# Scale AI that performs in production.

> Openlayer gives every team a consistent way to evaluate quality, compare versions, monitor live behavior, and improve performance across models, agents, RAG, and traditional ML.

## By the numbers

| Stat | Meaning |
| --- | --- |
| 7% | of organizations have fully scaled AI across the enterprise (Source: McKinsey’s 2025 State of AI Global Survey) |
| 79% | of enterprises exceeded their AI budgets in the past year (Source: DoiT, AI Spending Survey 2026) |
| 79% | of production AI agents still rely primarily on human evaluation (Source: Measuring Agents in Production, 2025) |


## One quality standard across every AI team.

### Standardize quality across teams

Give every team consistent tests, thresholds, and release criteria across LLM, RAG, agent, and traditional ML projects.

Start with 175+ ready-to-use evaluations or define standards for your own use cases.

### Compare every version

Measure changes to models, prompts, data, retrieval, and tools against the same baseline.

See the tradeoffs across quality, safety, latency, and cost before deciding what to release or scale.

### Track performance and cost

Monitor quality, drift, latency, token usage, and spend across every connected project.

See which systems are improving, which are slipping, and which are consuming more budget without producing better results.

### Create evidence as you work

Keep evaluations, approvals, production monitoring, and system changes in one traceable record.

Give governance teams continuously updated evidence without adding another process for delivery teams.


## Results you can build on.

### 100%

of releases evaluated against a defined quality standard

### 100%

of production requests evaluated continuously

### 100%

of AI spend attributed to a system, project, or team

### 175+

ready-to-use evaluations for AI quality, safety, and performance


## Built for the standards your organization follows.

- EU AI Act
- ISO/IEC 42001
- NIST AI RMF
- OSFI E-23


## Proof

> Openlayer gives us one view of quality, performance, and cost across every version, so we can make better decisions about what to scale.
> 
> Daniel Chyan, CTO, Jericho Security


## Scale what works.

Know which AI systems are delivering value, then expand them with quality, cost, and risk continuously measured.