16,500 papers. Every answer cited.
For a scientific research organization we built a platform that turns an unmanageable literature corpus into direct, trustworthy answers. Ask a question in plain English; get a synthesis where every single claim carries an inline citation to a specific source abstract — at machine speed, with the researcher staying the validator.
and searchable
on a focused question
diagram, and long-form report
rule, not by hope
Too much literature. Too little trust.
Researchers face a volume of literature no human can read: this corpus alone holds roughly 16,500 abstracts. Keyword search returns lists, not answers. And generic AI chatbots do the opposite — they answer fluently but hallucinate and cite nothing, which is disqualifying for scientific work.
The job: turn the pile into fast synthesis a researcher can actually stand behind.
Anti-hallucination as architecture, not a promise.
Understand the question.
A fast model expands the query with scientific synonyms — the common name becomes the Latin binomial — so retrieval doesn’t miss what the literature calls things.
Retrieve, then synthesize.
A search index pulls the most relevant abstracts; a stronger model writes the answer under an enforced result-integrity rulebook.
Cite everything, invent nothing.
Every claim carries an inline citation to a specific abstract. Numbers must be verbatim — no rounding. Cross-source inferences are flagged as synthesis. And missing evidence gets an explicit “the provided sources do not address this” instead of a guess.
The researcher validates.
The tool is a research analyst, not an oracle: the citations exist precisely so a human expert can check every step of the reasoning.
Literature review at machine speed — auditable at every step.
The platform runs a focused, fully cited answer in about thirty seconds, and long-form reports over larger swaths of the corpus in minutes. The integrity rules weren’t decoration — the citation architecture was hardened specifically to withstand third-party expert scrutiny. The client’s own non-technical staff manage the corpus themselves through import tooling we built alongside the platform.
No staff displaced. The machine does the reading; the researcher owns the conclusions.
The pattern generalizes
Any firm with one expert and an unreadable pile of documents — specifications, contracts, regulations, research — has this same shape of problem. The answer is the same discipline: retrieval plus enforced citations, with your expert as the validator. See how we work →
Your documents already hold the answer. Ask them.
Bring the pile — specifications, contracts, regulations, research — and one question you’d like answered. We’ll show you what a fully cited answer from your own corpus looks like.
Can AI summarize research without making things up?
This platform turns roughly 16,500 scientific abstracts into direct answers where every single claim carries an inline citation to a specific source — no uncited claims allowed, by enforced rule rather than hope. Ask a question in plain English and get a synthesis in about 30 seconds, across five query modes (cited Q&A, comparison, summary, diagram, long-form report).
How does Blackfrog prevent AI hallucination?
Anti-hallucination is built in as architecture, not promised as a feature: retrieve first, then synthesize, cite everything, invent nothing — and the researcher stays the validator. It’s the same discipline behind every Blackfrog product, pointed at knowledge work: the machine reads at scale, and a person confirms it’s right before anyone relies on it.
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