Observability specialists for your dedicated team.

Where Observability fits in a long-term product team.

The distinction from monitoring is about unanticipated questions. Monitoring answers questions you thought of in advance: is CPU high, is the error rate up. Observability means being able to ask a new question, such as why this customer's requests are slow on Tuesdays, without first deploying new instrumentation.

That capability comes from high-cardinality structured data. Logs and traces carrying the identifiers that matter, such as user, tenant, request and version, allow any slicing after the fact. Aggregate metrics cannot answer those questions no matter how many dashboards are built from them.

What your dedicated team does with Observability.

AI systems raise the stakes because they are non-deterministic. Without the prompt, the retrieved context and the response recorded for each call, a reported failure can't be investigated, because there is nothing to reproduce.

Designing that in from the start is part of what your dedicated AI team delivers.

Common Observability use cases.

• New questions answered without redeploying High-cardinality data allowing arbitrary slicing after an incident starts.
• Non-deterministic systems made investigable Prompt, context and response recorded, because there is no reproduction otherwise.
• Correlation across services Identifiers carried through, so one request is followable end to end.

Adding Observability skills to your team.

Observability is assigned across platform and application capacity, designed in at the start because retrofitting it after an incident is when it is least available.

You can add Observability capacity as one dedicated specialist or as part of a wider remote team, managed by a service delivery manager from our offices in Thailand. Companies hire dedicated developers this way under one agreement and one monthly fee.