AI with the laboratory context attached
Draft protocols from source material, find relevant knowledge, explore data, and review action plans in the system where laboratory work is recorded.
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.
A draft scientists can inspect
Import a method to create a draft protocol with steps and materials, then review and correct it before the team uses it. Provider options remain configurable by the organisation.
AI should support human expertise, not replace it
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.
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.
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.
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.
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.
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.
Generate protocol drafts from a prompt or source file
FlaskTrack can transform a written request, research document, procedure, or uploaded file into a structured protocol that is ready for review.
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.
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.
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
- ✔ 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 FlaskTrack and LLM infrastructure
- ✔ Technical deployment roadmaps for organizations implementing private AI internally
Evaluate AI against your governance requirements
AI features should be assessed as part of your operating model—not as a substitute for scientific review.
Provider control
Confirm which providers, models, credentials, retention settings, and private deployment options your organization will permit.
Review responsibility
Define who may generate drafts, who must review them, and which actions still require formal approval or an electronic signature.
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.