility — and the specialist whose entire professional practice is precisely that.
The same instinct that says not to run your own assays without a wet-lab specialist applies to the literature base your decisions rest on.
The resulting gap is not theoretical. It shows up as a missed paper in a competitor's space that derails a program. As a due-diligence literature review that did not survive an investor's follow-up questions. As an AI-generated landscape review full of citations that fall apart on inspection. These are expensive, recoverable only with hindsight, and increasingly common.
Three shifts that make this urgent now.
The absence of a research librarian is not equally felt across teams. Three recent shifts have made the gap more consequential for small research teams than ever before.
The decisions keep getting higher-stakes.
Investor pitches, regulatory submissions, partnership deals, grant applications, scientific publications — all rest on the quality of your evidence synthesis. A missed publication in your space can sink a program. A hallucinated citation in a briefing document can permanently damage credibility with a reviewer, regulator, or board. Neither outcome is easily recoverable.
AI tools raise the ceiling and the floor.
Modern research tools — including AI workbenches with built-in citation checking — have made first-pass literature work dramatically faster, and dramatically better at catching their own mechanical errors. What they cannot supply is independence: a system checking its own output is not the same as outside, accountable review. The gap that matters now is judgment — knowing which findings actually hold up, which sources to trust, which conclusions survive a board member's or reviewer's scrutiny. That judgment is what a research librarian provides.
Reproducibility expectations have risen.
Regulators, reviewers, and increasingly investors want documented methodology. "We looked at the recent papers and they seem promising" is no longer an acceptable evidence base for a program decision or a regulatory submission. A PRISMA-style search log, a deduplication record, an explicit inclusion/exclusion rationale — these are how serious work gets defended when it matters.
A note on Claude Science and tools like it.
Yes, we know about Claude Science, Anthropic's new AI workbench with a built-in reviewer agent. It's a real capability, and we think it's good for science. It is also, by design, a single system checking its own work. The role of an independent research librarian is not displaced by a better internal reviewer — it is what a second, accountable, outside party looks like. That distinction matters most exactly where the stakes are highest: the submission, the board deck, the diligence memo.
What the work actually looks like.
The title librarian undersells the practice. The research librarians in our network hold MLIS degrees combined with PhDs across biology, chemistry, engineering, and adjacent science fields. Many have spent years inside pharma medical information teams or established academic-medical library programs before going independent.
What they deliver is not a stack of PDFs. It is a defensible evidence base — a documented search strategy that can be reproduced, a synthesized landscape that captures signal without drowning you in it, organized internal literature your whole team can use, and a methodology trail that survives scrutiny from peer reviewers, regulators, or investors. The work divides cleanly into a few recurring engagement types.
Target validation review
Defensible literature landscape around a new target — pathways, mechanisms, competing programs, documented gaps.
Competitive intelligence
Synthesized landscape of programs, trials, publications, and patents in your space. Strategic signal, not a data dump.
Due diligence sprint
Fast-turnaround evidence review for an investment committee, inlicensing decision, or BD deal on deadline.
Regulatory literature package
Search strategy and documented pulls for IND-enabling briefing documents, safety sections, and prior-art citations.
AI output audit
Independent verification of AI-assisted literature work — whatever tool produced it, including built-in review features. An external, accountable check before it reaches a deck, submission, or board.
Knowledge management & library setup
Set up your team's own literature infrastructure — reference management, naming conventions, searchable internal database, ongoing curation. The library your team should have had from day one.
Embedded librarian
A vetted research librarian embedded in your team 8–10 hours a week. Standing Slack presence, rolling literature monitoring, ongoing search support, cross-project documentation.
Who you're working with.
Every research librarian in the network is vetted by hand before being matched to a project. Credentials are verified, sample work is reviewed for methodology rigor, and professional-grade confidentiality practice is confirmed. NDAs are standard on every engagement, signed before scope is shared.
What "vetted" means, concretely.
An MLIS degree or relevant PhD, verified directly with the issuing institution. Hands-on experience in pharma, biotech, medical libraries, or research-library settings serving science-driven teams. Methodology rigor demonstrated through sample work — search strategies, PRISMA flow diagrams, deliverables from past engagements, anonymized.
The network is small by design. We start with a curated group of librarians, each selected for fit with the engagement types above. Matching is concierge, not algorithmic: we review each brief personally before proposing candidates.
Tell us about your project.
Twelve short questions — about two minutes. We'll review personally and reply within 48 hours with one or two proposed specialists matched to your brief.