Seven concrete situations
Can't see your unpublished data. You either paste excerpts — leaking IP into a shared model with no context — or it invents citations that sound plausible.
Ingests your corpus into a tenant-isolated vector store and grounds every citation in real content you own. Your data never trains a shared model.
Hallucinates author names, DOIs, and journal issue numbers. Retractions have happened.
Cites only documents indexed from your actual corpus with verified DOIs. Never fabricates a reference.
Pulls efficacy numbers from training data that may be out of date or wrong. Overclaims because it has no way to know what your trial actually showed.
Every numerical claim is grounded in the trial data in your corpus. Flags assertions it can't trace back to a source before publication.
Different voice every month. Loses track of each site's history. Re-asks you for the same background every run.
Ingests each site's most recent reports into the shared corpus and produces updates in the organization's established voice via the tenant's Tone Profile.
Your content is logged on OpenAI's servers and may be used to train shared models. Your competitors can get better because you asked a question.
Tenant-isolated storage. Your corpus never leaves your workspace, never trains a shared model, and is encrypted at rest. API-only contracts with every LLM sub-processor prohibit training on your data.
Knows what a rubric is in general. Doesn't read your solicitation URL, doesn't know the current NIH scoring criteria, and can't tell you that your Significance section would score a 4.
Reads the solicitation, extracts the review criteria, scores each section against them, and suggests specific edits. Like having a $10K grant consultant on retainer.
Each user gets a different voice. No shared memory of tone, terminology, or branding.
Tenant-level Tone Profile (8-dimension brand voice) applies to every workflow run across the team. The lab's voice stays the same whether the PI, a postdoc, or a comms manager ran the workflow.
The underlying point
A general LLM treats scientific writing as a stylistic problem. A scientist treats it as a truth problem — every claim has to trace back to real data, in the right voice, for the right audience. Eldari is a full stack around that constraint: corpus grounding, domain-aware intelligence modules, tenant isolation, Science Profile per tenant, and team-level voice consistency.
A hallucinated citation in a social post is embarrassing. A hallucinated citation in a grant ends a program. That's the gap we built Eldari to close.
Ready to see it on your own data?
Run a workflow against your corpus in a free trial, or have us walk through your use case on a 30-minute demo.