Self-Driving Labs® Technologies for Modern Science

Scalable AI and closed-loop systems, built for diverse lab environments across life-, materials-, and chemical sciences. Flexible solutions backed by years of deep-tech and scientific expertise.

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AI Platform SDLabs

Atinary’s AI-driven R&D platform integrates machine learning (ML) optimizers, data analytics, and visualization into a single intuitive interface, enabling scientists to solve complex, multi-objective optimization challenges faster when designing and running experiments.

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Self-Driving Labs

Atinary is launching its own Self-Driving Lab, a smart data and IP factory in Boston, Massachusetts.

Reimagining the Lab Environment

Break free from traditional bottlenecks and step into the Modern Lab. Atinary is already augmenting scientists by bridging the gap between R&D digitalization and physical experimentation. We move beyond manual trial-and-error by combining AI, robotics, and human expertise to accelerate scientific workflows across life-, materials-, and chemical sciences.

Intuitive and user-friendly interface, without requiring coding or AI expertise

High dimensionality optimization challenges with constraints and multi-objectives using top ML algorithms

Data analytics and visualization module with real-time and interactive charts

Option to integrate with robotics, data management and software platforms to enable to enable Self-Driving Labs® mode

State-of-the-art data and privacy protection: robust data protection hosted on AWS, featuring SOC-2, HIPAA, and GDPR compliance

Explore Our AI Engine

Global Optimization for Real-World Scientific Challenges

Find optimal solutions—faster, smarter, for any parameter type.

Atinary Falcon is our proprietary optimization algorithm, built to tackle the complex challenges in experimental R&D and process development. While traditional methods like one-factor-at-a-time (OFAT) are labor-intensive and Design of Experiments (DoE) struggles with high-dimensional spaces, Atinary Falcon provides an accelerated, explorative-exploitative path to navigate infinite parameter combinations through advanced Bayesian Optimization — all without needing to code or have machine learning expertise.

Use Cases

With the use of Atinary Falcon, the process chemistry team at dsm-firmenich optimized a high-dimensional hydroformylation process. By balancing product yield, selectivity, and catalyst stability simultaneously, they achieved a 10–30x reduction in Rhodium loading, reducing Rhodium cost contributions by 95–97% and cutting reaction times by 50%, proving that sustainability and high performance can be optimized in tandem.

What Makes Falcon Different:

Proprietary Bayesian Optimization algorithm, reimagined: Developed in-house by scientists, Atinary Falcon outpaces conventional Design of Experiments (DoE) and most open-source solutions.

Unmatched speed & scalability: Reduces optimization runtimes by up to 120x compared to other algorithms, dramatically cutting costs and resources.

Truly global search: Excels where others stall—handling huge chemical spaces (>20 million combinations), strict constraints, and multiple objectives.

Real-time intelligence: Utilizes dynamic, live modeling (not just static statistics), guiding experiments as new data streams in.

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Atinary Falcon

Constraint-Driven Intelligence

Master complex, real-world constraints with robust ML.

Atinary Emmental is engineered for R&D environments where complexity is the norm and constraints are non-negotiable. This specialized algorithm lets scientists build feasible, real-world models—ensuring only physically or chemically possible solutions are explored.

Capabilities & Benefits

What Makes Emmental Different:

Solves highly complex, multi-objective, multi-constraint problems beyond the reach of standard Bayesian optimization or DoE tools

Built into Falcon, Emmental explicitly models constraints, ensuring all recommendations are feasible and actionable, a key advantage over many open-source methods that can’t handle constraints or real lab limitations

Outperforms leading open-source and commercial methods (e.g., GPyOpt, BoTorch) in both speed and success rate

Efficient and scalable, optimized for real R&D workflows, from small-scale screening to industrial scale-up

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Emmental

Transfer Learning Acceleration

Leverage your lab’s past data, transfer knowledge and learning to new challenges, and optimize more efficiently from the start with reduced experimental workload.

Capabilities & Benefits

What Makes SeMOpt Different

Semantic Memory Approach: SeMOpt mimics expert intuition, guiding optimization with relevant historical and contextual data.

Meta-Learning & Few-Shot Learning: Rapidly adapts to new unseen tasks or experimental settings with minimal data.

Compound Acquisition Function: Dynamically balances historical knowledge and new observations for optimal search.

Works with Scarce Data: Requires modest historical datasets on source tasks to provide robust guidance for new experiments. No prior data needed on the target task.

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SeMOpt

Self-Driving Labs® Technologies for Modern Science

AI Platform SDLabs

Self-Driving Labs

Reimagining the Lab Environment

Explore Our AI Engine

Global Optimization for Real-World Scientific Challenges

Constraint-Driven Intelligence

Transfer Learning Acceleration

COMPANY

RESOURCES

Self-Driving Labs® Technologies for Modern Science

AI Platform SDLabs

Self-Driving Labs

Reimagining the Lab Environment

Explore Our AI Engine

Bayesian Optimization (Atinary Falcon)

Constraint Optimization (Emmental)

Transfer Learning (SeMOpt)

COMPANY

RESOURCES