Callosum
Category: AI Infrastructure
System-level software platform that orchestrates AI workloads across heterogeneous chips and models to enable 'Heterogeneous Intelligence' — diverse AI models running on different hardware types working together as an integrated, co-evolved system. Callosum was founded in 2024. The company is led by Danyal Akarca. Based in London, United Kingdom. Team size: N/A. Total funding raised: $10.25M. Latest round: Pre-Seed. Key investors include ["Plural (Lead investor - European VC firm)","ARIA (Advanced Research and Invention Agency - UK government R&D funding)","Charlie Songhurst (Angel investor)","Stan Boland (Angel investor - FiveAI)","John Lazar (Angel investor - Royal Academy of Engineering)"].
- Founded
- 2024
- Headquarters
- London, United Kingdom
- Team size
- N/A
- Total funding
- $10.25M
Value proposition
Delivers 2x accuracy, 7x faster performance, and 4x lower cost compared to homogeneous single-chip solutions by exploiting diversity across models and hardware. Achieves up to 12x cost reduction on specific workflows while setting new SOTA on benchmarks using only open-source models.
Products and solutions
["Heterogeneous Intelligence Platform (core orchestration software)","Heterogeneous Recursion Engine (deep context reasoning across mixed models/chips)","Heterogeneous Vision-Language-Action System (web automation and multi-modal agents)","Topology-aware Cache Management System (optimal eviction and pre-fetching)","On-die Grammar Enforcement Kernels (custom silicon optimization for AWS Inferentia2)"]
Unique value
First company to co-evolve heterogeneous chips and intelligence together, inspired by brain architecture principles — the human brain achieves intelligence through diverse specialized cell types working together, not by copying one neuron billions of times. Vertically integrated 'Intelligent System' where workflows are hardware-aware, models are task-graph-aware, and kernels are output-constraint-aware, with every layer co-optimized in context of the whole.
Target customer
Companies building multi-agent AI systems requiring superior performance across complex workflows; emerging chip manufacturers seeking to demonstrate hardware capabilities at scale; enterprises with heterogeneous AI workloads (automation, inference, complex reasoning)
Industries served
["AI Infrastructure","Enterprise AI & Automation","Cloud Computing","AI Chip Manufacturing","Data Centers","Inference-Optimized Compute"]
Technology advantage
Combines neuroscience-inspired heterogeneous architecture with full-stack optimization (from kernels to workflows). Achieves: 2x accuracy/7x speed/4x cost improvements on complex workflows; 25% improvement over ICLR 2026 SOTA on Visual WebArena using zero frontier API calls; up to 12x cheaper workflows (SambaNova GPT-OSS-120B vs GPT-5); 2.4x speedup via topology-aware caching; 1,767x faster grammar enforcement at batch-64 through on-die masking; multi-cloud/multi-chip support (AWS, GCP, Azure across Nvidia, AMD, Cerebras, SambaNova, Trainium/Inferentia).
How they differentiate
First company to co-evolve heterogeneous chips and intelligence together with brain-inspired architecture. Achieves 2x accuracy, 7x faster performance, and 4x lower cost compared to homogeneous single-chip solutions by orchestrating AI workloads across diverse hardware (Nvidia, AMD, AWS Trainium/Inferentia, Cerebras, SambaNova) with neuroscience-inspired heterogeneous computing principles.
Main competitors
["Ray/Anyscale","Nvidia","Cloud providers with custom chips (Google TPU, AWS Trainium/Inferentia)"]
Key partnerships
["AWS (custom NKI kernels for Inferentia2 silicon, on-die grammar enforcement)","Cerebras (heterogeneous recursion benchmarking and deployment)","SambaNova (heterogeneous recursion configurations)","Coworker AI (enterprise production partner for GitHub workflow benchmarks)","ARIA/UK Government (£50M Scaling Inference Lab access for chip validation)","Google DeepMind (founder research collaboration history)","Photonics/interconnect companies (next-gen data center connectivity R&D)","Academic: University of Cambridge, Oxford, MIT, ETH Zurich, Imperial College London (founder backgrounds and ongoing research ties)"]
Notable customers
["Coworker AI (enterprise production partner for GitHub workflow benchmarks)"]
Major milestones
["Company founded in November 2024 (UK Companies House registration #16076949)","Emerged from stealth mode in February 2026","Raised $10.25M pre-seed round led by Plural with support from ARIA","Published first technology results demonstrating heterogeneous recursion capabilities","Developed custom NKI kernels for AWS Inferentia2 silicon","Achieved benchmark results beating single-call Claude Opus 4.5 at every context length"]
Growth metrics
Recently emerged from stealth mode in February 2026 with $10.25M pre-seed funding; demonstrated 25% improvement over ICLR 2026 SOTA on Visual WebArena using zero frontier API calls; achieved up to 12x cost reduction on specific workflows
Market positioning
Early-stage UK-based AI infrastructure startup challenging Nvidia's dominance by enabling multi-model, multi-chip AI workloads. Targets two customer segments: companies building multi-agent AI systems requiring superior performance, and emerging chip manufacturers seeking to demonstrate hardware capabilities at scale.
Geographic focus
United Kingdom (London), Europe, with global reach through multi-cloud platform support (AWS, Google Cloud, Microsoft Azure)
Patents and IP
No registered patents publicly disclosed as of February 2026. Core IP resides in proprietary heterogeneous orchestration algorithms, topology-aware runtime, and custom silicon kernels.
About Danyal Akarca
Cambridge-trained computational neuroscientist and medical doctor. PhD candidate at University of Cambridge's MRC Cognition and Brain Sciences Unit, researching generative models of brain networks. Previously Research Fellow at Medicalchain. Co-author of research published in Nature Machine Intelligence on brain-inspired AI systems.
Official website: https://www.callosum.com