Official Swift SDK for the Tuteliq API
AI-powered child safety analysis for iOS, macOS, tvOS, and watchOS
API Docs • Dashboard • Discord
Add Tuteliq to your Package.swift:
dependencies: [
.package(url: "https://github.com/Tuteliq/swift.git", from: "1.0.0")
]Or in Xcode: File → Add Package Dependencies → Enter:
https://github.com/Tuteliq/swift.git
import Tuteliq
let tuteliq = Tuteliq(apiKey: "your-api-key")
// Quick safety analysis
let result = try await tuteliq.analyze(content: "Message to check")
if result.riskLevel != .safe {
print("Risk: \(result.riskLevel)")
print("Summary: \(result.summary)")
}// Simple
let tuteliq = Tuteliq(apiKey: "your-api-key")
// With options
let tuteliq = Tuteliq(
apiKey: "your-api-key",
timeout: 30, // Request timeout in seconds
maxRetries: 3, // Retry attempts
retryDelay: 1 // Initial retry delay in seconds
)let result = try await tuteliq.detectBullying(
content: "Nobody likes you, just leave"
)
if result.isBullying {
print("Severity: \(result.severity)") // .low, .medium, .high, .critical
print("Types: \(result.bullyingType)") // ["exclusion", "verbal_abuse"]
print("Confidence: \(result.confidence)") // 0.92
print("Rationale: \(result.rationale)")
}let result = try await tuteliq.detectGrooming(
DetectGroomingInput(
messages: [
GroomingMessage(role: .adult, content: "This is our secret"),
GroomingMessage(role: .child, content: "Ok I won't tell")
],
childAge: 12
)
)
if result.groomingRisk == .high {
print("Flags: \(result.flags)") // ["secrecy", "isolation"]
}let result = try await tuteliq.detectUnsafe(
content: "I don't want to be here anymore"
)
if result.unsafe {
print("Categories: \(result.categories)") // ["self_harm", "crisis"]
print("Severity: \(result.severity)") // .critical
}Transcribe audio and run safety analysis on the transcript:
let audioData = try Data(contentsOf: audioURL)
let result = try await tuteliq.analyzeVoice(
AnalyzeVoiceInput(
file: audioData,
filename: "recording.mp3",
analysisType: "all", // "bullying", "unsafe", "grooming", "emotions", or "all"
ageGroup: "11-13"
)
)
print("Transcript: \(result.transcription.text)")
print("Duration: \(result.transcription.duration)s")
print("Segments: \(result.transcription.segments.count)")
print("Risk Score: \(result.overallRiskScore)") // 0.0 - 1.0
print("Severity: \(result.overallSeverity)")Supported audio formats: mp3, wav, m4a, ogg, flac, webm, mp4 (max 25MB).
Analyze images for visual safety concerns and extract text via OCR:
let imageData = try Data(contentsOf: imageURL)
let result = try await tuteliq.analyzeImage(
AnalyzeImageInput(
file: imageData,
filename: "screenshot.png",
analysisType: "all" // "bullying", "unsafe", "emotions", or "all"
)
)
print("OCR Text: \(result.vision.extractedText)")
print("Visual Severity: \(result.vision.visualSeverity)")
print("Categories: \(result.vision.visualCategories)")
print("Contains Text: \(result.vision.containsText)")
print("Risk Score: \(result.overallRiskScore)")
print("Severity: \(result.overallSeverity)")Supported image formats: png, jpg, jpeg, gif, webp (max 10MB).
Runs bullying and unsafe detection in parallel:
let result = try await tuteliq.analyze(content: "Message to check")
print("Risk Level: \(result.riskLevel)") // .safe, .low, .medium, .high, .critical
print("Risk Score: \(result.riskScore)") // 0.0 - 1.0
print("Summary: \(result.summary)")
print("Action: \(result.recommendedAction)")let result = try await tuteliq.analyzeEmotions(
content: "I'm so stressed about everything"
)
print("Emotions: \(result.dominantEmotions)") // ["anxiety", "sadness"]
print("Trend: \(result.trend)") // .improving, .stable, .worsening
print("Followup: \(result.recommendedFollowup)")let plan = try await tuteliq.getActionPlan(
GetActionPlanInput(
situation: "Someone is spreading rumors about me",
childAge: 12,
audience: .child,
severity: .medium
)
)
print("Steps: \(plan.steps)")
print("Tone: \(plan.tone)")let report = try await tuteliq.generateReport(
GenerateReportInput(
messages: [
ReportMessage(sender: "user1", content: "Threatening message"),
ReportMessage(sender: "child", content: "Please stop")
],
childAge: 14
)
)
print("Summary: \(report.summary)")
print("Risk: \(report.riskLevel)")
print("Next Steps: \(report.recommendedNextSteps)")All methods support externalId and metadata for correlating requests:
let result = try await tuteliq.detectBullying(
DetectBullyingInput(
content: "Test message",
externalId: "msg_12345",
metadata: ["user_id": "usr_abc", "session": "sess_xyz"]
)
)
// Echoed back in response
print(result.externalId) // "msg_12345"let result = try await tuteliq.detectBullying(content: "test")
// Access usage stats after any request
if let usage = tuteliq.usage {
print("Limit: \(usage.limit)")
print("Used: \(usage.used)")
print("Remaining: \(usage.remaining)")
}
// Request metadata
print("Request ID: \(tuteliq.lastRequestId ?? "N/A")")
print("Latency: \(tuteliq.lastLatency ?? 0)s")do {
let result = try await tuteliq.detectBullying(content: "test")
} catch let error as TuteliqError {
switch error {
case .authenticationError(let message):
print("Auth error: \(message)")
case .rateLimitError(let message):
print("Rate limited: \(message)")
case .validationError(let message, let details):
print("Invalid input: \(message)")
case .serverError(let message, let statusCode):
print("Server error \(statusCode): \(message)")
case .timeoutError(let message):
print("Timeout: \(message)")
case .networkError(let message):
print("Network error: \(message)")
case .unknownError(let message):
print("Error: \(message)")
}
}import SwiftUI
import Tuteliq
struct ContentView: View {
@State private var message = ""
@State private var warning: String?
@State private var isChecking = false
let tuteliq = Tuteliq(apiKey: ProcessInfo.processInfo.environment["TUTELIQ_API_KEY"] ?? "")
var body: some View {
VStack {
TextField("Message", text: $message)
.textFieldStyle(.roundedBorder)
if let warning = warning {
Text(warning)
.foregroundColor(.red)
}
Button("Send") {
Task { await checkAndSend() }
}
.disabled(isChecking)
}
.padding()
}
func checkAndSend() async {
isChecking = true
defer { isChecking = false }
do {
let result = try await tuteliq.analyze(content: message)
if result.riskLevel != .safe {
warning = result.summary
return
}
// Safe to send
warning = nil
// ... send message
} catch {
warning = error.localizedDescription
}
}
}- Swift 5.9+
- iOS 15+ / macOS 12+ / tvOS 15+ / watchOS 8+
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
for msg in messages {
try await client.detectBullying(text: msg)
}
// Good — recent messages analyzed together
let window = recentMessages.suffix(10).joined(separator: " ")
try await client.detectBullying(text: window)The grooming method already accepts a messages array 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