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Synthetic Sciences

Active

Claude Code for Scientific Research

Winter 2026Founded 20252 peopleSan Francisco, CA, USA
AI insightcan contain mistakes
AI Research InfrastructureSaaSResearch scientists, computational labsMedium competition
Moat
Process data from delegated AI-driven research creates network effects; proprietary dataset improves autonomous capabilities.
Key risk
Adoption by researchers requires trust in AI outputs; competing with open-source / free tools is challenging.
Why now
Multi-agent AI and autonomous systems maturity make delegated research feasible; researcher demand for productivity tools increasing.
Competitors
OpenAI Codex, academic research platforms, specialized lab software

About

We’re building the infrastructure for the future AI co-scientist, starting with computational research. Our core thesis is that powerful AI scientists require two things built together: a human-centered product that generates high-quality process data, and the research infrastructure to turn that data into increasingly autonomous systems. We’re building both. SynSci is our platform where scientists delegate complex research tasks to swarms of AI co-scientists across reading → hypotheses → methods → experimentation → results → drafting, even while they sleep.

Founders · 2

Ishaan Gangwani
Ishaan GangwaniFounder

International Olympiad in Artificial Intelligence 2025 (HM). USACO Platinum division (top 0.01% of competitive programmers globally). Prev: IOAI '25; Z-Fellows; Emergent Ventures

Aayam Bansal
Aayam BansalFounder

Built COVID-19 helpline infrastructure for Indian govt serving 50k+ people daily Patented (US, SG, IND) and Sold Orthopaedic AISock for 5-figures at 17 Published at NeurIPS, ICML, ICLR, AAAI, CVPR & other A* conferences Prev: aisock (Acq.), Z-Fellows, Emergent Ventures

Launch

Launched on Y Combinator · Feb 2026
View launch post ↗

Cursor for Scientific Research.

Synthetic Sciences offers a platform where AI co-scientists handle the full research loop—from literature review to hypotheses, experiments, results, and publication drafts—for scientific domains, starting with ML and computational biology. It targets researchers and R&D teams seeking to accelerate scientific work by integrating literature, code, and experiments into a unified workflow.

Formerly InkVell

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