Backed by Y Combinator

Orchestrate AI
Co-Scientists

Delegate literature reviews, train models on cloud GPUs, design experiments, and generate publication-ready LaTeX with agents built for scientific execution.

Get Onboarded
npm i -g @synsci/cli
Live Product Walkthrough

4 Modes. 1000s of Workflows.

Switch modes mid-session to change objectives, tool access, and execution style without dropping context.

01 Core

Research

End-to-end research execution: hypothesis framing, literature synthesis, experiment planning, GPU training, evaluations, and manuscript drafting.

02 SOTA

Biology

Wet-lab and computational biology specialist for protein design, genomics, pathway analysis, and biomedical reasoning workflows.

03

Flywheel

Build compounding model improvements from production usage, auto-design fine-tunes, run evaluations, and ship stronger versions continuously.

04

Write

Turn rough notes into publication-ready output with structured arguments, verified citations, clean LaTeX, and camera-ready formatting.

Unified Workspace

Train models and read papers in one flow.

Fine-tune DeepSeek on Tinker GPUs, run evaluations, and review literature in one continuous workspace orchestrated by your agents.

Your Credentials. Your Compute.

Connect once on the dashboard. Credentials sync to every agent and every session.

Universal Credential Sync

Connect once on the dashboard. GitHub, Hugging Face, Weights & Biases, and other API keys automatically sync to every agent and every session.

GPU Orchestration

Provision compute clusters across multiple providers seamlessly.

Deep Integrations

Connect your stack once and coordinate research execution across repos, experiments, vector databases, and cloud runtimes.

GitHub Hugging Face Weights & Biases Modal Prime Intellect Pinecone OpenAlex PubMed

Zero Setup Overhead

Start researching immediately. Everything from package dependencies to Python environments is fully managed by our sandboxed runtime.

Research

Long-Horizon RL Environments for Scientific Research.

We develop RL environments and process-based training data for LLMs, starting with agentic coding environments for ML research workflows.

Agents That Never Stop.

Keep long-running workflows alive in persistent browser runtimes. Start a training run or literature review, walk away, and your agents continue execution autonomously.

Persistent Agent Runtime Always Active in Browser

Persistent Sandboxes

Each agent continues inside an isolated, stateful environment with full checkpointing and deterministic resume.

State recovery without manual setup Every run remains reproducible and resumable Context survives disconnects automatically

Elastic Compute Profiles

Scale from lightweight analysis nodes to full GPU clusters without changing prompts, workflows, or context state.

From CPU sessions to multi-GPU clusters Model runs, evaluations, and writing in one flow Dynamic allocation based on task intensity

Asynchronous Work Orchestration

Queue long evaluations, training loops, and literature synthesis pipelines while your browser session is offline.

Background queues continue while you are away Automatic artifact collection and organization Morning-ready summaries delivered to dashboard
Reliable Checkpointing Continuous Orchestration Cross-Session Continuity

Simple, Transparent Pricing.

Credits cover all included models. Connect your own keys for external services.

Plus
$50/month
For individual researchers.
  • 50 credits / month
  • 4 modes (Research, Biology, Flywheel, Write)
  • Dozens of integrations
  • 3 frontier models included
  • GPU orchestration
  • Community support
Get Started
Enterprise
Custom
For large research organizations.
  • Unlimited credits
  • Everything in Pro
  • Dedicated support
  • Custom integrations
  • On-premise deployment
  • SLA guarantees
Contact Us

All plans include all 4 modes · Credential sync · GPU orchestration