AI infrastructure & operations
We build the runtime around your AI: durable jobs, bounded retries, controlled writes, deployment pipelines, and telemetry that lets your team see failures, recover work, and manage operating cost.
A task API accepts an approved request, then the response is lost. Restarting the entire job can create a duplicate unless the runtime preserves request identity and checks the outcome.
Inside the system
Inspect an illustrative document-to-task job, from admission to operating telemetry. Infrastructure and integration choices depend on the workload and destination APIs.
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Follow the architecture as you scroll, or select a layer.
Authenticate the caller, validate the payload, and assign a stable job identity. Persist accepted work before execution; apply tenant quotas, concurrency limits, and secret access boundaries.
Run model and API calls as separate activities with persisted completion state. Classify retryable errors, cap attempts and elapsed time, and pause exhausted jobs for an operator.
Bind approval to the exact payload. Use destination-supported idempotency and a write ledger; reconcile ambiguous responses by request identity. If the destination cannot confirm the outcome, hold for review.
Correlate job, model, and tool spans with queue age, latency, and usage. Route alerts to named owners; use versioned rollouts, tested rollback procedures, and recovery runbooks.
Inspect the behavior
Synthetic job JOB-042 turns a document into an approved task. This example assumes a destination API with enforced request identity and reliable lookup; no live calls are made.
Illustrative system · Synthetic inputsThe model returns a valid task draft. APR-019 approves payload-v1 before the task API is called.
The destination returns TASK-208 for the approved request. The ledger records confirmation, and the job completes with its request identity available for duplicate-event handling.
Verification specification
We turn expected behavior into repeatable tests and operating controls. Acceptance thresholds are agreed for your workflow, data, and the consequences of an error.
Inject worker restarts, duplicate events, and lost responses; inspect the job ledger and destination records after reconciliation.
No unapproved writes; unknown outcomes stay unresolved until verified.
Measure queue age, p95 completion time, retries, input/output tokens, and attributed cost per completed job under representative load.
Set workload-specific budgets and backpressure before rollout.
Rehearse worker deployment, rollback, alert routing, secret rotation, and recovery with the team responsible for operating the system.
Rollback does not undo external actions; reconciliation has an owner.
The handover
A defined implementation, reviewable evidence, and an operating model your team can own.
How we workInfrastructure configuration, scoped access, durable workers, queue policies, and versioned deployment pipelines in your environment.
Retry policies, approval-bound payloads, write ledger, reconciliation adapters, and failure-injection scenarios.
Traces, dashboards, alerts, cost visibility, and runbooks. Ongoing operations have agreed scope, hours, and escalation ownership.
Architecture decisions
We assess your infrastructure, identity controls, network boundaries, and deployment process before choosing the architecture. Hosting, data residency, vendor dependencies, and operational access are defined in the project scope.
Infrastructure delivery and ongoing support are scoped explicitly. For continuing operations, we agree the systems covered, support hours, response expectations, maintenance work, and escalation contacts. Coverage follows that written agreement.
Talk to an engineer
Bring a workflow, a product brief, or an AI system that needs to ship.
