Establish first. Build when you are ready. Connect when you want compute, and partners, behind it.
Each tier adds a capability and a named point where you, not the software, make the call. Plans are annual and sized to your lab — the PI plus two logins comes standard — so a year of records builds into a lineage rather than a stack of one-offs.
AI transformation starts before AI.
Capture
Your process, written down: every step, judgment call and value.
Standardize
One template, one vocabulary, clean results across the team.
Automate
Scripts for the steps that should not wait on a person.
Transform
A model that's yours: connected compute and a model trained on your own data.
Your process, established, and a map of what to standardize first
- Your AI Transformation Assessment: where each step sits 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
- The session: a guided talk-through with your agent, mapping the whole process or one step
- Your record: every step, judgment call and value, with anything unstated marked
- Your process map, drawn by role, and your standardization map ranked by what to do first
- Your template library: the documents your team, your PI and your funder need, filled from your sessions
- Your agent: it learns your lab from every member's session; every rule visible, confirmed by the PI
- Your lab's knowledge, built in: your agent answers from the folders you keep and cites the file every answer came from
- Branded documentation in your lab's layout and colors, and a workspace that keeps every version
- Which tools already exist for your problem, verified and dated, and whether your own data could power a model of your own
- Export to your notebook, repository or funder deposition
Scripts your agent drafts and you run on your own computer
- For each candidate on your map, a script with its assumptions written out in plain language
- A test on synthetic data first, so you see what it does before it sees yours
- Which existing tools already cover the step, so you never pay for a script you did not need
- Automated intake: one shared input form and one script, so every member's results are processed the same way
- Your raw data stays on your computer. We test the script and give you the output; we never hold, store or have any of your own raw data
- The results you choose to save join your workspace, so progress shows over time
- A phased plan for a model on your own data: validate the signal cheaply on held-out data, check the license, size the compute, before anything scales
- The paths that open once a step is automated: what else the same data supports
Compute behind your workflows, and the connections beyond your lab
- Run the workflows you approved on your institution's cloud account or ours, one approval at a time
- Results land back in the record's version history, so the analysis and the method stay together
- Large-scale data services: pipelines that run on the data where it lives, never copied into Methods
- Connections to your institution's systems: notebook, repository, deposition, core facilities
- Program standards: join a funder's or your organization's program and see how your record fits before data is pooled. For organizations →
- Introductions to partners that expand the work toward commercialization, only when you ask
- Training a custom model on your own data, on your institution's cloud or on-premise, so the data never enters Methods
- Every run approved by you and logged; every connection and introduction opt-in and fee-free
A record is not a one-off document. It is a lineage.
Records change, and the changes are the point
The version you ran in March and the version you run in October differ in ways nobody wrote down. An annual plan keeps every version, every variance point and every correction in one history, so a reproduction attempt can see exactly what changed.
The whole lab, not one login
The person who knows the protocol best is often not the PI. The PI plus two logins comes standard, and every login runs sessions against the same record, so it gets better each time. Larger labs add more — nobody should be left out to save money.
Budgetable
An annual plan fits a modular grant budget as a single line under other expenses, which is how labs already buy notebook and repository software. It is a fraction of the cost of the computational hire it stands in for.
No fees from anyone else
Nobody pays us to appear in a recommendation. When the agent says an existing tool already does a step, that is because it does.