Automate your lab.
Verify every result.
Scivity runs your lab's research loop end to end — agents plan experiments, execute them under your policies, and validate every result before it reaches you. Running on computational research today; physical instrument integration is in development.
Most of what a lab does isn't science. It's the work around the science.
Scivity takes a research question and carries it through the full loop — planning, execution, validation, reporting. Every action is policy-checked, logged, and accountable, so your lab keeps control while the routine work runs itself.
One platform — from hypothesis to a result you can defend.
The engine
Question in. Verified result out.
Three stages, one loop — reasoning, actuation, validation. A research question enters; a conclusion you can defend leaves.
7 parallel
Reasoning Layer
Hypotheses are generated, ranked, and triaged before any compute is spent.
Connection Layer
Experiments run through a controlled actuation layer — GPU compute today, laboratory instruments next. Every action is policy-checked before it executes.
Validation Pipeline
Independent checks and a complete provenance chain before any conclusion is trusted.
How verification works →Every result leaves the platform with a complete provenance chain, and survives independent validation before anyone is asked to trust it.
How verification works →Capabilities
What it does today.
What the platform reliably does for a lab right now. Nothing on this list is a roadmap item or a concept demo.
End-to-end autonomous runs
A research question goes in; a finished, validated experiment comes out. Proven in ML research.
Hypothesis triage
Weak directions are filtered out before they consume compute or your team's time.
Multi-stage validation
Independent checks on every run — errors are caught before a conclusion is trusted.
Reports with full provenance
Structured reports that trace every result back to the code, data, and parameters that produced it.
Coverage
One platform. Any lab.
The loop is the same wherever research runs: plan, execute, validate, report. Computational labs run end to end on the platform today. Physical instruments are in active development — and we mark that difference plainly.
Formal mathematical verification is applied where the domain allows it.
Computational labs
ML and AI, computational biology, chemistry, and physics — labs whose experiments run on compute.
- Autonomous hyperparameter search and architecture optimization
- Multi-agent literature synthesis and hypothesis generation
- Verified results fit for paper submission or production deployment
- GPU compute with reproducibility guarantees
Physical laboratories
Not something you can buy today. The same loop is being extended to instruments through the protocols labs already speak — SiLA 2, OPC UA LADS, PyVISA, MQTT. If you run a physical lab and want in early, talk to us.
Who we are
Who's behind it.
Scivity Labs builds one stack: agents reason, the platform executes, and every result carries its provenance. Researchers supervise the science, not the plumbing.
Common questions
What labs ask first.
Is Scivity software or a wet lab?
Scivity is the connection and autonomy layer between AI and laboratory instruments. Today it runs computational experiments — training, simulations, dataset analysis — where the layer is proven first. The architecture is built to drive physical lab equipment via SiLA 2, OPC UA LADS, PyVISA, and MQTT; that integration is in active development.
What is autonomous today?
End-to-end autonomous execution, validated in ML research. Broader computational domains are rolling out as the platform matures.
Which scientific domains do you support?
We start with computational research — ML and AI, computational chemistry, computational physics, computational biology — because the connection layer is fastest to prove there. The same layer extends to physical instruments.
How do I get access?
Join the waitlist — closed beta opens Q2–Q3 2026. If your lab has a concrete automation problem now, book a call and we'll walk through what the platform can take on today, and what it can't yet.
Is there an API?
Closed beta in Q2–Q3 2026. Public API later in 2026 — MCP-native, framework-agnostic. Book a call for early access.
How are results validated?
Through a multi-stage validation pipeline including code execution, statistical checks, and reproducibility verification. Methodology will be shared later.
Is anything open source?
The platform is proprietary. We share results and industry analysis through our blog and the AI for Science Landscape.
Who is building this?
Vahe Galstyan, founder. Nuclear engineer. LinkedIn is linked from the About section.
Get access
Start with
one experiment.
Join the waitlist, or book a call and walk us through what your lab runs. We'll tell you straight what the platform can automate today — and what it can't yet.
Book a Call
Schedule 30 minutesLocation
Yerevan, Armenia