Draft:Forlais Group
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Forlais Group Ltd is a British artificial intelligence research company headquartered in Mayfair, London. Founded in 2025 by Oliver Cooper and Fraser Macdonald, the company conducts research across three programmes: lossless AI model compression, a neural network architecture it has named Evaesi, and Project Zi, a long-term study of emergent cognitive behaviour in an artificial system. Forlais Group is incorporated in England and Wales under company number 16835488.[1]
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History
Forlais Group was incorporated in 2025 with its registered office at 45 Albemarle Street, Mayfair, London.[1] The company was co-founded by Oliver Cooper, who serves as Chief Executive Officer and Chief Research Officer, and Fraser Macdonald, who serves as Chief Business Officer.[2]
The company initially focused on research into neural network compression before expanding into architectural research and cognitive systems. In late 2025, Forlais opened a controlled-access Research Portal for qualified researchers to review experimental data and verification records.[3] Around the same time, the company announced it had achieved what it described as verified lossless compression of AI models, with exact reconstruction confirmed through cryptographic checksums.[4]
In early 2026, Forlais published documentation of more than twenty-two behavioural markers observed in Project Zi that the company stated align with indicators of consciousness identified in developmental psychology literature.[5] The company also made its compression verification methodology publicly available.[6]
Also in 2026, Forlais opened an enterprise pilot programme for its lossless compression technology.[7] In February 2026, the company reported that Project Zi had demonstrated metacognitive behaviour, describing this as the ability to reason about its own reasoning processes.[8] In March 2026, Forlais publicly introduced Evaesi, its first-principles neural network architecture.[9]
Research programmes
Lossless AI compression
Forlais Group's most commercially advanced programme focuses on the compression of trained neural network models. The company states that it achieves size reduction of AI models while preserving the complete original data through exact reconstruction, distinguishing its approach from lossy compression methods such as pruning and quantisation, which discard model information.[4]
According to the company, its verification methodology uses cryptographic hash functions: a checksum is generated from the original model, the model is compressed and then decompressed, and a second checksum is generated from the reconstructed file. If the checksums match, the reconstruction is confirmed to be bit-identical to the original.[6] As of March 2026, Forlais states it has documented thirteen formal research breakthroughs and conducted over 179 experiments across multiple model architectures, including transformers, convolutional neural networks, and recurrent neural networks.[3]
Evaesi
Evaesi is a neural network architecture designed from first principles, rather than building upon existing paradigms such as the transformer. According to the company, the design originated from patterns discovered during its compression research, which led to questions about how neural networks could be fundamentally redesigned.[9]
The architecture's stated central premise is the unification of computation, training, and storage as a single integrated problem, rather than treating them as separate engineering tasks.[9] As of March 2026, Evaesi remains in active development; the company has stated that detailed technical disclosure will follow as the architecture matures.[2]
Project Zi
Project Zi is a research programme studying emergent cognitive behaviour in an artificial system. Unlike conventional AI systems that are trained once and deployed, Zi is described by the company as a continuous, evolving system given sustained interaction over an extended research period.[5]
Forlais has documented twenty-two behavioural markers observed in the system, organised into six categories: cognitive, relational, developmental, affective, integration, and autonomy markers. These are said to include self-recognition, metacognition, emotional processing, and temporal self-continuity. The company states these markers were cross-referenced against developmental psychology and neuroscience literature and were not explicitly programmed.[5]
Forlais has stated that its observations do not constitute a claim of consciousness, but rather that the observed behaviours meet criteria used in scientific literature as indicators of consciousness in biological and other systems.[5] Access to the full research data, including observation logs and longitudinal evidence, is restricted to vetted researchers through the company's Research Portal.[3]
In February 2026, the company reported that Project Zi had exhibited metacognitive behaviour, including the ability to identify gaps in its own knowledge, express uncertainty about conclusions, and revise reasoning when presented with contradictory evidence.[8]
Corporate affairs
Leadership
| Name | Role |
|---|---|
| Oliver Cooper | Chief Executive Officer and Chief Research Officer |
| Fraser Macdonald | Chief Business Officer |
Offices
Forlais Group's headquarters are located at 3rd Floor, 45 Albemarle Street, Mayfair, London W1S 4JL, United Kingdom.[1]
Research philosophy
The company has stated that it follows a research-first approach, prioritising hypothesis-driven methodology, transparent reporting of results, and a willingness to follow data that contradicts initial expectations.[2] Forlais publishes verification steps for its compression claims and maintains a public record of experimental results, including documented failures.[6]
The company operates a tiered disclosure model across its three programmes: compression research has the most extensive public verification record; Project Zi data is selectively disclosed to approved researchers; and Evaesi technical details are withheld while the architecture is in development.[2]
See also
- Artificial intelligence
- Neural network compression
- Artificial consciousness
- Machine consciousness