Yes. You can start with the servers, cloud account or data centre your business already controls. The first decision is which part of a workflow needs to improve and how you will verify the change.
Before choosing a deployment, trace one example from its trigger through each API call to the stored result. Include the person who owns a failure and the steps needed to recover it. That exercise can reveal what the project needs: a connection fixed, clearer error handling, a different model route or a deployment change.
Follow one request before choosing a platform
For an SME operations lead or an enterprise team assessing a delivery partner, a narrow starting point makes the work easier to inspect. Choose one recurring task with a clear destination, such as classifying a service request in an existing help desk.
Keep the example specific. Which event starts the run? Which fields leave your environment? What record proves completion? If the process stops halfway, can someone tell whether to retry it? These questions turn a broad request to improve AI into a change you can scope and accept.
Illustrative example: a request stalls after an API call
Imagine a company whose workflow runs in its own cloud account. A new help desk request triggers a model API call to suggest a category. The workflow saves the suggestion beside the request ID in the company's database.
Now suppose the API returns a category, but the database write fails. The model has done its work; the help desk still shows an unprocessed request. Retrying the whole run may call the model again.
One possible design uses the request ID as a unique key and records a pending status before the API call. It validates the returned category, then saves it with a completed status in one update. Once the database is available again, pending records show which requests to inspect, and retries target the same record. Another model call may also incur another usage charge.
Someone still needs to review the unresolved request, decide whether to retry or handle it manually, and confirm that the final record is correct.
The acceptance check becomes concrete: interrupt a sample run after the API response but before the database write, then verify that recovery produces one correct record. That repair can be scoped against the current deployment before considering a move.
Separate the model's location from the backend
A workflow can run on your server and still send text to an external model API. The backend's location tells you where the orchestration runs; it does not tell you where every field is processed.
For the example above, list exactly which request fields go to the model, what comes back, and where logs and outputs are stored. Check the external provider's data handling against your requirements before connecting it. Removing unnecessary fields may reduce what needs to leave your environment.
If certain data must remain on premises, assess local inference separately. That includes model suitability, available compute, expected workload and ongoing operations. A capacity and requirements check comes before any promise that your current hardware can support it.
A first check your team can use
- Pick one sample item and name its trigger.
- Trace every API call, the fields sent and the destination.
- Find the stored result and how it links to the original item.
- Identify the owner of a failed run and the recovery steps.
- Agree one acceptance check and the access and backup arrangements needed to perform it.
Use this map to choose the smallest useful scope. It also gives your team something concrete to review when comparing delivery proposals.
Match support to the work
Sell.Systems prioritises work on your servers, cloud accounts or data centres. Our managed cloud is optional.
Starter support is from US$600/month for one system and up to 15 approved delivery hours per month. Hourly work is from US$45/hour for an agreed task. The choice depends on whether you need ongoing work on one system or a bounded change.
Setup or migration, hosting, API and model usage, licences and extra hours are quoted and agreed separately. Before work starts, we agree scope, support hours, response arrangements, access, backups, acceptance and exclusions. Include who owns recovery outside the agreed support hours.
To discuss one workflow and its boundaries, book a free 15-minute introductory Google Meet. Bring the sample run and the point where ownership or recovery becomes unclear.