TARE
ELN + LIMS with a Digital Lab Assistant
TARE combines an electronic lab notebook, lightweight LIMS, and Digital Lab Assistant to help teams document experiments, track samples and materials, preserve custody context, attach evidence, review AI-assisted records, and export audit-ready documentation.
Record system
ELN + LIMS
Workflow mode
Human reviewed
Assurance layer
Audit replay
Product surfaces
More than a concept page.
TARE is being shaped around real lab screens: dashboards, experiment planning, inventory, plates, metrology, and audit-ready evidence workflows.
Try the free GHS label generatorCommand workspace
A dashboard-level view of lab records, operational status, and review queues.
Plate workflows
Assay plate views for sample assignment, run context, and structured lab records.
Inventory and materials
Structured inventory views for materials, locations, hazards, and lab operations.
Backed & supported by
Backed by Virginia Innovation Partnership Corporation (VIPC) and member of NVIDIA Inception.
Scientific work needs an operating record
Move from scattered lab artifacts to a reviewable chain of work.
Private beta focuses on ELN records, inventory/materials context, sample and custody events, evidence attachments, audit trails, and signed exports. Vision-assisted documentation, instrument ingestion, and edge workflows are active R&D.
Notebook to sample history
Custody-aware evidence
Lab operations context
What TARE Does
ELN, LIMS, assistant, and provenance in one lab workspace.
Document
Track
Assist
Prove
Reviewable record flow
Built around attribution, context, evidence, and approval.
Custody-aware event capture tied to samples, materials, experiments, and evidence objects.
Tamper-evident audit trail primitives and operator attribution for reviewable timelines.
Evidence integrity checks and signed export bundles designed to support defensible records.
Record created
Experiment session opened with operator attribution and linked protocol context.
Samples and materials linked
Inventory, operators, custody-relevant events, cases, and evidence objects stay connected.
Human review completed
AI-assisted draft content remains provisional until a person reviews and accepts it.
Signed export assembled
Audit trail, custody events, and evidence references are packaged into a defensible bundle.
Advanced R&D
Dream Lab explores provisional research support with human review.
Dream Lab — Experimental
Dream Lab is an experimental Digital Lab Assistant research workspace for design partners. It explores quota-limited background hypothesis generation, literature-grounded critique, and draft protocol skeletons. Outputs are provisional until reviewed by a human operator.
Review Dream Lab roadmapPilot verticals
Built for evidence-sensitive lab workflows.
TARE is for evidence-sensitive lab workflows where records, samples, materials, attachments, custody, and review need to stay connected. We are recruiting design partners across several lab verticals to identify the strongest initial market wedge.
Forensic / Evidence
Pain: Custody, audit trail, evidence, signatures, and defensible exports must stay connected.
Supports today: Case-aware records, evidence attachments, custody-relevant events, hashing, audit trails, and signed exports.
Learning: Which chain-of-record workflows create the strongest first wedge for design partners.
Not yet productized: Full agency-specific validation packets and every forensic lab instrument workflow.
Environmental / Water
Pain: Field collection, sample history, attachments, and reporting context often split across tools.
Supports today: Sample and material context, field evidence attachments, reviewable records, and export bundles.
Learning: How much LIMS depth is needed before field-to-report workflows are self-serve.
Not yet productized: Complete regulatory reporting templates for every jurisdiction.
Materials / Industrial Testing
Pain: Specimens, test records, photos, instruments, operators, and attachments need durable linkage.
Supports today: Experiment records, sample/specimen context, media attachments, audit trails, and review workflows.
Learning: Which specimen tracking and test evidence patterns repeat across industrial labs.
Not yet productized: Deep instrument ingestion and calibration packages for every test method.
R&D Bio/Chem
Pain: ELN work, protocols, observations, reagents, assistant output, and review state drift apart.
Supports today: ELN records, protocol-aware notes, inventory/material context, and human-reviewed AI assistance.
Learning: Where AI-assisted documentation is valuable without overstepping lab authority.
Not yet productized: Validated protocol generation or autonomous lab execution.
Media-heavy Lab Records
Pain: Photos, video, instrument exports, and shared-drive files sit outside the formal record.
Supports today: Attachments, evidence hashing, record linkage, reviewable timelines, and export packaging.
Learning: Which multimodal capture flows should become first-class product workflows.
Not yet productized: Fully automated interpretation of every image, video, or instrument file.