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Documentation Index

Fetch the complete documentation index at: https://docs.kaireonai.com/llms.txt

Use this file to discover all available pages before exploring further.

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Overview

AI Configuration lets administrators tune the behavior of each AI analyzer and configure the LLM provider across the platform. Settings are saved per tenant (organization), so each organization can customize analysis thresholds, caps, algorithms, and AI provider independently. Navigate to Settings > AI Configuration in the KaireonAI sidebar.

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LLM Provider Configuration

The platform supports 6 LLM providers. Configuration is resolved in priority order:
  1. Database — Per-tenant settings stored in Settings > Integrations > AI / LLM Provider
  2. Environment variables — AI_PROVIDER, AI_MODEL, AI_API_KEY, AI_BASE_URL
  3. Defaults — Google Gemini 2.5 Flash
ProviderKeyDefault ModelBase URLAuth
Googlegooglegemini-2.5-flashAutoAPI key
AnthropicanthropicClaude modelsAutoAPI key
OpenAIopenaiGPT-4o modelsAutoAPI key
Amazon BedrockbedrockConfigurableAutoIAM role, access key, or inference profile
OllamaollamaAny local modelhttp://localhost:11434None
LM Studiolm_studioAny local modelhttp://localhost:1234/v1None (uses lm-studio placeholder key)
For Bedrock, additional configuration is available: region, auth mode (access_key or iam_role), role ARN, access key ID, secret access key, and inference profile.

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Analyzer Settings

Analyzer settings are managed via the GET /api/v1/ai/analyzer-settings and PUT /api/v1/ai/analyzer-settings endpoints. The GET endpoint is accessible to viewers, editors, and admins. The PUT endpoint requires the admin role.

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Setup

1

Open AI Configuration

In the sidebar, expand Settings and click AI Configuration.
2

Review the four analyzer sections

The page shows collapsible cards for each analyzer: Segmentation, Policy, Content Intelligence, and Rule Building. Each card lists the tunable parameters with their current values.
3

Adjust parameters

Change any value by editing the input field. Each parameter shows a plain-language description. Click Technical detail to see the underlying implementation detail.
4

Save or reset

Click Save Configuration to persist your changes. Use Reset Section on an individual card or Reset All to Defaults to restore factory defaults.
Settings are loaded once on page load and edited locally until you save. Multiple admins editing at the same time will overwrite each other — the last save wins.

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Segmentation Parameters

ParameterTypeMinMaxDefaultDescriptionTechnical Detail
Min Clustersinteger2202Fewest groups to split customers intoK-Means n_clusters lower bound. Fewer clusters = broader segments.
Max Clustersinteger2508Most groups customers can be split intoK-Means n_clusters upper bound. More clusters = finer-grained segments.
Algorithmenum——kmeansMethod used to find natural groupingskmeans (centroid-based, fast) or dbscan (density-based, handles outliers).
Included Featuresstring[] or null——null (all)Which attributes to consider when groupingFeature whitelist for clustering input matrix. Null = all numeric features.

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Policy Parameters

ParameterTypeMinMaxDefaultDescriptionTechnical Detail
Daily Capinteger11003Max messages a customer receives per dayHard cap on contact frequency per customer per calendar day.
Weekly Capinteger150010Max messages a customer receives per weekHard cap per customer per rolling 7-day window.
Monthly Capinteger12,00030Max messages a customer receives per monthHard cap per customer per rolling 30-day window.
Lookback Daysinteger773090How far back to analyze interaction historyInteraction history window (days) for policy violation detection.
Min Sample Sizeinteger1010,000100Minimum interactions before analysis is meaningfulStatistical minimum sample size for reliable policy analysis.

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Content Intelligence Parameters

ParameterTypeMinMaxDefaultDescriptionTechnical Detail
Min Impressionsinteger1100,000100Times content must be shown before judging performanceMinimum impression count before metrics are statistically valid.
Metric Weights (CTR)number010.33Weight given to click-through rateWeighted blend coefficient for CTR in content scoring.
Metric Weights (CVR)number010.34Weight given to conversion rateWeighted blend coefficient for CVR in content scoring.
Metric Weights (Revenue)number010.33Weight given to revenue per impressionWeighted blend coefficient for revenue-per-impression.
Confidence Levelnumber0.50.990.95How sure we need to be before recommending a changeStatistical confidence threshold (1 - alpha) for hypothesis tests.

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Rule Building Parameters

ParameterTypeMinMaxDefaultDescriptionTechnical Detail
Max Conditionsinteger1205Maximum conditions allowed in a single ruleUpper bound on rule clause count to prevent overly complex rules.
Allowed Operatorsstring[]——equals, gt, lt, gte, lte, contains, inComparison types available when building rulesOperator whitelist for AI-generated rule conditions.
Field Type Constraintsstring[] or null——null (all)Which field types can be used in rulesField type whitelist for rule condition targets. Null = no restrictions.

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Validation

All parameters are validated server-side using Zod schemas. If any value is outside its allowed range, the API returns a 400 Bad Request with details about which parameter failed validation. Each sub-schema (segmentation, policy, content, ruleBuilder) is parsed independently — a bad value in one analyzer does not reset valid overrides in other analyzers.

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Per-Run Overrides

Each AI analyzer (Segmentation, Policy Recommender, Content Intelligence, Rule Building) supports per-run parameter overrides in its Advanced Parameters panel. Per-run overrides:
  • Take effect for that run only and do not change the saved tenant configuration
  • Default to the current tenant-level values from AI Configuration
  • Include a Reset to Defaults button to restore tenant-level values
This lets analysts experiment with different settings without affecting the organization-wide defaults.

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Large Dataset Warning Flow

When a dataset contains 5,000 or more rows (the ML_ROW_THRESHOLD), the platform displays a confirmation dialog before proceeding with analysis. The dialog provides three pieces of information:
  1. Accuracy — The ML Worker uses K-Means clustering, logistic regression, and TF-IDF, which are more accurate than LLM pattern matching for large datasets.
  2. Cost estimate — Proceeding with the LLM shows the estimated token count and cost. The estimate is calculated as rows x fields x 15 + 500 tokens.
  3. Speed — The ML Worker processes data locally in seconds vs. LLM round-trip latency.
The dialog offers two choices:
  • Use ML Worker — Routes the analysis to the ML Worker for full-dataset processing (only available if the ML Worker is connected and healthy).
  • Proceed with LLM — Continues with LLM-based analysis using sampled data. Useful when the ML Worker is unavailable.
For datasets over 5,000 rows, LLM-based analysis samples the data rather than processing it all. This reduces cost but may miss patterns present in the full dataset. Deploy the ML Worker for the most accurate results on large datasets.
If the ML Worker is not connected, the dialog still appears but explains that the ML Worker is not available and suggests enabling it in Settings > Integrations.

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Next Steps

Auto-Segmentation

Configure segmentation analysis.

Policy Recommender

Optimize contact frequency policies.

Content Intelligence

Analyze creative performance.