Real models. Full-capacity GPU. No mock data.

Genomics pipelines,
built visually.

Drag, connect, run. Genovera AI turns a canvas into real science — DNA annotation, structure prediction, protein design and functional analysis, executed on live models, not stubs.

30+ node types RTX 6000 Ada · 48 GB Real-time collaboration

Powered by production models & databases

NT-v2-500mESMFoldAlphaFold2ProteinMPNNOpenMMNCBI BLASTUniProtKEGGInterPro
The canvas

Watch a pipeline build itself.

Every node is a real model. Drop them on the canvas, connect the wires, and watch data flow from raw sequence to finished result.

protein-design.pipeline
running
Upload
FASTA file
Annotate
NT-v2 · 6 tasks
ESMFold
3D structure
ProteinMPNN
redesign
Export
results out
The platform

One canvas. The whole bioinformatics stack.

Thirty-plus node types — every one wired to a real model or public database. Connect them however your science demands.

Gene annotation

NT-v2-500m fine-tuned with LoRA across six tasks — gene regions, splice sites, biotype, promoters, CpG islands and repeats — windowed for full chromosomes.

Genomics

Structure prediction

Fold any sequence with ESMFold on-GPU or pull experimental structures from AlphaFold2. Per-residue pLDDT rendered live in a 3D viewer.

Folding

Protein design

Inverse-fold and optimize with ProteinMPNN, then relax and score stability with OpenMM CUDA molecular dynamics.

Design

Functional analysis

BLAST → UniProt → InterPro, plus KEGG pathways, GO enrichment and STRING interactions stitched into one annotated report.

Annotation

Connect any source

Pull data straight from S3, GCS, Azure Blob, Google Drive, OneDrive or FTP/SFTP — reference or import, with credentials encrypted at rest.

Sources

Structured storage

Nested folders, trash & restore, quotas, provenance and expiring share links — every output tracked back to the run that made it.

Data
Under the hood

No mock execution.
Just the real thing.

Every node calls the model it claims to. Inference runs on a dedicated NVIDIA RTX 6000 Ada with 48 GB of VRAM, reached over a managed tunnel — so a fold is a fold, and a design is a design.

48 GB
RTX 6000 Ada VRAM
Kahn
topological scheduler
Provenance
on every output
30+
composable nodes
node → model
gene_annotation
NT-v2-500m + LoRA
6 tasks
esmfold
facebook/esmfold_v1
on-GPU fold
protein_designer
ProteinMPNN
inverse design
md_simulation
OpenMM CUDA
stability score
functional_annotator
BLAST · UniProt · InterPro
KEGG · GO · STRING
ncbi_blast
NCBI BLAST REST
submit / poll
0
fine-tuned NT-v2 tasks
0+
composable node types
0 GB
RTX 6000 Ada VRAM
0 bp
sequence context window
How it works

From idea to insight in three moves.

01

Compose

Drop nodes on the canvas and wire them edge-to-edge — or describe your goal and let the AI builder lay it out for you.

02

Run

A topological scheduler dispatches each node to its model in order, streaming logs and partial results as they land.

03

Inspect

Watch structures render in 3D, read functional reports, and trace every artifact back to the exact run via provenance.

Priya
Folded the variant — pLDDT jumped to 0.81 🎉
Nice. Pipe it into ProteinMPNN for stability?
Arjun
On it — wiring the design node now. Running in ~40s.
Built for teams

Science is a team sport.

A pipeline doubles as a collaboration room. Edit the same canvas, talk it through, drop 3D structure cards into chat, and review results together — all without leaving Genovera.

Multiplayer canvas

Live cursors, presence and Yjs CRDT co-editing — your team shapes a pipeline together, in real time.

Teams & organizations

WhatsApp-style group chat, org tenancy, a connection graph and 1:1 DMs — built in, not bolted on.

Scoped & private

Discovery is limited to your org and accepted connections. Operator-provisioned orgs, verified domains, clean offboarding.

Bring your sequences. We'll bring the GPUs.

Spin up your first pipeline in minutes — no install, no setup, no mock data.