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The name

conspecta

Latin, from cōnspicere

to catch sight of; to observe, perceive, behold.

Science starts when someone catches sight of something. We build the trail of everything that follows.

2024 · first experiments

Built from conversations

The work started as experiments in North Carolina through 2024, and it has been shaped ever since by conversations with scientists across very different labs.

Geography helped. Wake Forest, Duke, UNC-Chapel Hill, NC State, and the labs of Research Triangle Park all sit in this corner of the United States, so the scientists we were building for were never far away.

Those conversations are why the workspaces take the shape they do. Each one traced something a working lab actually does, and fed it back into what we were building.

  • Microscopy groupsimaging & counting
  • Flow cytometry labsgating & stats
  • Genetics teamssequences & constructs
  • Academic coresshared instruments
  • Commercial R&Dsign-offs & IP

meanwhile, in most labs

The work is connected. The tools aren't.

Image analysis happens in one application. Samples live in spreadsheets and freezer logs. Plans end up on whiteboards, and figures get rebuilt by hand for slides.

  • Desktop image toolexports to ~/Desktop
  • Flow softwareone licensed PC
  • Sample spreadsheetv7_FINAL(2).xlsx
  • Freezer logpaper, taped to box B

Nothing connects any of it, and reproducing a result months later becomes guesswork.

what we built instead

One platform where everything stays connected

Samples, images, image analysis, tables, figures, and sign-offs live in one place, and every link between them is made as the work happens, not reconstructed afterward.

One result, fully traced

  • SampleHeLa-P12
  • Image analysisrun 4
  • Tablecounts · tbl 3
  • FigureFig. 2a
  • Sign-off✓ v3 · approved

what it earns you

It builds itself as you work

The trail comes from the actions themselves: a sample logged, a run started, a page signed off. All of it is a byproduct of doing the work.

Results you can defend.

A reviewer questions a number a year later, and you follow the figure straight back to the sample it came from. Nothing to reconstruct out of file names, memory, and whoever is still in the lab.

Grants that renew.

Writing the progress report doesn't start with digging up a year of work. It's already in the project with dates attached, so the report is a summary, and no experiment goes uncounted because nobody could find it.

IP that holds up.

When a patent attorney asks who invented what and when, the dates and names were set as the work happened, not assembled afterward, which is what makes them count. Edit a signed-off version and the approval flags itself for re-check.

today

The AI enablement platform for the lab

What keeps your science defensible is exactly what an assistant needs to be useful.

Your assistant already works here

Bring the model of your choice. Conspecta speaks MCP, the open standard for connecting assistants to real tools. Your assistant works from the connected project itself, so everything it writes, runs, and answers is grounded in your data instead of guessed.

Count the colonies in the new image set and save the numbers.

next

When the lab starts to run itself

A handful of companies are building self-driving labs: machines that plan experiments, run them, learn, and go again. How far that gets, and how fast, is an open question our argument doesn't depend on, because one thing stays on the same side of the line wherever it lands.

What keeps moving to the machine

  • Running the analysis
  • Drafting the write-up
  • Proposing what a result might mean
  • Suggesting what to try next

The first two are routine today. The rest may take longer than anyone expects, or arrive sooner. We aren't betting on which.

What doesn't move

Deciding what the lab puts its name on, and being able to show what that rests on: which sample, which settings, which decisions, and who approved it.

A model can produce an interpretation. It cannot be accountable for one.

our position

However much of the bench ends up automated, science that runs itself still answers to a scientist.

Frequently asked questions

Who is Conspecta for?
Research teams in academia and industry who work with images, samples, or both, and anyone who needs to go from raw data to interpretable, shareable results without stitching together a pile of disconnected tools.
What does Conspecta replace?
The patchwork most labs assemble: a desktop image tool, separate flow cytometry software, spreadsheets for samples and freezer contents, a lab notebook or shared docs, a stats and graphing package, and a project board. Conspecta brings those jobs into one connected platform.
Do I need to install anything?
No. Conspecta runs in the browser on any operating system, and storage, backups, and updates are handled for you, so there's nothing to install and nothing tied to one machine.
Is my research data secure and private?
Yes. Your data is encrypted in transit and at rest, isolated per project, automatically backed up, stored in the United States, and never sold or used to train our models. See our security and privacy practices.
Do I own my data, and can I get it out?
Your data is always yours. You can export everything at any time in standard, open formats, and we don't lock your research in.
How long has Conspecta been around?
Conspecta became a company in 2025 in North Carolina, in the United States, built on work that started as experiments in 2024. It has been shaped since by conversations with working scientists, and used on real research.
Where is Conspecta based?
Conspecta is based in North Carolina, in the United States, near Duke, UNC-Chapel Hill, NC State, and the labs of Research Triangle Park. It runs in the browser, so labs anywhere in the world can use it.
Can my whole lab work in Conspecta together?
Yes. Conspecta is built for teams. Projects are shared, work stays in one place, and each member sees the projects they belong to.

Want to learn more?

Reach out and we'll walk you through how Conspecta fits the way your lab already works.