AI in Production
The part nobody sells you until it breaks.
- Timeline
- Ongoing
- Engagement
- Monthly retainer with SLAs
- Built for
- Anyone running an AI feature that customers or revenue depend on
The problem
Providers deprecate model versions. Prompts drift. Cost moves ten-fold on a usage-pattern change. A silent quality regression looks exactly like nothing happening.
Who this is for
- Anyone running an AI feature that customers or revenue depend on
- Teams who shipped an AI product and have no way to tell if it is still working
Who this is not for
- Products not yet in production
- Internal experiments with no users
How it runs
- 01
Instrument
Evals, quality signals, per-task cost and usage land before anything else changes.
- 02
Guardrail
Abuse handling, rate limits, budget alerts and failure modes that degrade rather than collapse.
- 03
Operate
Drift and quality monitoring, incident response, and a human who is accountable when it fires.
- 04
Upgrade
Model version changes tested against your eval suite before adoption, never after.
What you get
- Eval suite running on every change
- Guardrails and abuse handling
- Per-task cost tracking and budget alerts
- Quality and drift monitoring
- Model-version upgrade path tested against your evals
- Incident response with defined SLAs
AI in Production: questions we get asked
What breaks in AI systems after launch?
Quality degrades silently. Providers deprecate the model version you built against, your input distribution drifts away from what you tested, and costs move with usage patterns you did not anticipate — none of which throws an error.
How do you monitor whether an AI feature is still working?
With an eval suite that runs continuously against production behaviour, not just at build time. A quality regression becomes an alert with evidence attached instead of a slow decline someone notices in a support queue three months later.
How do you keep AI costs from running away?
Per-task cost tracking with budget alerts, plus caching and model-routing decisions made against measured cost rather than list price. Cost is a design constraint, and unmonitored it can move ten-fold on a usage-pattern change.
Delivered in your region.
- GDPR
- UAE PDPL
- ISO 27001 practices
- Data residency in the EU, the UAE, or your own cloud account
APPINE L.L.C-FZ, Dubai
Related services
$ appine assess --fixed-fee
Two weeks. Fixed fee. You end with a decision, not a deck.
A system and codebase audit, a risk register ranked by severity, an engineering baseline, and an explicit recommendation — keep, harden, rebuild, or don't do it at all.
If the answer is “don't hire us,” we'll write that down too.