> ## Documentation Index
> Fetch the complete documentation index at: https://docs.klayrai.com/llms.txt
> Use this file to discover all available pages before exploring further.

# Andromeda

> KlayrAI's user intelligence layer that adapts diagnostics to your decision-making style

# Andromeda

Andromeda is KlayrAI's user intelligence layer. It builds a behavioral profile of each user over time and uses that profile to adapt every diagnostic, recommendation, and report to match your decision-making style.

## Why personalization matters

Two media buyers looking at the same campaign data may need completely different advice:

* A **conservative buyer** managing a client's budget wants cautious, low-risk suggestions with proven track records
* An **aggressive growth marketer** spending their own money wants bold scaling recommendations that maximize upside

Andromeda ensures that KlayrAI speaks your language and recommends actions you'll actually take.

## The UserBehaviorProfile

Every KlayrAI user has a `UserBehaviorProfile` that captures their preferences and patterns:

| Field                     | Type                                           | Description                                                                                                    |
| ------------------------- | ---------------------------------------------- | -------------------------------------------------------------------------------------------------------------- |
| `riskAppetite`            | `LOW`, `MEDIUM`, `HIGH`                        | How much risk the user is comfortable with in budget changes and targeting experiments                         |
| `focusMetric`             | `ROAS`, `CPA`, `CPM`, `CTR`, `VOLUME`, `REACH` | The primary KPI the user optimizes for                                                                         |
| `preferredActions`        | `string[]`                                     | Actions the user has historically approved (e.g., "increase\_budget", "refresh\_creative", "narrow\_audience") |
| `avgManagedBudget`        | `number`                                       | Average daily budget across all managed campaigns                                                              |
| `recommendationApplyRate` | `number`                                       | Percentage of KlayrAI recommendations the user has approved and applied                                        |

## How the profile is built

Andromeda computes the profile from the `UserEvent` table, which tracks every meaningful interaction:

### Events tracked

| Event                    | Signal                                                   |
| ------------------------ | -------------------------------------------------------- |
| Recommendation approved  | Indicates comfort with that action type and risk level   |
| Recommendation dismissed | Indicates discomfort or disagreement with the suggestion |
| Manual campaign changes  | Reveals preferred optimization patterns                  |
| Diagnostic frequency     | Shows how actively the user monitors campaigns           |
| Dashboard focus          | Which metrics and views the user spends time on          |
| Budget change patterns   | Historical budget adjustments reveal risk tolerance      |

### Computation schedule

Profiles are recomputed **weekly** via a scheduled job. The computation looks at the trailing 30 days of user events with a decay factor — recent behavior is weighted more heavily than older behavior.

<Note>
  New users start with a default profile (MEDIUM risk appetite, ROAS focus metric) until enough behavioral data is collected — typically after 5-10 diagnostic interactions.
</Note>

## How Andromeda adapts diagnostics

The user's Andromeda profile is injected into every Claude diagnostic call as context. This changes the analysis in several ways:

### Risk appetite adaptation

<AccordionGroup>
  <Accordion title="LOW risk appetite">
    * Recommendations are conservative and incremental
    * Budget changes are capped at 10-15% adjustments
    * "Wait and monitor" is suggested when data is ambiguous
    * Emphasis on protecting current performance
    * Language: "Consider", "You might want to", "A safe approach would be"
  </Accordion>

  <Accordion title="MEDIUM risk appetite">
    * Balanced recommendations with moderate ambition
    * Budget changes of 15-30% suggested when evidence supports it
    * Both upside potential and downside risks are presented
    * Language: "We recommend", "The data supports", "This should improve"
  </Accordion>

  <Accordion title="HIGH risk appetite">
    * Aggressive scaling and experimentation encouraged
    * Budget changes of 30-50%+ when opportunity exists
    * Focus on maximum upside with acceptable risk
    * Faster iteration cycles recommended
    * Language: "You should", "The opportunity is clear", "Act now to capture"
  </Accordion>
</AccordionGroup>

### Focus metric adaptation

The user's primary KPI determines how issues are prioritized and which recommendations come first:

| Focus metric | Prioritization behavior                                                                  |
| ------------ | ---------------------------------------------------------------------------------------- |
| **ROAS**     | Efficiency issues ranked highest. CPA creep and budget waste flagged aggressively.       |
| **CPA**      | Cost-per-acquisition is the north star. Learning phase and creative fatigue prioritized. |
| **CPM**      | Auction costs monitored closely. Overlap and audience saturation ranked highest.         |
| **CTR**      | Creative performance emphasized. Fatigue and ad relevance issues prioritized.            |
| **VOLUME**   | Conversion volume is key. Scaling opportunities and budget headroom highlighted.         |
| **REACH**    | Audience expansion opportunities prioritized. Saturation flagged early.                  |

### Recommendation filtering

Andromeda tracks which types of recommendations users historically approve. If a user consistently dismisses "expand audience" suggestions but approves "refresh creative" suggestions, future diagnostics will:

1. Rank creative-related recommendations higher
2. Provide more detail and confidence data for audience recommendations (to build trust)
3. Frame audience suggestions in terms the user responds to

## Example: Same campaign, different profiles

Consider a campaign with declining ROAS (2.1x, down from 3.0x) and rising CPA.

### Profile A: Conservative, ROAS-focused

> **Risk level: HIGH**
>
> Your ROAS has declined 30% over the past 7 days. The primary driver is creative fatigue in your "Broad Lookalike" ad set.
>
> **Recommendation:** Pause the underperforming creatives and introduce 3 new variations based on your top-performing angles. This is a low-risk change that should stabilize ROAS within 5-7 days.
>
> Consider reducing the daily budget by 15% until performance recovers.

### Profile B: Aggressive, VOLUME-focused

> **Risk level: MEDIUM**
>
> Your campaign is showing signs of creative fatigue, but overall conversion volume remains strong at 142 this week. ROAS has dipped to 2.1x.
>
> **Recommendation:** Keep current spend levels to maintain volume. Immediately launch 5 new creatives to rotate against fatigued ones. If new creatives perform, increase budget by 30% to capitalize on the refreshed creative set.
>
> The volume opportunity outweighs the temporary ROAS dip.

<Note>
  Same data, same issues — completely different tone, recommendations, and priorities. This is the value of Andromeda.
</Note>

## API access

When using the API, the `andromedaContext` field in diagnostic responses shows how the profile influenced the analysis:

```json theme={null}
{
  "andromedaContext": {
    "riskAppetite": "MEDIUM",
    "focusMetric": "ROAS",
    "adaptations": "Recommendations prioritize ROAS recovery over volume. Conservative budget suggestions aligned with medium risk appetite."
  }
}
```

## Privacy

* Andromeda profiles are stored per-user and scoped to their workspace
* Profile data is never shared between users or workspaces
* Users can reset their profile at any time from **Settings > Andromeda**
* Profile data is not used to train AI models