of PhD scientist time spent curating, not modelling.
typical platform-build before producing value.
measurable scientist throughput when curation is structured.
Extract · verify · flag · disambiguate · review per record. The operational layer.
RDKit-backed SMILES validation, canonicalisation, stereochemistry checking, rendering.
Every record: source DOI, page, figure, exact text span, model version. Audit-ready.
Scientist-hours saved, throughput, build-vs-buy TCO — natively measured.
Your rules, in declarative YAML. Versioned, diffable, exportable, yours.
Patterns harvested every cycle close. Methodology library grows. Compounding moat.
Before / after, in the rhythm of an actual workday. The substrate compresses the gap between data arrival and curated training set.
Overnight ingest flagged 84 records; scientist reviews — accepts, rejects, overrides.
System extracts records, scores confidence, queues sub-threshold cases for review.
Provenance report exports for partner's regulatory packet — every record citable.
Patterns from the week feed the next cycle. Override-rate flags rules to tune.
Rules, not platforms. Scientific definitions as portable, version-controllable artifacts.
On-prem, customer-owned. Runs in your infrastructure. No vendor lock-in.
Methodology is code. Curation is something you ship, not hope for.
Composable, not monolithic. DANTE is a layer in your stack — yours.
Ten minutes, at most. We read every reply personally — David responds within three working days, either with a 30-minute working session or a candid note about fit.
The substrate is built. Your rules are yours. Let's begin.