Attach notes, images, files, and decisions to the protocols, samples, batches, and projects they describe so the reasoning remains with the result.
FlaskTrack turns the ELN into a connected knowledge layer: readable by people, linkable to laboratory records,
searchable across the organization, and available to governed AI assistance.
A scientist records an observation against the sample and links supporting files. The next researcher can find both the note and the execution that gave it meaning.
Laboratory knowledge should not live in disconnected documents
Conventional electronic notebooks often preserve written observations while separating them
from the protocols, samples, batches, materials, schedules, and operational events that provide
their meaning. FlaskTrack keeps laboratory documentation connected to the underlying work and
makes selected knowledge available through AI-assisted retrieval.
🔗
Connect notes to real records
Link documentation directly to batches, samples, protocols, workflows,
ingredients, tools, species, plasmids, suppliers, files, and reports.
Preserve institutional knowledge
Turn experimental reasoning, troubleshooting, decisions, methods, and observations
into durable knowledge that remains available as projects and teams change.
Reduce documentation sprawl
Replace fragmented documents, private notebooks, shared folders, and chat history
with one organized laboratory knowledge environment.
Flexible writing for scientific and operational work
✍️
Markdown laboratory notes
Create structured notes using headings, lists, tables, links, checklists,
code blocks, images, references, and formatted scientific content.
🧩
Embedded laboratory records
Embed cards and links for batches, samples, protocols, workflows, ingredients,
tools, files, and other FlaskTrack entities directly inside a note.
📎
Files and supporting evidence
Attach images, instrument output, spreadsheets, source documents, certificates,
raw data, presentations, and other supporting files.
🏷️
Tags and organization
Organize notes by project, experiment, method, program, species, department,
investigation, campaign, or internal classification.
Documentation that participates in laboratory operations
🧪
Experiment and study records
Document hypotheses, experimental plans, variables, observations, interpretations,
conclusions, follow-up work, and links to the executed records.
📋
Methods and troubleshooting
Preserve internal procedures, optimization notes, recurring failure patterns,
corrective actions, and lessons learned alongside formal protocols.
👥
Team collaboration
Share documentation across the organization, reference team members,
coordinate review, and keep operational knowledge visible to authorized users.
Build an AI Knowledge Base from your laboratory documentation
Every laboratory note can become more than a static document. FlaskTrack allows authorized
users to selectively index notes and supporting files into an organization-controlled
AI Knowledge Base. That indexed knowledge can then be used during AI Assistant conversations,
helping teams retrieve methods, observations, decisions, troubleshooting history, and internal
guidance through natural-language questions.
Responses remain grounded in the laboratory’s own indexed material and preserve links back
to the original source records, allowing users to verify context before acting on scientific
or operational information.
Retrieve prior experimental context
Find earlier observations, conclusions, method changes, failure analyses,
and related work without manually searching every notebook entry.
Connect answers to operational history
Follow references from indexed notes back to the related samples, batches,
protocols, files, measurements, and execution records.
👩🔬
Support onboarding and continuity
Help new and existing team members retrieve organization-specific procedures,
terminology, decisions, and historical context without relying on tribal knowledge.
Built for scientific traceability, retrieval, and review
📝 Create structured laboratory notes using Markdown, tables, links,
checklists, images, and supporting files
🔗 Embed and reference batches, samples, protocols, workflows, materials,
tools, catalogs, and other operational records
🧬 Preserve the relationship between written observations, sample lineage,
protocol execution, measurements, and outcomes
📥 Selectively index notes and files into an organization-controlled
AI Knowledge Base
💬 Use indexed laboratory knowledge during AI Assistant conversations
and natural-language retrieval
📖 Review source-linked responses and return to the original note or file
before making scientific decisions
📤 Export and share documentation for review, collaboration,
archiving, or external delivery
🛡️ Maintain access, source context, audit history,
and accountable human review
Buyer evaluation
Evaluate the notebook as part of the laboratory record
A connected ELN should improve retrieval and context without making scientific writing
rigid or hiding records inside a proprietary format.
01
Writing fit
Test real note structures, attachments, links, review practices, and collaboration patterns used by
your researchers.
02
Record relationships
Verify how notes connect to samples, batches, protocols, molecular designs, files, users, and audit
history.
03
Portability
Export representative notes and attachments before purchase so the organization understands the
usable exit path.
More than a notebook attached to your laboratory
FlaskTrack makes documentation part of the operational system itself. Scientific reasoning,
experimental records, methods, files, and institutional knowledge remain connected to the
protocols, samples, batches, materials, people, and outcomes that give them meaning.
By indexing selected documentation into the AI Knowledge Base, your team can carry that
accumulated knowledge into every authorized AI Assistant conversation without separating
answers from their original scientific sources.
