Real-time intelligence for machines that move.
Ogbon is bringing ultrafast inference to robots, drones/edge that must act in real time.
01 / The problem
The device is too small. The cloud is too far.
Physical AI has to sense, decide and act in milliseconds.
A robot arm, a drone or an inspection rig works inside a tight control loop. Miss it, and the machine hesitates, drops the part, or crashes.
The models good enough to act in the world don’t fit on the device. So teams shrink them until they fit, and lose the intelligence they wanted.
The public cloud is a region away: long, jittery round trips on GPUs tuned for chat, not for a loop that needs an answer every few milliseconds.
No one owns the full path from model to network to machine with real-time as the single goal.
02 / Why now
Physical AI is moving from demos to deployment
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Foundation models for machines are here. Vision-language-action models let one model drive many tasks. They are large, and getting larger.
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Fleets are coming. Humanoids, warehouse robots, drones and inspection rigs are entering production. Every unit needs inference, every second it runs.
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Inference is the recurring cost. Training is paid once. Inference is paid per machine, per hour. Whoever makes it fastest and cheapest owns the margin.
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Networks can now carry it. Private 5G, fibre to industrial sites and cheap local compute make a site or city inference cloud practical.
03 / How it works
Device. Site. City.
One stack, tuned across all three, decides what runs where.
The machine keeps its reflexes on the device. Heavier models run on a cluster close enough to stay inside the control loop.
Where each decision runs
Device
Reflexes and safe fallback
Site
Heavy models, on your floor
City
Shared capacity, close by
Our SDK runs on the machine and handles what must happen instantly. Heavier thinking streams to an Ogbon cluster on your site or in your city. If the link drops, the machine falls back safely on the device.
04 / What we build
We own the whole latency stack.
From the model to the network to the machine, every layer is built for one thing: real-time.
Bring your model and connect your machines. We handle the compute, and tune every layer together.
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01
Model compression
Distillation, quantisation and pruning, tuned per model and per hardware target. Big-model behaviour at a fraction of the compute.
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02
Custom kernels
Hand-written GPU kernels for the operators physical AI runs most, recovering the latency general-purpose runtimes leave behind.
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03
Real-time chunking
Splits work between device and cloud and streams actions in chunks. The machine keeps moving while the next chunk computes.
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04
Specialised cloud
Inference clusters at your site or in your city, tuned for control-loop latency, not chat throughput.
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05
SDKs
Libraries for robots, drones and edge devices, with fallbacks, scheduling and telemetry built in. Drop it in, and real-time just works.
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06
Full solutions
Where our latency and cost edge is largest, we run the whole stack for you: manufacturing lines, drone fleets, inspection and warehouses.
05 / Who it’s for
Built for fleets.
Anywhere many machines need to think in real time, in one place or across a city.
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Robotics teams
Shipping manipulation, humanoids and autonomous mobile robots.
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Drone operators
Running inspection, delivery and mapping fleets, up to whole city drone corridors.
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Industrial sites
Factories, warehouses, ports and mines running many machines in one place.
How we start
- 01
Bring one model. The one that hits the latency wall hardest today.
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We prove the drop. Compressed, tuned and served from a cluster near your machines.
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Expand across the fleet. Pay per machine, with a latency guarantee.
Why Ogbon
We hit the wall first.
We built AI on edge hardware and learned exactly where compute runs out. Ogbon is the stack we wished we had: research-grade optimisation, run as infrastructure.
06 / Research
Applied research, shipped as product.
We publish what we learn about making large models fast and cheap in the physical world.
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Benchmarks
Latency and cost, measured on the models physical AI actually runs.
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Methods
How we compress, schedule and stream large models inside a control loop.
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Results
What worked, what didn’t, and the numbers behind both.
07 / Join us
Help us build something that doesn’t exist yet.
We’re assembling a founding team of exceptional engineers and researchers to make large models run in real time in the physical world. We hire in Lagos, in London and remotely.
We’re looking for people who enjoy difficult problems, question conventional approaches, and want to push the boundaries of computing.
You don’t need a PhD or experience at a major technology company.
You need curiosity, strong technical ability, and the willingness to experiment.
Head of Engineering & Research
Lead Ogbon’s technical research, develop the computing architecture, manage experiments, and guide the engineering team.
Responsibilities
- Set the research agenda and turn latency targets into a sequence of testable milestones.
- Design the overall architecture across compilation, scheduling, and distributed execution.
- Define how experiments are run, measured, and reported so results can be trusted and reproduced.
- Hire, mentor, and lead the founding engineering and research team.
- Make hard technical tradeoffs, including deciding when an approach should be abandoned.
Requirements
- Deep experience building performance-critical systems: compilers, runtimes, databases, operating systems, or HPC.
- A strong grasp of computer architecture: caches, SIMD, memory bandwidth, and interconnects.
- A record of leading technically ambitious projects from idea to working system.
- Rigour about measurement and honesty about negative results.
- The ability to explain complex ideas clearly to engineers and non-specialists.
Senior C++ Engineer
Build high-performance software and optimise AI model execution on GPUs and edge hardware.
Responsibilities
- Write and optimise the kernels and runtime that execute model operations on GPUs and edge devices.
- Profile hot paths and remove bottlenecks in compute, memory access, and threading.
- Use SIMD instruction sets such as AVX2, AVX-512, and NEON where they make a measurable difference.
- Build benchmarks and tests that keep performance from regressing.
- Work with research engineers to turn ideas into fast, correct code.
Requirements
- Strong modern C++ (17 or later) and a solid understanding of what the compiler generates.
- Experience with profiling tools such as perf or VTune.
- A clear understanding of caches, memory layout, and multithreading on CPUs and GPUs.
- Experience with numerical or high-performance code is a strong plus.
- Comfort reading assembly when needed.
Senior Backend Engineer Distributed Systems
Develop distributed systems that coordinate AI workloads across devices, site clusters and city clusters.
Responsibilities
- Design the coordination layer that splits AI workloads between machines and nearby inference clusters.
- Build scheduling, partitioning, and data-transfer systems with minimal overhead.
- Handle failures, stragglers, and network variability without losing throughput.
- Instrument the system so latency and throughput can be understood end to end.
- Contribute to decisions on protocols, serialisation, and networking.
Requirements
- Experience building distributed systems in production or research settings.
- Strong programming ability in C++, Rust, Go, or a similar systems language.
- A solid grasp of networking fundamentals and bandwidth and latency tradeoffs.
- Familiarity with consensus, replication, or distributed scheduling.
- Clear thinking about correctness under concurrency.
Systems Engineer Linux & Performance
Optimise Linux, latency, memory usage, and parallel execution on inference servers and edge devices.
Responsibilities
- Tune Linux for compute-heavy workloads: scheduling, NUMA, huge pages, and interrupts.
- Measure and optimise memory usage, cache behaviour, and parallel execution.
- Build tooling that profiles experiments across many machines.
- Investigate performance anomalies down to the kernel and hardware level.
- Document findings so the rest of the team can build on them.
Requirements
- Deep Linux knowledge, including kernel-level performance concepts.
- Experience with perf, eBPF, flame graphs, or similar tooling.
- An understanding of NUMA, CPU topology, and the memory hierarchy.
- Systems programming and scripting ability in C, C++, Rust, Python, or shell.
- Patience and curiosity for problems nobody has documented yet.
AI/ML Research Engineer
Research efficient AI architectures, model optimisation, quantisation, and real-time inference.
Responsibilities
- Research model architectures and techniques suited to real-time inference on GPUs and edge devices.
- Run experiments in quantisation, sparsity, pruning, and distillation.
- Measure accuracy and performance tradeoffs rigorously.
- Work with compiler and systems engineers to make research results run fast.
- Follow the research literature and reproduce promising results.
Requirements
- A strong understanding of how modern neural networks work, especially transformers.
- Experience with PyTorch, JAX, or a similar framework.
- Hands-on experience with model optimisation techniques such as quantisation.
- Solid foundations in linear algebra and numerical methods.
- The ability to design clean experiments and report results honestly.
Infrastructure Engineer
Build and manage Linux servers, computing clusters, deployment systems, and research infrastructure.
Responsibilities
- Build and operate the Linux servers and clusters used for research.
- Automate provisioning, configuration, and deployment.
- Maintain monitoring, logging, and experiment-tracking infrastructure.
- Keep the research environment reliable, reproducible, and secure.
- Plan hardware and network capacity with the research team.
Requirements
- Experience administering Linux servers at scale.
- Familiarity with infrastructure-as-code tools such as Ansible or Terraform.
- Networking knowledge: switching, routing, and diagnosing throughput problems.
- Experience with monitoring stacks such as Prometheus and Grafana.
- A strong preference for automation over manual work.
Make your machines think in real time.
We’re working with a small group of design partners. Tell us what you’re building.