SVC-01 · Engineering service
AI Systems Engineering
Build and operate LLM and agent systems as production software — with evaluation, observability, reliability and economics designed in.
Capabilities
The parts that turn a demo into a service.
- 01
Agents & orchestration
Bounded loops, explicit state, step budgets, idempotent tool calls and recovery paths that hold under concurrency.
- 02
RAG & model integration
Retrieval pipelines, context budgeting, caching and provider abstraction without hiding failure behaviour.
- 03
Model routing & economics
Route by task, not by habit. Cost per successful workflow measured and attributed to customer and feature.
- 04
Evaluation & release gates
Representative datasets, regression suites, quality/cost/latency thresholds, canaries and rollback for prompts and models.
- 05
Observability
One trace per request across models, tools and retries, carrying tokens, cost, versions, tenant and outcome.
- 06
Resilience & hardening
Timeouts, backoff with jitter, rate-limit handling, provider fallback, queueing and load behaviour verified before launch.
Typical engagements
Where this work usually starts.
Most engineering engagements follow a AI Systems Audit, so the work is scoped from evidence rather than a wish list.
- 01Implementing audit remediationRouting, retry policy, tracing and evaluation gates from a prioritized findings list.
- 02Taking an agent from pilot to productionState, concurrency, failure handling and telemetry added before real traffic arrives.
- 03Model migration with a safety netReplay harness, thresholds, canary and rollback so a provider or model change is a controlled release.
- 04Embedded senior engineeringA defined window in your repo and standups, with written handoff at the end.
Operating an LLM or agent system that needs to become boring?
Describe the workflow and where it hurts. If an audit is the better first step, we will say so.