AI engineering

Production AI is a software engineering problem.

Models are only one component of an AI system.

Production systems also require data pipelines, APIs, orchestration, security, observability, evaluation, infrastructure and integration.

Argbit engineers the complete system.

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Engineering principles

Five commitments in every system we build.

Observable by design

AI behaviour should be measurable.

Evaluation before scale

Quality needs objective evaluation rather than anecdotes.

Human control where it matters

Automation boundaries should reflect operational risk.

Model independence

Architectures should avoid unnecessary dependency on a single model provider.

Secure by default

AI should inherit enterprise-grade identity, permissions and auditability.

Evaluation in the pipeline

If it isn't in CI, it isn't a quality bar.

Evaluation suites run with the rest of the test suite. A regression in task completion, escalation quality, latency or cost per run blocks the release the same way a failing unit test does.

ci/evaluate.sh

argbit eval run suites/servicing.v3 \
  --dataset fixtures/cases-2026-07 \
  --assert completion>=0.94 \
  --assert escalation-precision>=0.90 \
  --assert p95-latency<=4200ms \
  --assert usd-per-task<=0.42

Moving an AI initiative from prototype to production?

Tell us where it currently stalls — reliability, integration, evaluation, security or cost — and we will describe what closing that gap involves.

Start a conversationNo AI theatre. Just engineering.