AI & Machine Learning
Build a scientific research platform
Kliv is an AI app builder. Describe your scientific research platform, and it builds the database, workflows, collaborator access, validation rules, and archive-grounded agent around your study.
Just enter your idea into the text box and AI will build it for you
What is Kliv?
Kliv isn’t an electronic lab notebook or a fixed data product — it’s an AI that builds software for you.
You describe the app your study needs in plain words, and Kliv builds it: the database, the screens, the validation rules, the collaborator access, the archive, and the typed endpoints. You own it, run it on your own accounts, and change it later by asking. It isn’t a template.
On this page, that app is an AI-driven scientific research platform. Kliv builds all kinds of web apps; this page focuses on research data, protocols, instruments, collaborators, and archive-grounded answers.
Why a lab needs more than a shared drive
A freshwater ecology lab with many field seasons may have a continuous record of dozens of sites. The problem is often where that record lives: folders, old directories, inconsistent file names, and protocol details held in people’s heads.
Kliv builds a platform shaped around the study design. Sites, campaigns, samples, instruments, protocol versions, and collaborators become records with rules. The archive becomes searchable, and the lab no longer depends on who remembers which folder matters.
One study’s records
The records a field research group keeps, and what each one enforces.
Sites and campaigns
Every lake is a site with coordinates and history; every season is a campaign with a plan. Each measurement points back to both, so “Lake Vänern, August, all years” is a query.
Samples and measurements
Each reading carries its site, instrument, protocol version, and logger. Units are checked at entry, so impossible values are rejected before they reach analysis.
The protocol shelf
SOPs, method papers, and meeting minutes are versioned. A 2019 series stays attached to the protocol that was current in 2019, and the agent answers from that shelf.
Instruments and calibration
Every probe has a calibration log. A reading logged against an overdue instrument is flagged as soon as it lands.
Roles and collaborators
PIs, postdocs, field technicians, and partner universities sign in with different access. Unpublished campaigns stay visible only to the project team.
What Kliv adds to the bench
Not analysis software. This is the system of record around the analysis you already run.
An agent that has read the archive
A question such as “What fixative did the 2019 zooplankton series use?” can return the answer and the SOP passage it relied on, from the version in force that season.
Search that survives turnover
Semantic search over the lab’s own archive can surface the right field note even when the file name is not helpful.
A typed endpoint for pipelines
Dataloggers and the R pipeline can post results to a typed server-side function. A malformed batch is rejected with a reason.
Rows that respect embargoes
A partner university’s login sees the shared campaign and nothing beyond it. The restriction is enforced on each row, not by which link someone has.
Dashboards for operations and grants
Campaign completeness, instrument downtime, and samples per site per season come from the same rows the science uses, with numbers traceable to records.
An example: Dr. Elin’s freshwater ecology lab
Here’s how one lab might use a research platform built with Kliv. It’s only an example — you would describe your own study design, records, roles, and validation rules.
The study structure becomes the database
Dr. Elin describes 40 lake sites, annual field campaigns, samples, instruments, protocol versions, and collaborator roles. Kliv builds the app around those records.
Field data is checked as it arrives
A measurement carries its site, instrument, protocol version, unit, and logger. If the value is impossible or the instrument calibration is stale, the app flags it immediately.
The archive answers protocol questions
A new student asks which fixative was used in the 2019 zooplankton series. The agent answers from the SOP version attached to that season and cites the passage.
Collaborators see only shared work
A partner university signs in and sees the campaigns shared with them. Unpublished campaigns from another project do not appear.
Analysis stays where it belongs
The lab keeps R and Python in place. The pipeline posts results to a typed endpoint, and provenance ties derived numbers back to the samples.
The grant record outlives turnover
When a student graduates, the samples, calibration logs, protocol history, and archive remain in a database the lab controls.
Describe the study platform you need
Kliv builds from your description, so include the records, validation rules, roles, archive documents, and external pipelines you need. Here are three examples:
Field ecology lab
Sites, campaigns, protocol-tagged measurements.
“Build a research data platform for our freshwater ecology lab. Track 40 lake sites, field campaigns, samples, measurements, instruments, calibration dates, protocol versions, and SOP documents. Let collaborators sign in with role-based access and let our R pipeline post validated results to a typed endpoint.”
Materials test archive
Specimens, rigs, methods, and procedures.
“Build a platform for our materials lab. Track specimens, tensile test results by rig, calibration logs, method versions, operator notes, and an agent that answers from our uploaded procedures and cites the source document.”
Cohort study tracker
Participants, visits, sites, and instrument versions.
“Build a longitudinal study platform. Track participants, visits, questionnaire scores by instrument version, per-site data entry, role-based access for each research site, and exportable tables for our statistics pipeline.”
From shared drive to system of record
The archive becomes searchable. Upload SOPs, method papers, and lab minutes into the knowledge base. The agent answers with the passage it relied on, and every exchange is stored for audit.
The data is not locked away. Measurements are rows in a database the lab controls, tables can download when needed, and the app’s source syncs to a repository the lab owns.
Analysis code stays where it is. Kliv does not run your statistics. R, Python, and other analysis tools stay in place; Kliv builds the system of record around them.
An agent grounded in a private corpus is a pattern with legs well beyond one lab — the AI-apps overview collects what else it can carry.
Common questions
What exactly is Kliv?
Kliv is an AI app builder. You describe the research platform you need, and Kliv builds the database, screens, access rules, validation, archive, and agent around it.
Do I get a real app, or a template?
A real app. It is built around your study’s sites, samples, protocols, roles, and pipelines, not a generic lab notebook layout.
Can I change it after it’s built?
Yes. You can add fields, change validation rules, add collaborators, revise dashboards, or add a new endpoint by asking.
Does the agent invent answers?
It answers from the protocol shelf and from rows the asker may read, citing the passage it used. If the archive does not contain the answer, it should say so.
Can collaborators at another university join?
Yes. They sign in with their own accounts and see the campaigns you share with them. The restriction is enforced on rows, not links.
Who owns the data when the grant ends?
The lab does. Measurements are in your database, downloads are yours, and the app’s source lives in your own Git repository.
Does Kliv run our statistics?
No. Your analysis code stays in R, Python, or wherever it lives today. Kliv receives outputs through typed endpoints and keeps provenance tied to samples.
Can we reject bad data before it lands?
Yes. Typed server-side functions can validate incoming batches and reject malformed rows with reasons before they reach the tables.
Can dashboards use the same records as the research data?
Yes. Dashboards for completeness, downtime, and sampling coverage read from the same rows as the research records.
Can I move off Kliv later?
Yes. The code syncs to your own Git repository, and your data can be exported from the database you control.
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Put the study where the lab can trust it
Describe the sites, samples, instruments, protocols, roles, and validation rules. Kliv builds the research platform around those details.
