Inference engineering services
Private coding assistants
Run private coding assistants with open models for selected engineering tasks. We help reduce reliance on paid tools like Claude Code and Codex where an open-model workflow meets your quality, response-time and cost requirements.
Discuss your workloadWhen this helps
For engineering teams exploring private coding assistance for selected tasks, such as code explanation, targeted edits or test support. This may reduce reliance on paid tools. We evaluate the model, repository access and editor or agent workflow for the tasks you want to delegate.
Choose tasks that can be evaluated
Define a small set of coding tasks with clear inputs and review criteria. Examples include explaining a change, editing a bounded area or preparing tests, provided the team can judge whether the output is useful and correct.
Compare the complete workflow for each task: gathering repository context, making an edit, running checks and reviewing the result. Keep tasks that meet your requirements on the private route; retain existing tools for the rest.
Set repository access boundaries
Agree which repositories, files and context the assistant may access, and what data goes to any service involved. Decide how credentials, generated changes and logs are handled in the deployment and who can review them.
Keep access appropriate to the selected tasks. Record which parts of the workflow stay inside the team's environment and which dependencies or integrations remain external, so security and platform owners can assess the design.
Fit the model into the editor or agent workflow
Map how developers request help, provide repository context, inspect proposed edits and run tests. Review the interface and workflow changes needed for the selected model and deployment, including how a developer accepts, edits or rejects suggestions.
The model endpoint is only one part. We also check the editor or agent integration, tool access and handling of proposed changes, so the evaluation covers the workflow developers will actually use.
Evaluate results and roll out selected tasks
Compare accepted diffs, test success, review effort, response time and operating cost on the agreed tasks. Track errors and cases where developers discard or substantially rewrite suggestions, so activity is not mistaken for useful output.
If results support continued use, define access, monitoring, ownership and rollback conditions for the deployment. Agree scope, price and timeline around the workload before implementation begins.
What you receive
- A shortlist of selected coding tasks with evaluation inputs and review criteria.
- A repository access and data-flow outline for the proposed assistant workflow.
- A comparison of accepted changes, test outcomes, latency, errors and operating cost.
- Where agreed, a private model endpoint and workflow integration with monitoring, handover and rollback instructions.
Common questions
Will an open model replace tools such as Claude Code or Codex?
An open-model workflow can take over selected tasks if it passes your quality and workflow checks. We compare accepted changes, test outcomes and cost to decide what to move and where to keep your existing tools.
How do you judge whether coding suggestions are useful?
Use agreed tasks and review accepted diffs, test success, developer review effort, latency, errors and cost. A suggestion that is generated but not accepted or does not pass the relevant checks is not a successful result.
Can repository access be limited?
Define access boundaries for the selected workflow, including which repositories and context are available and how data and logs are handled. Your security and platform owners must review the resulting design.
Start with one workload
Tell us what runs today, where cost or latency causes problems, and which quality requirements must hold. We agree scope, price and timeline before implementation.
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