Official Kotlin SDK for the Tuteliq API
AI-powered child safety analysis
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dependencies {
implementation("dev.tuteliq:tuteliq:1.0.0")
}dependencies {
implementation 'dev.tuteliq:tuteliq:1.0.0'
}<dependency>
<groupId>dev.tuteliq</groupId>
<artifactId>tuteliq</artifactId>
<version>1.0.0</version>
</dependency>- Kotlin 1.9+
- Java 17+
import dev.tuteliq.*
import kotlinx.coroutines.runBlocking
fun main() = runBlocking {
val client = Tuteliq(apiKey = "your-api-key")
// Quick safety analysis
val result = client.analyze("Message to check")
if (result.riskLevel != RiskLevel.SAFE) {
println("Risk: ${result.riskLevel}")
println("Summary: ${result.summary}")
}
client.close()
}import dev.tuteliq.Tuteliq
// Simple
val client = Tuteliq(apiKey = "your-api-key")
// With options
val client = Tuteliq(
apiKey = "your-api-key",
timeout = 30_000L, // Request timeout in milliseconds
maxRetries = 3, // Retry attempts
retryDelay = 1_000L, // Initial retry delay in milliseconds
)val result = client.detectBullying("Nobody likes you, just leave")
if (result.isBullying) {
println("Severity: ${result.severity}") // Severity.MEDIUM
println("Types: ${result.bullyingType}") // ["exclusion", "verbal_abuse"]
println("Confidence: ${result.confidence}") // 0.92
println("Rationale: ${result.rationale}")
}import dev.tuteliq.*
val result = client.detectGrooming(
DetectGroomingInput(
messages = listOf(
GroomingMessage(role = MessageRole.ADULT, content = "This is our secret"),
GroomingMessage(role = MessageRole.CHILD, content = "Ok I won't tell"),
),
childAge = 12,
)
)
if (result.groomingRisk == GroomingRisk.HIGH) {
println("Flags: ${result.flags}") // ["secrecy", "isolation"]
}val result = client.detectUnsafe("I don't want to be here anymore")
if (result.unsafe) {
println("Categories: ${result.categories}") // ["self_harm", "crisis"]
println("Severity: ${result.severity}") // Severity.CRITICAL
}Runs bullying and unsafe detection:
val result = client.analyze("Message to check")
println("Risk Level: ${result.riskLevel}") // RiskLevel.SAFE/LOW/MEDIUM/HIGH/CRITICAL
println("Risk Score: ${result.riskScore}") // 0.0 - 1.0
println("Summary: ${result.summary}")
println("Action: ${result.recommendedAction}")val result = client.analyzeEmotions("I'm so stressed about everything")
println("Emotions: ${result.dominantEmotions}") // ["anxiety", "sadness"]
println("Trend: ${result.trend}") // EmotionTrend.WORSENING
println("Followup: ${result.recommendedFollowup}")import dev.tuteliq.*
val plan = client.getActionPlan(
GetActionPlanInput(
situation = "Someone is spreading rumors about me",
childAge = 12,
audience = Audience.CHILD,
severity = Severity.MEDIUM,
)
)
println("Steps: ${plan.steps}")
println("Tone: ${plan.tone}")import dev.tuteliq.*
val report = client.generateReport(
GenerateReportInput(
messages = listOf(
ReportMessage(sender = "user1", content = "Threatening message"),
ReportMessage(sender = "child", content = "Please stop"),
),
childAge = 14,
)
)
println("Summary: ${report.summary}")
println("Risk: ${report.riskLevel}")
println("Next Steps: ${report.recommendedNextSteps}")All methods support externalId and metadata for correlating requests:
val result = client.detectBullying(
content = "Test message",
externalId = "msg_12345",
metadata = mapOf("user_id" to "usr_abc", "session" to "sess_xyz"),
)
// Echoed back in response
println(result.externalId) // "msg_12345"
println(result.metadata) // {"user_id": "usr_abc", ...}val result = client.detectBullying("test")
// Access usage stats after any request
client.usage?.let { usage ->
println("Limit: ${usage.limit}")
println("Used: ${usage.used}")
println("Remaining: ${usage.remaining}")
}
// Request metadata
println("Request ID: ${client.lastRequestId}")import dev.tuteliq.*
try {
val result = client.detectBullying("test")
} catch (e: AuthenticationException) {
println("Auth error: ${e.message}")
} catch (e: RateLimitException) {
println("Rate limited: ${e.message}")
} catch (e: ValidationException) {
println("Invalid input: ${e.message}, details: ${e.details}")
} catch (e: ServerException) {
println("Server error ${e.statusCode}: ${e.message}")
} catch (e: TimeoutException) {
println("Timeout: ${e.message}")
} catch (e: NetworkException) {
println("Network error: ${e.message}")
} catch (e: TuteliqException) {
println("Error: ${e.message}")
}import dev.tuteliq.*
import kotlinx.coroutines.Dispatchers
import kotlinx.coroutines.withContext
class SafetyChecker(private val apiKey: String) {
private val client = Tuteliq(apiKey = apiKey)
suspend fun checkMessage(message: String): AnalyzeResult {
return withContext(Dispatchers.IO) {
client.analyze(message)
}
}
fun close() {
client.close()
}
}The bullying and unsafe content methods analyze a single text field per request. If your app receives messages one at a time, concatenate a sliding window of recent messages into one string before calling the API. Single words or short fragments lack context for accurate detection and can be exploited to bypass safety filters.
// Bad — each message analyzed in isolation, easily evaded
messages.forEach { msg ->
client.detectBullying(text = msg)
}
// Good — recent messages analyzed together
val window = recentMessages.takeLast(10).joinToString(" ")
client.detectBullying(text = window)The grooming method already accepts a messages list and analyzes the full conversation in context.
Enable PII_REDACTION_ENABLED=true on your Tuteliq API to automatically strip emails, phone numbers, URLs, social handles, IPs, and other PII from detection summaries and webhook payloads. The original text is still analyzed in full — only stored outputs are scrubbed.
- API Docs: api.tuteliq.ai/docs
- Discord: discord.gg/7kbTeRYRXD
- Email: support@tuteliq.ai
- Issues: GitHub Issues
MIT License - see LICENSE for details.
Before you decide to contribute or sponsor, read these numbers. They are not projections. They are not estimates from a pitch deck. They are verified statistics from the University of Edinburgh, UNICEF, NCMEC, and Interpol.
- 302 million children are victims of online sexual exploitation and abuse every year. That is 10 children every second. (Childlight / University of Edinburgh, 2024)
- 1 in 8 children globally have been victims of non-consensual sexual imagery in the past year. (Childlight, 2024)
- 370 million girls and women alive today experienced rape or sexual assault in childhood. An estimated 240–310 million boys and men experienced the same. (UNICEF, 2024)
- 29.2 million incidents of suspected child sexual exploitation were reported to NCMEC's CyberTipline in 2024 alone — containing 62.9 million files (images, videos). (NCMEC, 2025)
- 546,000 reports of online enticement (adults grooming children) in 2024 — a 192% increase from the year before. (NCMEC, 2025)
- 1,325% increase in AI-generated child sexual abuse material reports between 2023 and 2024. The technology that should protect children is being weaponized against them. (NCMEC, 2025)
- 100 sextortion reports per day to NCMEC. Since 2021, at least 36 teenage boys have taken their own lives because they were victimized by sextortion. (NCMEC, 2025)
- 84% of reports resolve outside the United States. This is not an American problem. This is a global emergency. (NCMEC, 2025)
End-to-end encryption is making platforms blind. In 2024, platforms reported 7 million fewer incidents than the year before — not because abuse stopped, but because they can no longer see it. The tools that catch known images are failing. The systems that rely on human moderators are overwhelmed. The technology to detect behavior — grooming patterns, escalation, manipulation — in real-time text conversations exists right now. It is running at api.tuteliq.ai.
The question is not whether this technology is possible. The question is whether we build the company to put it everywhere it needs to be.
Every second we wait, another child is harmed.
We have the technology. We need the support.
If this mission matters to you, consider sponsoring our open-source work so we can keep building the tools that protect children — and keep them free and accessible for everyone.
Built with care for child safety by the Tuteliq team