Science
AI transformation for research,
starting with how your work runs today.
Methods is the software that maps how the work runs, so a lab can see where AI helps.
Methods is an AI engine that works with researchers as an agentic collaborator. It captures how your work actually runs and turns it into standardized, branded documentation you confirm step by step. Keep your lab's documents and foundational knowledge in your own folders, and Methods uses retrieval-augmented generation (RAG) to ground a private lab agent in them, so every answer cites your lab's own sources. You stay in charge of the science, and nothing leaves until you sign off.
- ✓You know the step three people do three ways. Nobody wrote the rule, so nothing can be handed off or scaled.
- ✓The analysis runs the same way only when you run it. A spreadsheet routine, a patched script, a threshold set by eye.
- ✓Your funder wants the complete, deposited protocol that no one has had time to write down.
- ✓A new student learns by standing next to you, and something is lost in the teaching.
Bring one part of your research. Leave with a map of it.
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See Methods in the sample app
Click through a real session, the record it produced, the templates and what the agent learned. Example data. Nothing is saved.
Open the demo (opens in a new tab)Your record, and your process map: the whole program, one section, one judgment call.
The record above is what a second scientist follows. The map is the same process at three levels: the whole program, section by section, with who runs each, what already has a tool, what needs a rule, and what was never said; one section drawn by person, so every handoff shows, with a diamond at every judgment call and a flag on every value never stated; and a single judgment call with its rule and values, in your words. Everything is generated from what you said, in your words, and nothing leaves until you sign off.
Five things, every one of them yours.
Your AI Transformation Assessment
Each step of your work placed on the ladder, what your lab already has and what it still needs, your top three priorities, and the first win to put to work.
Your record
The protocol or process, every step and judgment call, with every unstated value marked. A second scientist can follow it; your name goes on it because you approved every line.
Your process map and standardization map
The workflow drawn by role, and the map of what already has a tool, what needs a script, what was never stated, and what to standardize first.
Your template library
The documents researchers were never trained to write, filled from your sessions in your lab's own layout: who does what, a program one-pager, your funder's data plan, an onboarding plan, a standard operating procedure. You pick; nothing is pushed.
Your agent
It knows your lab. Point it at the folders you already keep, and it answers from your own protocols and notes, citing the file each answer came from. It learns from your team's sessions and keeps what it learns in three memories, every rule visible with where it came from, and the PI decides what stays. When you are ready, it tells you whether your own data could train a model of your own.
Your lab's knowledge, built in.
Keep your lab's documents and foundational knowledge in your own folders. Methods uses retrieval-augmented generation (RAG) to ground your private lab agent in them, so every answer cites your lab's own sources. Start with an agent that knows your lab. Grow into a model that's yours.
One template.
One standard.
You already have the science. What most labs never had is the operations toolkit: a documentation standard everyone follows, templates the whole team fills the same way, and analysis that runs identically no matter who ran the bench. Methods listens for those gaps and offers the fix at sign-off, never mid-session.
See the tiersSeven stages carry an idea to impact. Methods works where the data leaves the instrument.
Design, execution and analysis are where a workflow lives in one person's hands and where a step only one person can run quietly becomes result-to-result variance. Analysis is where we start; that is the part we capture and, when you are ready, help you automate.
hypothesis
research
design
data generation
insights
publication
& impact