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.

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.

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

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”

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”

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”

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.

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

API access

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

{
  "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