Spam Detection

Burki’s AI-powered Spam Detection System evaluates calls in real-time to identify and automatically terminate spam, scam, and unwanted calls—protecting your business and saving agent time.

Overview

The Spam Detection System uses LLM-based evaluation to analyze conversations and detect unwanted calls:

Real-Time Analysis

Evaluates transcripts during live calls without blocking conversation flow.

Configurable Scenarios

Define what constitutes spam for your specific business context.

Fail-Open Design

Errors default to allowing calls—never blocks legitimate callers.

How It Works

Call Begins

Spam detection is registered when the call starts. A grace period prevents premature evaluations.

Transcript Evaluation

As conversation progresses, the LLM periodically evaluates the transcript against your defined scenarios.

Verdict Aggregation

Multiple evaluations are aggregated. A final verdict requires consistent results with sufficient confidence.

Action

If spam is confirmed with high confidence after the minimum call duration, the call is automatically terminated.

Configuration

Enable and configure spam detection per assistant:

{
  "spam_detection_config": {
    "enabled": true,
    "denied_scenarios": [
      "caller is trying to sell insurance or financial products",
      "caller is conducting a survey or market research",
      "caller refuses to identify themselves after being asked",
      "caller is making threats or using abusive language",
      "caller appears to be a robocall or automated system"
    ],
    "allowed_scenarios": [
      "caller is a returning customer with a valid order number",
      "caller mentions a specific employee by name",
      "caller is responding to a callback request",
      "caller provides a valid account number or reference"
    ],
    "max_evaluations_per_call": 5,
    "min_evaluations": 2,
    "min_confidence": 0.75,
    "min_call_duration_before_termination": 15,
    "min_transcript_length": 50,
    "min_transcript_turns": 1
  }
}

Configuration Parameters

Parameter	Type	Default	Description
`enabled`	boolean	`false`	Enable spam detection for this assistant
`denied_scenarios`	array	`[]`	Scenarios that indicate spam/unwanted calls
`allowed_scenarios`	array	`[]`	Scenarios that whitelist legitimate calls
`max_evaluations_per_call`	integer	`5`	Maximum evaluations per call (cost control)
`min_evaluations`	integer	`2`	Minimum evaluations before final verdict
`min_confidence`	float	`0.75`	Required confidence threshold (0-1)
`min_call_duration_before_termination`	integer	`15`	Grace period in seconds
`min_transcript_length`	integer	`50`	Minimum transcript characters to evaluate
`min_transcript_turns`	integer	`1`	Minimum caller turns before evaluation

Scenario Configuration

Denied Scenarios

Define what constitutes spam for your business. Be specific and descriptive:

Common Denied Scenarios

Telemarketing & Sales

“caller is trying to sell insurance products”
“caller is offering credit card services”
“caller is promoting investment opportunities”
“caller is selling extended warranties”

Surveys & Research

“caller is conducting a survey”
“caller is doing market research”
“caller is collecting opinions for a study”

Suspicious Behavior

“caller refuses to identify themselves”
“caller provides false or inconsistent information”
“caller is asking for sensitive personal information”
“caller claims to be from a government agency demanding immediate payment”

Automated Calls

“caller appears to be a robocall”
“caller is playing pre-recorded messages”
“call has long silences followed by scripted speech”

Abuse

“caller is using profanity or abusive language”
“caller is making threats”
“caller is harassing the assistant”

Allowed Scenarios

Whitelist legitimate call patterns to prevent false positives:

Common Allowed Scenarios

Customer Identification

“caller provides a valid order number”
“caller mentions a valid account number”
“caller references a recent transaction”
“caller knows their service agreement details”

Expected Callbacks

“caller is returning a call from our company”
“caller received a voicemail from us”
“caller has a scheduled callback appointment”

Business Relationships

“caller mentions a specific employee by name”
“caller is a known vendor or partner”
“caller references an active contract or agreement”

Context-Specific

“caller is responding to an appointment reminder”
“caller mentions the specific promotion they’re calling about”
“caller provides the reference number from their email”

Start with a small set of clear scenarios and expand based on real spam patterns you observe. Over-broad scenarios may cause false positives.

Verdict Aggregation

The system requires multiple consistent evaluations before taking action:

Requirements for Final Verdict

Minimum Evaluations: At least min_evaluations (default: 2) must be completed
Confidence Threshold: Average confidence must exceed min_confidence (default: 0.75)
Consistency: Evaluations must be consistent (not flip-flopping)
Transcript Quality: Sufficient transcript content to make a judgment

Example Aggregation

Evaluation	Is Spam	Confidence	Scenario
1	Yes	0.85	”selling insurance”
2	Yes	0.82	”selling insurance”
Final	Yes	0.835	”selling insurance”

If evaluations conflict (one spam, one not spam), more evaluations are gathered until consistency is achieved or the limit is reached.

Safety Features

Grace Period

No call can be terminated within the first min_call_duration_before_termination seconds (default: 15).This prevents premature termination from:

Misunderstood greetings
Brief initial silences
Caller gathering their thoughts

Even if spam is detected with high confidence early, termination waits until the grace period expires.

Non-Blocking Evaluation

Spam evaluation runs asynchronously and never blocks conversation flow:

Transcript is evaluated in background
AI continues responding normally during evaluation
No latency added to conversation
If evaluation is slow, conversation proceeds unaffected

Fail-Open Design

If any error occurs during spam detection:

The call continues normally
Error is logged for debugging
No false terminations from system issues

This ensures legitimate callers are never blocked due to technical problems.

Evaluation Limits

The max_evaluations_per_call parameter caps total evaluations to:

Control LLM costs
Prevent infinite evaluation loops
Once limit reached, no further evaluations occur

Typical calls need 2-3 evaluations to reach a verdict.

Call Termination

When spam is confirmed:

Verdict Persisted: The spam detection result is saved to the call record
Termination Event Published: A Redis pub/sub event signals call termination
Call Ends: The call is terminated with a configurable message
Analytics Updated: Call is marked as spam in analytics

Termination Data

The call record includes:

{
  "call_meta": {
    "spam_detection": {
      "is_spam": true,
      "detected_at": "2026-01-19T10:30:00Z",
      "reason": "Spam detected: caller is trying to sell insurance",
      "scenario_matched": "selling insurance products",
      "confidence": 0.85
    }
  }
}

Best Practices

Start Conservative

Begin with obvious spam scenarios and high confidence thresholds:

{
  "min_confidence": 0.85,
  "min_evaluations": 3,
  "min_call_duration_before_termination": 20
}

Monitor for false positives before loosening thresholds.

Use Specific Scenarios

Good: “caller is trying to sell life insurance or annuities”Bad: “caller is being annoying”Specific scenarios lead to more accurate detection and fewer false positives.

Balance Allowed Scenarios

If you see legitimate calls being flagged:

Review the call transcript
Identify the pattern that makes it legitimate
Add a corresponding allowed scenario

Allowed scenarios take precedence when both match.

Monitor and Iterate

Review spam-flagged calls regularly:

Check for false positives
Identify new spam patterns to add
Adjust confidence thresholds based on results
Use call analytics to track spam rates over time

Example Configurations

Customer Service
Sales Line
High Security

{
  "spam_detection_config": {
    "enabled": true,
    "denied_scenarios": [
      "caller is selling products or services",
      "caller is conducting surveys",
      "caller refuses to provide account information",
      "caller is making prank calls"
    ],
    "allowed_scenarios": [
      "caller provides order number or account number",
      "caller references recent purchase or service",
      "caller is returning our call"
    ],
    "min_confidence": 0.80,
    "min_call_duration_before_termination": 15
  }
}

{
  "spam_detection_config": {
    "enabled": true,
    "denied_scenarios": [
      "caller is selling competing products",
      "caller is a recruiter or headhunter",
      "caller is requesting donations",
      "caller is conducting political surveys"
    ],
    "allowed_scenarios": [
      "caller is asking about our products or services",
      "caller mentions seeing our advertisement",
      "caller was referred by existing customer"
    ],
    "min_confidence": 0.85,
    "min_call_duration_before_termination": 20
  }
}

{
  "spam_detection_config": {
    "enabled": true,
    "denied_scenarios": [
      "caller refuses to identify themselves",
      "caller cannot provide verification information",
      "caller is asking for sensitive data without authentication",
      "caller claims to be from IT and needs passwords"
    ],
    "allowed_scenarios": [
      "caller successfully verifies their identity",
      "caller provides valid employee ID",
      "caller is on the approved caller list"
    ],
    "min_confidence": 0.70,
    "min_evaluations": 2,
    "min_call_duration_before_termination": 30
  }
}

Troubleshooting

Legitimate Calls Being Flagged

Review the flagged call’s transcript
Check which scenario was matched
Add an allowed scenario to whitelist the pattern
Consider increasing min_confidence threshold

Spam Calls Not Being Detected

Verify spam detection is enabled for the assistant
Check if your denied scenarios are specific enough
Lower min_confidence if verdicts aren’t reaching threshold
Review the transcript—does it clearly match a denied scenario?

Too Many Evaluations

If you’re seeing high LLM costs from spam detection:

Reduce max_evaluations_per_call
Increase min_transcript_length to skip short transcripts
Increase min_transcript_turns to require more conversation

Spam detection results are included in call webhooks, allowing you to build custom analytics and alerting systems.

Getting Started

Core Concepts

AI Providers

Features

Advanced

Help & Resources

Overview

Real-Time Analysis

Configurable Scenarios

Fail-Open Design

How It Works

Configuration

Configuration Parameters

Scenario Configuration

Denied Scenarios

Allowed Scenarios

Verdict Aggregation

Requirements for Final Verdict

Example Aggregation

Safety Features

Call Termination

Termination Data

Best Practices

Example Configurations

Troubleshooting

​Overview

Real-Time Analysis

Configurable Scenarios

Fail-Open Design

​How It Works

​Configuration

​Configuration Parameters

​Scenario Configuration

​Denied Scenarios

​Allowed Scenarios

​Verdict Aggregation

​Requirements for Final Verdict

​Example Aggregation

​Safety Features

​Call Termination

​Termination Data

​Best Practices

​Example Configurations

​Troubleshooting

Overview

How It Works

Configuration

Configuration Parameters

Scenario Configuration

Denied Scenarios

Allowed Scenarios

Verdict Aggregation

Requirements for Final Verdict

Example Aggregation

Safety Features

Call Termination

Termination Data

Best Practices

Example Configurations

Troubleshooting