# Keep AI systems reliable, affordable and useful after launch.

> Improve is veridive’s reliability and continuous improvement service for AI systems in production. Through a monthly Embedded AI Partnership, the team monitors quality, cost and latency, re-runs evaluation sets when models or data change, maintains incident runbooks and reviews each system with its owners every quarter to choose the next improvement.

Launch is a starting point. veridive helps you watch how the system performs, respond when models or data change, and choose the next improvement with evidence.

## What does AI reliability involve?

AI reliability and continuous improvement is the work that keeps an AI system useful after launch. veridive monitors quality, cost and latency, re-runs the evaluation set whenever a model, prompt or data source changes, handles incidents with written runbooks, and reviews the system with its owners every quarter to choose the next improvement on evidence.

## When should you talk to us?

- Quality dropped after a model update and nobody noticed for weeks.
- The AI bill doubled and nobody can say which feature caused it.
- The people who built the system have moved on, and the documentation is thin.
- You can’t say how the system performs today compared with the day it launched.
- Nobody is sure who fixes it when quality drops.

## What do you get?

- **Monitoring.** Quality, cost, latency and usage tracked against the thresholds agreed at acceptance.
- **Cost controls.** Budgets, alerts and routing rules that keep spend under a ceiling you set.
- **Regression checks.** The evaluation set re-run whenever a model, prompt or data source changes.
- **Incident runbooks.** Written steps for what to do when quality drops or a source fails, and who does it.
- **Quarterly improvement reviews.** A review with the owners: what changed, what it cost and what to improve next.
- **A support agreement scoped to your needs.** Support hours, responsibilities and service levels agreed in writing.

## How does ongoing improvement run?

1. **Take stock** (First month). Review the live system, its evaluation set and its costs, then agree thresholds and alerts.
2. **Watch and respond** (Ongoing). Monitor continuously, run regression checks on every change and follow the runbook when something breaks.
3. **Review and improve** (Every quarter). Review results with the owners and choose the next improvement with evidence.

## How can you start?

- **Embedded AI Partnership** · Monthly. A named team working alongside your internal owners, with monthly capacity, support scope and service levels agreed together.
- **Pilot to Production** · ≈6–10 weeks. Reliability starts before launch: a pilot is accepted only when monitoring, regression checks and a runbook are in place.

## What changes?

- **Problems found early.** Changes in quality or cost are caught by checks, not by customers.
- **Predictable spend.** Costs stay under the ceiling, and every increase has an explanation.
- **Upgrades without guesswork.** New models are adopted when the evaluation set shows they are better, not before.
- **A system your team can run.** Runbooks and documentation mean nothing depends on us remembering.

## Questions about running AI in production

### Who fixes an AI system when quality drops?

The owner named in the runbook, following a written first step. Before launch we agree who watches the system, which alerts fire when quality or cost moves outside its thresholds, and what happens next. In an Embedded AI Partnership, veridive takes on part of that responsibility under a written support scope, alongside your internal owners.

### Why do AI costs rise after launch, and how do you control them?

Costs usually rise because usage grows, prompts get longer or tasks run on a larger model than they need. We track cost per task, set budgets and alerts, and route routine cases to smaller models while harder ones go to a larger model or a person. Every change is checked against the evaluation set first.

### How often should an AI system be re-evaluated?

Every time something it depends on changes, and at least every quarter. A new model version, a prompt edit or a new data source can change quality quietly, so the evaluation set runs as a regression check on each change. The quarterly review then looks at trends, costs and the next improvement.

## Related

- Where it works: [Customer operations](https://veridive.com/solutions/customer-operations/) · [Knowledge & document intelligence](https://veridive.com/solutions/knowledge-document-intelligence/) · [Supply chain intelligence](https://veridive.com/solutions/supply-chain-intelligence/)
- Field notes: [The cost of a token vs. the cost of a mistake](https://veridive.com/insights/cost-of-a-token-vs-cost-of-a-mistake/) · [Evaluation sets are the new requirements document](https://veridive.com/insights/evaluation-sets-are-the-new-requirements/) · [What makes an AI pilot ready for production?](https://veridive.com/insights/ai-pilot-to-production-checklist/)
- See also: [Before anything goes live](https://veridive.com/approach/#checklist) · [Connect: Data & AI foundations](https://veridive.com/services/data-ai-foundations/) · [Ways in](https://veridive.com/services/#ways-in) · [Start a project](https://veridive.com/contact/)

## Is a live system drifting?

Tell us what you are seeing. We will reply within one business day with a recommended first step. [Start a project](https://veridive.com/contact/) or write to [hello@veridive.com](mailto:hello@veridive.com).