Enterprise AI services, built on large-scale compute and frontier models.
Sinewgrid designs, builds and runs AI services for enterprise teams. The services sit on GPU capacity from the major clouds and on the models and tokens of ChatGPT, Claude, Gemini and Grok, routed through one gateway with budgets, logging and data controls agreed per customer. We have agreements in place with enterprise customers across our domains, and we are onboarding them, testing with them and moving quickly toward production. We name no customer here without its written permission.
Production AI without the infrastructure project.
Most enterprise AI efforts stall in the gap between a promising demo and a service people can rely on. The demo needs one model and a good afternoon. The service needs capacity that does not run out, budgets that do not surprise finance, logs that security can read, and a way to swap models when a better or cheaper one appears. Sinewgrid closes that gap. We bring the compute and token supply, the gateway that routes each request to the right model, and the people who build and run the service, so your team spends its time on the business problem and not on provider contracts and cluster operations. Because we buy at scale and measure every call, you see cost per completed task, not a pile of token invoices. Because the gateway sits between your applications and the models, you are not tied to one provider, and a change of model is a configuration decision, not a rebuild. And because every engagement is staged, with an exit at the end of each stage and a document you keep, you can start small and prove the value before you commit further. We are onboarding enterprise customers now, and we would rather show a pilot’s measured results than promise them.
What we take on.
Use case and architecture
We pick the workflow, define how it will be judged, and choose the model, capacity and cloud design that fit the task and the budget.
Agents and assistants
Assistants and agent workflows connected to your documents, systems and tools, with evaluation sets written before the build starts.
Operate in production
Routing across models, caching, fallbacks, monitoring and incident response, with a monthly report on cost and quality.
Compute and token supply
We plan and buy the GPU capacity and model tokens the service needs, so a customer does not negotiate with each provider alone.
Fine-tuning and private models
Where a task justifies it, we adapt a model to your data inside your tenant and compare it with a general model on the same test.
Controls and review
Access rules, audit logs, human review steps and a written record of which model handled which data.
One path from a request to a result.
- Your applicationStaff or systems send a request to the service you approved.
- GatewayApplies access rules, budgets and caching, and routes the call by task across ChatGPT, Claude, Gemini, Grok or a private model.
- ComputeHosted models and tools run on GPU capacity in the cloud region you choose.
- ReportEvery call is logged with cost, latency and the model used.
Four stages, each with an exit.
An engagement stops at the end of any stage if the evidence does not support going on. Each stage ends with a document you keep.
- 1 · ScopingOne workflow, one success test, a data review and a cost range. You keep the scoping note.
- 2 · PilotA working service for a small group, measured against the test. You keep the evaluation report.
- 3 · ProductionMonitoring, budgets, access controls and a runbook. You keep the runbook.
- 4 · OperateMonthly reporting on cost, quality and incidents, with a review of model choices.
In your cloud account
We can run the service inside your own cloud account, so the data stays in an environment you already govern.
In ours, per agreement
Where it fits, we host the service for you under a written agreement that names the region, the models and the data handling.
Commercial terms
Terms are set per engagement in writing. This site publishes no price list.
How we handle your data.
These are design rules for every engagement. Each one is confirmed in the written agreement before data moves.
Your data stays yours
Customer data is used to serve that customer. We do not use it to train models for anyone else.
Tenant separation
Each customer has its own environment, storage and keys, held in a region the customer chooses.
Provider terms reviewed
Before a model is used on your data, we read that provider’s data-handling terms, retention settings and regional options, and record the result.
Least access
Our staff reach customer data only when a task needs it, with named access and an audit trail.
Logs without content by default
The gateway records cost, latency and model choice. Prompt and answer content is logged only when you ask for it.
Human review where it counts
High-impact outputs go to a person before they reach a customer, a filing or a system of record.
This site does not claim any security certification or audit report. If that changes, we will say which one, who issued it and for what scope. See Trust for what we will and will not claim.
Where we would start.
These are the kinds of workflow we design for. They show the scope of the service and are not a list of customers.
Internal knowledge assistant
Staff ask questions across policies, manuals and past work and get answers with links to the source documents.
Customer service operations
Drafting replies, routing cases and summarizing long threads, with a person approving anything sent out.
Code and engineering assistance
Code review, test generation and migration help, evaluated on your own repositories.
Document and data processing
Extracting fields from contracts, filings and forms at volume, with checks against the source.
Research and analysis assistants
Reading large document sets and producing cited summaries that an analyst then verifies.
Financial workflows
Filing analysis and research code generation under model-risk review. See Sinewgrid Finance.
Agent workflows
Multi-step tasks across your tools, with budgets, step limits and a log of every action.
Compute and token planning
For teams that already run AI services and need capacity, cost and failover planned. See the architecture.
Measures we commit to publishing.
Each pilot is reported against these measures, which are defined before the first run.
Task quality
Accuracy on a test set written with you, compared with how the task is done today.
Cost per completed task
All tokens and compute, including checking steps and retries, for each finished task.
Latency and availability
Response time at peak load, and how the service behaved when a model or region failed.
Plain answers.
Do you work with OpenAI, Anthropic, Google and xAI?
We buy and use their models and tokens. No provider has endorsed Sinewgrid or partnered with it. ChatGPT is a trademark of OpenAI, Claude of Anthropic, Gemini of Google and Grok of xAI.
Can we choose which models are used?
Yes. The gateway can restrict a service to the models you approve, including a single provider or a private model.
Do you have enterprise customers today?
Yes. We have agreements in place with enterprise customers across our domains and are actively onboarding them, testing with them and moving toward production. We do not list customers on this site, and we will name a customer only with its written permission.
What do we need to start?
One workflow, a sponsor who owns the result, and a data contact who can approve access.
Planning a training run?
Tell us the model, the data and the schedule. We reply with a capacity plan and the evidence behind it.