Human-led operations · configurable AI · private deployment options
AI-Assisted Lab Operations
Human ingenuity, scientific judgment, and operational experience are irreplaceable.
Flask Track uses AI as a practical tool for reducing repetitive data entry,
document conversion, report construction, and other monotonous administrative work.
Give researchers and operators more time for experimentation, interpretation,
troubleshooting, and higher-level scientific work while keeping generated records
reviewable and under human control.
AI should support human expertise, not replace it
Flask Track 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
Flask Track 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 Flask Track to privately hosted Ollama models for greater
infrastructure control and local AI processing.
🏢
Enterprise On-Site AI Deployments
Flask Track 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 Flask Track 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.
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. Flask Track supports
enterprise planning and implementation for privately operated AI infrastructure.
🖥️
On-Site Flask Track Installation
Deploy Flask Track 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 Flask Track 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 Flask Track 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.
📘
Flask Track Documentation
Ask how to configure, operate, or troubleshoot Flask Track 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 Flask Track’s analytical environment, available schemas, and
custom organizational data.
Generate complete protocols from a prompt or source file
Flask Track can transform a written request, research document, procedure,
or uploaded file into a structured protocol that is ready for review.
✍️
Prompt-to-Protocol
Describe the biological procedure and generate a complete protocol
with structured execution steps.
📄
File-to-Protocol
Import a paper, procedure, protocol document, or operational note
and convert it into Flask Track records.
🧪
Materials & Requirements
Generate required ingredients, tools, biological materials,
quantities, concentrations, and supporting protocol requirements.
📝
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 Flask Track can generate the connected protocols,
ordered steps, required materials, and data-capture structures that make up the
complete 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. Flask Track 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 Flask Track 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
- ✔ End-to-end workflow generation containing multiple ordered protocols
- ✔ 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 Flask Track and LLM infrastructure
- ✔ Technical deployment roadmaps for organizations implementing private AI internally
Automate the monotonous work while people lead the science
Flask Track 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.