AI compute and foundation models · Raleigh, North Carolina
Compute for living and moving intelligence.
Sinewgrid is an AI compute company. Our architecture is built on GPU capacity from the major clouds and on the compute and tokens of the leading AI platforms: ChatGPT, Claude, Gemini and Grok. We run them as one system to deliver enterprise AI services and to train foundation models for biology and robotics.
Capacity, customers and certifications are published once each is verified and cleared.
Four jobs, done as one service.
Cloud GPUs and frontier tokens
GPU capacity from AWS, Google Cloud, Microsoft Azure, Oracle Cloud and GPU clouds, plus large volumes of tokens from ChatGPT, Claude, Gemini and Grok. We do not build data centers.
See the architecture → 2 · RunOne training system
A control plane schedules jobs across clouds, moves checkpoints and replaces failed nodes, so a long run survives a failure.
See the platform → 3 · TrainModels for the physical world
We train foundation models for cell therapy and mRNA and for robot learning, and feed lab and robot results back into training.
See all domains → 4 · DeliverEnterprise AI services
We offer enterprise-grade AI services built on large-scale compute, frontier models and tokens, with budgets, logging and data controls set per customer.
See Enterprise →Compute is the scarce input. We make it dependable.
Every serious AI program now runs into the same wall, and it is not ideas. It is dependable compute. Capacity is spread across several clouds and priced in several ways, and it can fail in the middle of a long run. Model tokens add a second bill that grows with every agent and every assistant a company deploys. Most teams meet this problem alone, one provider at a time, and lose weeks to it. Sinewgrid exists to take that problem off their hands. We source GPU capacity from AWS, Google Cloud, Microsoft Azure, Oracle Cloud and GPU clouds, we buy tokens at scale from ChatGPT, Claude, Gemini and Grok, and we run all of it through one control plane that schedules the work, moves checkpoints when a pool fails and reports what each result cost. Because we do not build data centers, we are free to choose the best capacity for each job, and to change that choice as the market changes.
On that foundation we do two kinds of work. We train foundation models where compute has to meet a real-world check: Sinewgrid Cell for cell therapy and mRNA, Sinewgrid Robotics for robot learning, with four more domains under study. And we deliver enterprise AI services to companies that want AI in production without building the infrastructure behind it. In both, we hold the same standard: claims come with the document that supports them, and real-world results return to training. That is what a partner gets from Sinewgrid: capacity they can plan around, models judged against reality, and a team that says plainly what it does and does not know.
The leading AI platforms sit under our architecture.
Agents, code generation, evaluation, data labeling and research assistants all run on frontier models, and that work consumes tokens in large volumes. Sinewgrid is designed around a steady, high demand for the compute and tokens of the four platforms below, routed by task through one gateway and sitting on top of GPU capacity from the major clouds.
ChatGPT
Models from OpenAI for agent workflows, code generation and evaluation.
Claude
Models from Anthropic for long-context analysis, code and agent workflows.
Gemini
Models from Google for multimodal analysis and large-context tasks.
Grok
Models from xAI for reasoning and real-time information tasks.
- TokensModel calls from agents, code generation and evaluation, metered per project.
- GatewayOne layer routes each call by task, applies budgets and caching, and falls back between platforms.
- GPU capacityCloud GPUs train and serve our own models and run simulation.
- Control planeOne scheduler reports cost and useful training time across all of it.
ChatGPT is a trademark of OpenAI, Claude of Anthropic, Gemini of Google and Grok of xAI. Names are used to identify products. No affiliation, partnership or endorsement is implied.
Where we apply the compute.
Two model families are in development. Four more domains are under study. All of them run on the same control plane.
Programming cells with sequence models
Models for how a sequence changes what a cell does, trained against lab results and checked in the lab again.
Teaching machines to move
Models for robot control trained on a data pyramid of simulation, teleoperation and real-robot runs.
Also exploring
Sinewgrid Protein
Candidate generation and scoring, checked in the lab.
ExploringSinewgrid Materials
Learned atomistic models that replace slow simulation.
ExploringSinewgrid Finance
Finance-specific language models and quant research code generation.
ExploringSinewgrid Autonomous
Video-scale models for robots and autonomous systems.
Capacity from the clouds. Architecture from us.
We do not build data centers or own GPUs. We source capacity from hyperscale and GPU clouds, including AWS, Google Cloud, Microsoft Azure and Oracle Cloud, and add the scheduler, data layer and reference architecture that make it behave like one training system.
Capacity sourcing
Reserved, short-term, on-demand and spot, matched to each jobMulti-cloud control plane
One scheduler, checkpoints that move, automatic failoverData, keys and security
Isolated tenants, private networks, customer-held keysCloud names show design targets. No provider has endorsed or partnered with Sinewgrid unless an agreement is listed on this site.
Built for four kinds of team.
We buy and run compute and tokens for our own models, and we also offer enterprise AI services and capacity planning to other teams. We have agreements in place with enterprise customers in these areas and are onboarding and testing with them now. We do not name customers without their written permission.
Biology teams with data and a lab
You have assay data and a wet lab. We bring sequence and formulation models, and the compute that retrains them as each result comes back.
See Sinewgrid Cell →Robot builders and research labs
You have hardware and demonstrations. We bring simulation at scale, training pipelines and held-out real-robot evaluation.
See Sinewgrid Robotics →Companies putting AI into production
You have a business process and data. We build and run AI services on large-scale compute, frontier models and tokens, with cost and data handling agreed up front.
See Enterprise →Teams that need compute and token planning
You have a training plan and a schedule. We plan the GPU and token capacity, the cloud architecture and the failover around it, drawing on what we buy and run ourselves.
See the architecture →Every number on this site has to be earned.
Three rules govern every capacity, reliability and customer figure we publish.
We state committed cloud capacity only after the agreement is executed, and we say which mode it is: reserved, short-term or on-demand.
Useful training time per run, with fixed definitions, reported across every capacity pool we use.
A customer or partner appears here only once the relationship is signed and cleared for publication.
Open standards we build on
Three short reads.
Why we do not build data centers
The reasoning behind sourcing capacity from the major clouds and adding a control plane.
Read more → TrustWhat we will and will not claim
Six commitments, and the document we hand over for each one.
Read more → CompanyWhere the name comes from
Sinew, grid, and why one word covers cells and robots.
Read more →Planning a training run?
Tell us the model, the data and the schedule. We reply with a capacity plan and the evidence behind it.