Assistance layer · human-reviewed AI · organization-controlled context
AI that works inside the laboratory system—not around it
Use AI to reduce repetitive work across protocol drafting, document extraction, reporting, SQL, knowledge
retrieval, and operational troubleshooting while keeping scientists and operators in control of the final
record.
Because AI operates against connected FlaskTrack context, assistance can reference the same workflows, samples,
files, reports, and organizational knowledge your team already governs instead of creating a second disconnected
system.
Versioned recordsProtocols, scientific entities, files, and controlled
releases
Live executionScheduled work, batches, samples, roles, and operational events
Instrument dataLocal ingestion, mapping, provenance, review, and downstream
use
Data pipelinesSQL, reports, Python/R transforms, APIs, and analytical storage
Built-in governancePermissions, signatures, audit history, review, and validation
support
FlaskTrack is built around the belief that the people performing and directing
biological work remain the most important part of the process. AI is most valuable
when it handles repetitive preparation and data-entry work while scientists,
engineers, technicians, and operators retain responsibility for decisions.
🧠
Human Judgment Remains Central
Scientific interpretation, operational decisions, exception handling,
and production approval remain with qualified people.
⌨️
Reduce Repetitive Data Entry
Use AI to convert source material into structured records, draft reports,
prepare protocols, and populate repeatable operational fields.
⏱️
Recover Time for Higher-Level Work
Spend less time transcribing procedures and constructing boilerplate
so teams can focus on research, analysis, troubleshooting, and execution.
✅
Review Before Production
AI-generated protocols, workflows, materials, forms, and schemas remain
reviewable until an authorized user updates and finalizes them.
Use the AI provider and models that fit your organization
FlaskTrack lets each organization configure its own AI provider, language model,
embedding model, and connection settings. Your team controls how AI is deployed
and which models are used for each supported capability.
🤖
OpenAI
Connect your organization’s OpenAI account and select the language
and embedding models that match your performance and cost requirements.
🧠
Anthropic
Use Anthropic models for supported generation, document analysis,
protocol creation, workflow imports, and organizational assistance.
🖥️
Ollama & Local Models
Connect FlaskTrack to privately hosted Ollama models for greater
infrastructure control and local AI processing.
🏢
Enterprise On-Site AI Deployments
FlaskTrack is built by an engineering and software team with experience
deploying controlled infrastructure. Enterprise clients can engage us to
evaluate, design, and configure private on-site FlaskTrack and LLM deployments.
⚙️
Model Selection
Choose the language model used for generation instead of relying
on a hardcoded platform-wide model.
🔎
Embedding Model Flexibility
Configure the embedding model and dimensions used to index and search
organizational knowledge.
Plan and execute laboratory work with AI agents
FlaskTrack combines an in-app agent with MCP access to your live laboratory
system. Ask for an operational task in plain language, let the agent resolve
the required records and dependencies, then review and approve the exact
actions before anything changes.
🤖
Agentic Planning
Describe what you want to accomplish and FlaskTrack can build a
structured action plan using the workflows, samples, batches,
protocols, species, materials, and other records already in your lab.
🔌
MCP-Native Laboratory Access
Expose controlled FlaskTrack tools to compatible AI agents through
MCP for structured discovery, read operations, workflow actions,
and organization-scoped automation.
🔎
Live Record Resolution
The agent can search real FlaskTrack data, resolve required records,
and present relevant options directly in the interface instead of
asking users to find and copy internal IDs.
✅
Review Before Execution
Mutating actions are prepared as an explicit plan for review before
execution, while FlaskTrack continues to enforce permissions,
validation, organization boundaries, and compliance requirements.
Private AI infrastructure for controlled environments
Organizations with sensitive data, internal security requirements, or restricted
network environments may need more than a hosted AI API. FlaskTrack supports
enterprise planning and implementation for privately operated AI infrastructure.
🖥️
On-Site FlaskTrack Installation
Deploy FlaskTrack within customer-controlled infrastructure,
including on-premises, private-cloud, isolated, or specialized environments.
🔒
Private LLM Evaluation
Evaluate available hardware, security boundaries, model requirements,
expected workloads, storage, and operational constraints.
🛠️
Implementation & Configuration
Configure supported local models, embedding models, provider connections,
knowledge indexing, and FlaskTrack integration for enterprise clients.
🗺️
Internal Deployment Roadmaps
Organizations that prefer to implement internally can receive a technical
architecture outline and practical path for building their own private setup.
Ask questions across documentation and organizational knowledge
The FlaskTrack assistant uses retrieval-augmented generation to find relevant
information before answering. Users can work with platform documentation and
organization-approved files from one controlled interface.
📘
FlaskTrack Documentation
Ask how to configure, operate, or troubleshoot FlaskTrack using
indexed platform documentation.
📂
Organization Knowledge Base
Search files and notes that your organization has explicitly marked
for inclusion in its knowledge base.
🧩
Context-Grounded Answers
Retrieve relevant document sections and use them as context for more
useful answers tied to your organization’s actual records.
🔐
Organization-Scoped Retrieval
Keep indexed knowledge, document chunks, embeddings, and AI access
scoped to the organization that owns them.
Build custom reports with AI-assisted SQL
Describe the report you need in plain language and let the report builder generate
SQL designed for FlaskTrack’s analytical environment, available schemas, and
custom organizational data.
Custom Data Forms
Create the custom fields and data-capture schemas needed to record
measurements, observations, outcomes, and execution details.
Import entire workflows from beginning to end
Supply a workflow source file and FlaskTrack can generate the connected protocols,
ordered steps, required materials, and data-capture structures that make up the
multi-step operational process.
🔁
Complete Workflow Generation
Generate a reusable workflow composed of multiple structured
protocols instead of importing each procedure independently.
🧱
Protocol Sequencing
Arrange generated protocols in their intended operational order
from initiation through completion.
⏱️
Execution Structure
Build protocol steps, timing, expected outcomes, requirements,
and data collection into the generated workflow.
🧬
Operationally Connected Records
Connect generated workflow records to the catalogs, schemas,
materials, and execution structures they require.
AI-generated records remain under human control
Generated protocols, workflow structures, materials, and custom schemas are not
immediately placed into production. FlaskTrack creates them as reviewable records
so authorized users can inspect and correct the results first.
📥
Generated as Reviewable
AI-created records enter the system in a reviewable state rather
than becoming immediately available for production execution.
🔍
Inspect Every Generated Record
Review protocols, steps, materials, workflow ordering,
custom fields, and generated schema definitions.
✏️
Update Before Approval
Correct generated content, add missing operational details,
and align the import with your organization’s standards.
✅
Finalize for Production
Approve reviewed records before they are used to run production
workflows, batches, samples, and protocol executions.
AI capabilities built for controlled lab operations
✔ Organization-managed OpenAI, Anthropic, and Ollama provider configurations
✔ Configurable language models, embedding models, dimensions, and provider settings
✔ Support for private and on-site enterprise model deployments
✔ Retrieval-augmented chat across FlaskTrack documentation and approved organization files
✔ Organization-scoped knowledge indexing, embeddings, and document retrieval
✔ AI-generated, schema-aware SQL for custom Arrow Flight-backed reports
✔ Complete protocol generation from prompts, papers, procedures, notes, and uploaded files
✔ Automatic generation of protocol materials, requirements, custom forms, and schemas
✔ Human-led review, correction, and approval of all AI-generated operational records
✔ AI assistance focused on repetitive data entry, document conversion, and report preparation
✔ Enterprise evaluation and setup of private, on-site FlaskTrack and LLM infrastructure
✔ Technical deployment roadmaps for organizations implementing private AI internally
Buyer evaluation
Evaluate AI against your governance requirements
AI features should be assessed as part of your operating model—not as a substitute for
scientific review.
01
Provider control
Confirm which providers, models, credentials, retention settings, and private deployment options your
organization will permit.
02
Review responsibility
Define who may generate drafts, who must review them, and which actions still require formal approval
or an electronic signature.
03
Measured value
Use representative documents and reporting tasks to verify that AI reduces administrative work
without introducing unacceptable review burden.
Automate the monotonous work while people lead the science
FlaskTrack uses AI to accelerate structured preparation, data entry, knowledge
retrieval, reporting, and document conversion. Human expertise remains responsible
for interpretation, correction, approval, and production decisions. Your organization
retains control of its provider, models, infrastructure, source material, generated
records, and final operational use.
