> ## Documentation Index > Fetch the complete documentation index at: https://docs.tuteliq.ai/llms.txt > Use this file to discover all available pages before exploring further. # Composite non-LLM image verification > Run a configurable set of forensic checks over a single image in one call: face matching against a reference image, age estimation, NSFW/skin-exposure forensics, and synthetic-image detection (deep-image classifier + C2PA provenance). All checks are deterministic/model-based — no LLM ever sees the image. Images are processed in memory only and never stored. ## OpenAPI ````yaml https://api.tuteliq.ai/docs/json post /api/v1/verify-image-match openapi: 3.1.0 info: title: Tuteliq API description: > # AI-Powered Child Safety API You're building a chat feature for a kids' learning app. A user sends a message that looks innocuous on the surface but contains subtle grooming escalation patterns. Tuteliq catches it, scores the risk by the child's age group, and gives your trust & safety team an actionable report — all in under 400ms. One API call replaces months of in-house ML work. --- ## KOSA Harm Categories Coverage | Harm Category | Endpoint | Status | |--------------|----------|--------| | Eating Disorders | `/safety/unsafe` | ✅ Covered | | Substance Use | `/safety/unsafe` | ✅ Covered | | Suicidal Behaviors | `/safety/unsafe` | ✅ Covered | | Depression & Anxiety | `/safety/unsafe` + `/analysis/emotions` | ✅ Covered | | Compulsive Usage | `/safety/unsafe` | ✅ Covered | | Harassment & Bullying | `/safety/bullying` | ✅ Covered | | Sexual Exploitation | `/safety/grooming` + `/safety/unsafe` | ✅ Covered | | Voice & Audio Threats | `/safety/voice` | ✅ Covered | | Visual Content Risks | `/safety/image` | ✅ Covered | --- ## Key Features - **Age-Calibrated Severity** — Risk scores adjust across four age brackets (under 10, 10–12, 13–15, 16–17) so moderation matches developmental context. - **Nuanced Context Recognition** — Distinguishes hyperbolic teen language from genuine crisis signals, dramatically reducing false positives. - **Grooming Pattern Detection** — Identifies multi-stage grooming tactics: trust escalation, secrecy requests, isolation attempts, and boundary violations. - **Emotional Trend Analysis** — Tracks persistent mood patterns across conversations to surface early indicators of declining mental health. - **Voice & Image Analysis** — Upload audio or images for transcription, OCR, and full safety analysis in a single call. - **Real-time Voice Streaming** — WebSocket endpoint for live audio moderation with configurable flush intervals and per-category alerts. - **Guidance & Reports** — Generate age-appropriate action plans and professional incident reports ready for handoff to human reviewers. - **Batch Processing & Webhooks** — Analyze up to 50 items per batch call, with HMAC-signed webhook alerts for critical incidents. --- ## Credits per Action | Endpoint | Credits | Notes | |----------|---------|-------| | `detectBullying` | 1 | Single text analysis | | `detectUnsafe` | 1 | Single text analysis | | `detectGrooming` | 1 per 10 msgs | `ceil(messages / 10)`, min 1 | | `analyzeEmotions` | 1 per 10 msgs | `ceil(messages / 10)`, min 1 | | `getActionPlan` | 2 | Longer generation | | `generateReport` | 3 | Structured output | | `analyzeVoice` | 5 | Transcription + analysis | | `analyzeImage` | 3 | Vision + OCR + analysis | Every response includes a `credits_used` field. Credit balance is also available via the `X-Credits-Remaining` response header. --- ## Quick Start **1.** Get your API key at [tuteliq.ai/dashboard](https://tuteliq.ai/dashboard) **2.** Install the SDK: ```bash npm install @tuteliq/sdk ``` **3.** Make your first call: ```typescript import Tuteliq from '@tuteliq/sdk' const tuteliq = new Tuteliq({ apiKey: 'YOUR_API_KEY' }) const result = await tuteliq.detectUnsafe({ content: "Don't talk to your parents about us meeting up", context: { age_group: "13-15" } }) console.log(result.unsafe) // true console.log(result.categories) // ["grooming_adjacent", "secrecy"] console.log(result.severity) // "high" console.log(result.recommended_action) // "Escalate to moderator" ``` --- ## Performance | Metric | Value | |--------|-------| | Average latency | ~400ms | | p95 latency | ~800ms | | Uptime SLA | 99.9% | Check real-time status at [tuteliq.ai/status](https://tuteliq.ai/status) version: 1.0.0 contact: name: Tuteliq Support url: https://tuteliq.ai email: support@tuteliq.ai license: name: Proprietary url: https://tuteliq.ai/terms servers: - url: https://api.tuteliq.ai description: Production server - url: http://localhost:3000 description: Development server security: - bearerAuth: [] - apiKeyHeader: [] tags: - name: Safety description: >- Core detection endpoints for all nine KOSA harm categories. Supports text, voice, and image input with age-calibrated severity scoring. - name: Fraud description: >- Financial and social fraud detection — social engineering, app fraud, romance scams, and money mule recruitment. Identifies tactics targeting minors and vulnerable users with evidence-based scoring. - name: Safety Extended description: >- Extended safety detection — gambling harm, coercive control, vulnerability exploitation with cross-endpoint modifiers, and radicalisation. Requires Indie tier or above. - name: Analyse description: >- Multi-endpoint analysis. Fan-out a single text to up to 10 detection endpoints in parallel with aggregated results, vulnerability modifier support, and per-endpoint breakdowns. - name: Analysis description: >- Emotional intelligence endpoints. Analyze conversations for dominant emotions, sentiment trends (improving/stable/worsening), and early indicators of depression or anxiety. Designed to surface mental health risks before they escalate. - name: Guidance description: >- Post-detection action plans tailored by audience. Generate age-appropriate guidance for children, parents, or platform trust & safety teams — complete with reading-level calibration and tone adjustment. Goes beyond "here's a risk score" to answer "what do we do about it?" - name: Reports description: >- Professional incident report generation for schools, counselors, and moderators. Converts raw conversation data into structured reports with risk levels, categorized findings, and recommended next steps — ready for handoff to human reviewers. - name: Batch description: >- Analyze up to 50 items in a single request with optional parallel processing. Supports all analysis types (bullying, grooming, unsafe, emotions). Built for production pipelines that need to process backlogs or moderate content in bulk. - name: Webhooks description: >- Real-time notification endpoints for receiving alerts when critical incidents are detected. Full CRUD management with HMAC-SHA256 signed payloads, automatic retry on failure, secret regeneration, and test delivery — everything you need for production event-driven architectures. - name: Usage description: >- API usage tracking, quota monitoring, and rate limit status. View daily summaries, historical trends, per-tool breakdowns, and monthly billing period usage. Includes upgrade recommendations when approaching limits. - name: Policy description: >- Customize detection behavior for your use case. Configure sensitivity thresholds, category weights, and moderation rules without changing your integration code. - name: Pricing description: >- Public pricing plan information. Browse available tiers, features, and limits. The public endpoint requires no authentication; detailed plan info requires an API key. - name: Account description: >- GDPR-compliant account data management. Exercise the Right to Erasure (Article 17), Right to Data Portability (Article 20), and Right to Rectification (Article 16). Manage consent records and access your full audit trail. Available to all tiers — privacy is not a premium feature. - name: Compliance description: >- Public transparency endpoints for GDPR compliance. Machine-readable Data Processing Agreement (DPA), current sub-processor list, and data retention schedules. No authentication required — anyone can verify our data practices. - name: Admin description: >- Administrative endpoints for breach management and data retention. Log, track, and manage data breach incidents with full audit trails and notification status tracking. Trigger manual data retention cleanup when needed. - name: Health description: >- Health check and monitoring endpoints. Liveness and readiness probes for Kubernetes/Cloud Run, full dependency health checks, and detailed component status for debugging. - name: Status description: >- Public API status and uptime monitoring. Component-level health (API, database, cache, AI engine), uptime percentages, and an embeddable status page. Use /status/ping for external monitoring services like UptimeRobot. externalDocs: description: KOSA Compliance Documentation url: https://tuteliq.ai/docs/kosa-compliance paths: /api/v1/verify-image-match: post: tags: - Verification summary: Composite non-LLM image verification description: >- Run a configurable set of forensic checks over a single image in one call: face matching against a reference image, age estimation, NSFW/skin-exposure forensics, and synthetic-image detection (deep-image classifier + C2PA provenance). All checks are deterministic/model-based — no LLM ever sees the image. Images are processed in memory only and never stored. requestBody: content: application/json: schema: type: object required: - image properties: image: type: string maxLength: 20971524 description: >- The image to verify, base64-encoded (max 15MB decoded). Processed transiently — never stored. reference_image: type: string maxLength: 20971524 description: >- Optional reference image (base64) for the face_match check. Without it, face_match is skipped even when requested. checks: type: array items: type: string enum: - face_match - age - forensic - synthetic maxItems: 4 description: >- Subset of checks to run. Defaults to all of: face_match, age, forensic, synthetic. external_id: type: string maxLength: 255 description: >- Your unique identifier (e.g. message ID, content ID) for correlating Tuteliq results with your own system. Echoed back in the response and included in webhook payloads so you can match alerts to the original content. customer_id: type: string maxLength: 255 description: >- Your end-customer identifier for multi-tenant / B2B2C scenarios. If you serve multiple customers (schools, apps, brands) from a single API key, set this to route webhook alerts to the correct customer. Echoed back in the response and included in webhook payloads as "customerId". metadata: type: object additionalProperties: true maxProperties: 20 description: >- Arbitrary key-value pairs for additional context (e.g. { "channel": "discord", "region": "eu" }). Stored with the detection result, echoed in the response, and included in webhook payloads. Maximum 20 properties. incident_moderation_enabled: type: boolean description: >- Per-call override of your account incident logging setting. When set, it takes precedence for THIS request: true forces the incident to be persisted, false suppresses persistence. Omit to use your account default (which itself defaults to enabled). Useful to suppress logging for test traffic or to opt specific calls in or out. required: true responses: '200': description: Default Response content: application/json: schema: type: object properties: schema_version: type: string description: Response schema version (currently "1.0") checks_run: type: array items: type: string description: >- The checks that actually ran (face_match is excluded when no reference_image was provided) face_match: type: object properties: matched: type: boolean description: Whether the two faces match similarity: type: number description: Face similarity (0.0-1.0) reason: type: string description: Why matching failed (e.g. "no_face_detected") age: type: object nullable: true properties: estimated_age: type: number description: Estimated age of the detected face in years confidence: type: number description: Face-detection confidence (0.0-1.0) description: >- Age estimate for the primary image, or null when no face was detected forensic: type: object properties: nsfw: type: boolean description: Whether the image was classified as explicit nsfw_confidence: type: number description: NSFW classifier score (0.0-1.0) skin_exposure_level: type: string enum: - low - moderate - high description: Coarse skin-exposure level synthetic: type: object properties: is_synthetic: type: boolean description: >- Deep-image classifier verdict (absent when the service is unavailable) synthetic_probability: type: number description: Deep-image synthetic probability (0.0-1.0) has_c2pa: type: boolean description: A C2PA / Content Credentials manifest is embedded is_ai_generated: type: boolean description: The C2PA manifest declares AI generation credits_used: type: number description: Credits charged for this call processing_ms: type: number description: Total processing time in milliseconds external_id: type: string description: Echo of the external_id you provided in the request customer_id: type: string description: Echo of the customer_id you provided in the request metadata: type: object additionalProperties: true description: Echo of the metadata you provided in the request components: securitySchemes: bearerAuth: type: http scheme: bearer description: API key as Bearer token apiKeyHeader: type: apiKey in: header name: x-api-key description: API key in header ````