Mission Brief
Today, ~90% of AI efforts never make it into production—especially in specialized domains like longevity, virtual cells, clinical trial analytics, patents, and biomanufacturing.
ArcellAI is the agentic data layer for techbio-centered Physical AI R&D. Our AI agents handle multimodal ingestion, provenance tracking, and orchestration across your entire stack—so scientific software, lab systems, and models actually deploy, interoperate, and improve over time.
Our mission is to accelerate scientific progress to the speed of software and enable accessible, affordable longevity for everyone.
In critical domains like longevity, virtual cells, clinical trial analytics, bioengineering, and biomanufacturing, most AI never leaves the lab. Models are impressive in slides and benchmarks, but fail when confronted with messy, multimodal, regulated reality.
The failure modes are always the same: fragmented data silos, brittle point-to-point scripts, no tamper-evident provenance, and no orchestration layer that makes AI agents trustworthy enough to operate on real R&D systems.
ArcellAI is built to close this gap: an agentic data layer that sits across lab, software, and model infrastructure, so specialized AI is deployable, auditable, and continuously improving.
Field Report
Observed failure patterns
ArcellAI response
See how ArcellAI transforms data workflows for R&D teams
End-to-end automation across ingestion → transformation → lineage → orchestration → model invocation. Standardized tool APIs and deep integrations turn your data infrastructure into autonomous scientific and engineering workflows. A self-driving semantic layer ensures metrics and calculations remain consistent across teams. Every run versions data, captures lineage, and strengthens your provenance graph.
Automates ingestion → cleaning/transformation → lineage → orchestration across your R&D stack.
Purpose-built agents with domain-specific intelligence via context engineering and tool use.
Versioned datasets, transformation lineage, and auditable workflows captured in a provenance graph lightly anchored on distributed ledger rails for tamper-evident, long-range agent memory.
Autonomously defines and centralizes research metrics, experimental KPIs, and statistical calculations—ensuring consistency and governance across all R&D analytics.
ReAct-style planning with coding and tooling agents orchestrated over foundation models and retrieval—decomposing goals into safe, multi-step pipelines backed by RAG, provenance-aware anchoring, and a governed universal latent space.
API-first, MCP-aligned tool layer across databases, notebooks, LIMS/ELN, lab robotics, and enterprise systems—all exposed as standardized agent tools.
Versioned datasets and transformation lineage anchored on distributed ledgers for tamper-evident lineage, smart-contract-powered reproducibility, decentralized knowledge graphs, and immutable records that strengthen ArcellAI's data flywheel and long-range agent memory.
A visual data science and AI canvas for building and exploring complex workflows: natural-language-to-visual insights, cognitive cartography for mapping data landscapes, spatial and network intelligence for relationship graphs, and interactive, node-based analysis.
ArcellAI turns fragmented data work into autonomous, reproducible workflows—so your team can focus on discovery. Go from raw data to decisions in hours, not months.
The brilliant minds behind ArcellAI's revolutionary agentic data science platform.
Our team information will be revealed soon. Stay tuned for updates on the exceptional researchers and engineers building the future of autonomous data science.
The ArcellAI platform is built on the data view design and API-first paradigm introduced in the PyTDC publication at ICML 2025. PyTDC is a multimodal machine learning platform for biomedical foundation models that unifies distributed data sources and standardizes AI inferencing and benchmarking endpoints.
Ready to revolutionize your data science workflows? Let's discuss how ArcellAI can accelerate your research.