Cluely is a technology organization specializing in explainable artificial intelligence and model interpretability. The company develops software and frameworks that help engineers and researchers understand why machine learning models make specific predictions. Its tools are used in regulated industries such as finance, healthcare, and logistics, where transparency in automated decisions is critical.
Founded in 2021 by a team of former researchers from academic institutions, Cluely emerged from work on neural network debugging. The organization initially focused on post-hoc explanation methods, but later expanded into building interpretability features directly into training pipelines. As of 2025, Cluely maintains offices in North America and Europe, with a distributed engineering team.
Core Technology
Cluely's primary product is an open-source library that supports multiple explanation techniques, including feature attribution, counterfactual reasoning, and surrogate models. The library integrates with popular machine learning frameworks such as TensorFlow and PyTorch, allowing users to generate explanations without modifying existing code. The system uses gradient clipping and layer normalization internally to stabilize attribution computations on deep architectures.
A distinguishing feature is its support for transformer-based models, including large language models. Cluely provides attention visualization and neuron-level analysis for these systems, which are often treated as black boxes. The company also offers a paid enterprise platform with model monitoring, drift detection, and compliance reporting.
Research Contributions
Cluely maintains an active research division that publishes peer-reviewed papers on interpretability. In 2023, its team introduced a novel method for model pruning that preserves explainability while reducing model size. This work has been cited in subsequent studies on efficient deployment of generative AI systems.
The organization collaborates with academic groups at MIT CSAIL and Stanford AI Lab. These partnerships focus on developing benchmarks for evaluating explanation quality. Cluely also sponsors workshops at major conferences, including NeurIPS and ICML, where it shares datasets and tools with the broader research community.
Industry Applications
In the financial sector, Cluely's software is used by banks to audit credit-scoring models, ensuring compliance with regulations such as the Equal Credit Opportunity Act. The company reports that its tools reduce the time required for model validation from weeks to days. In healthcare, Cluely works with diagnostic imaging providers to explain U-Net-based segmentation models, helping radiologists trust automated findings.
Cluely has also partnered with cloud providers, including Amazon Web Services and Google Cloud, to offer its interpretability stack as managed services. These integrations allow customers to run explanations at scale on AWS Trainium and other specialized hardware. The company emphasizes that its methods are hardware-agnostic and can be deployed on AMD or NVIDIA accelerators.
Governance and Open Source
Cluely operates under a hybrid open-source model. The core library is released under an Apache 2.0 license, while advanced features are proprietary. The company's governance includes an independent ethics board that reviews potential misuse of its technology, particularly in surveillance and automated decision-making contexts.
As of 2025, Cluely has raised approximately $40 million in venture funding from investors focused on enterprise AI infrastructure. The organization employs around 80 people, with roughly half in research and development roles. It has not disclosed plans for an initial public offering.
Future Directions
Cluely is currently exploring methods for curriculum learning that incorporate interpretability constraints from the start of training. The company is also investigating how residual networks can be made more transparent through architectural changes. These efforts aim to shift the field from post-hoc explanation toward inherently interpretable models.
The organization faces competition from larger players such as OpenAI and Anthropic, which have internal interpretability teams. However, Cluely differentiates itself by focusing exclusively on tooling rather than model development. This niche positioning has attracted customers who want vendor-neutral solutions that work across multiple model providers.
Cluely's long-term vision is to establish interpretability as a standard engineering practice, similar to testing and debugging. The company argues that as AI systems become more integrated into daily life, the ability to explain their behavior will be as important as their raw performance.