Mission Brief
Everyone thinks better models will unlock the real world. They won't. Deployment requires context. That's what we're building.
ArcellAI replaces fragmented data science and engineering with a unified, agent-native context layer—making AI systems deployable, auditable, and scalable across techbio, healthcare, and scientific R&D.
Our mission is to accelerate scientific progress to the speed of software.
90% of AI deployments fail to reach regulated production. Models look strong in notebooks, but break against messy, multimodal, regulated reality. AI fails at the system layer, not the model layer.
80% of engineering time is spent on data preparation. Fragmented data silos, brittle one-off pipelines, no trustworthy provenance, and no orchestration layer that makes AI agents safe enough to operate on real systems.
ArcellAI is built to close this gap: an agent-native context layer that sits across lab, software, and model infrastructure—making scientific and engineering AI deployable, auditable, and continuously improving.
Field Report
Observed failure patterns
ArcellAI response
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 context graph—enabling traceable, replayable runs and 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 and provenance-aware context.
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 captured in a proprietary context graph architecture—enabling traceable, replayable workflows, deterministic audit trails, and long-range agent memory.
Run agents as production systems with reliable execution and state. Agents plan experiments, select relevant data, and iteratively refine hypotheses with memory and ensemble reasoning.
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.
ArcellAI is built on the unified data architecture and API-first paradigm from the PyTDC publication at ICML 2025 (Velez-Arce et al.). 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.