Ingest
Native scientific data, metadata, scale, channels, acquisition context, and provenance are preserved.
AI-Powered Scientific Intelligence
BENNETT reads native scientific instrument files, measures what is present, tests interpretations against scientific constraints, blocks unsupported claims, and publishes defensible scientific output.
Access is reviewed by hand. The platform is in private beta while the engine set is validated.
Evidence first. Measurement before interpretation. Constraints always.
Native data in, defensible evidence out — with every stage recorded rather than assumed.
Native scientific data, metadata, scale, channels, acquisition context, and provenance are preserved.
Deterministic engines quantify signal, structure, spatial relationships, and applicable scientific parameters.
Interpretations are tested against modality, instrument, measurement, and scientific limits.
Measured evidence, methods, validation state, claim boundaries, and provenance are assembled into a defensible record.
Straight from the microscope — Zeiss, Nikon, Leica, Olympus, Bruker. No conversion, no export step. The vendor container is decoded as-is.
Archives are unpacked through a hardened extractor that refuses executables, symlinks, path traversal and compression bombs.
A gated engine is not a failure — it means your acquisition does not meet that engine's preconditions, and the report says which one and why. Interpretation stays inside what the modality, resolution and metadata can support: no inferring molecular interaction from co-localization, no numbers that did not come out of your pixels.
Identifyn built the scientific operating foundation. BENNETT is what that foundation made possible.
Identifyn operated under a LEAN manufacturing system designed to standardize method development, image acquisition, processing, and documentation across conventional microscopy, super-resolution, and single-molecule imaging. Reproducible workflows, structured metadata, and disciplined documentation created the scientific training foundation for BENNETT.
BENNETT is designed to reduce interpretive bias and workflow variability while lowering the time and cost required to move from native scientific data to defensible output.