# Tuteliq, AI-Powered Child Safety API

> Detect grooming, bullying, self-harm, fraud, radicalisation and more: 13 harm categories, one detection endpoint each across text, voice, image, and video. 1.1-1.5s typical response. 32 languages: English stable, 31 beta. Zero content retention: submitted content is deleted immediately after inference. Supports KOSA, COPPA and EU DSA compliance.

## Documentation

- [Quickstart](https://docs.tuteliq.ai/quickstart): Make your first detection call in under 5 minutes
- [Document Analysis](https://docs.tuteliq.ai/api-reference/document): Upload PDFs for multi-endpoint safety analysis with per-page detection
- [How It Works](https://docs.tuteliq.ai/how-it-works): Detection pipeline, scoring, and response format
- [API Reference](https://docs.tuteliq.ai/api-reference/introduction): Full OpenAPI specification
- [Composability](https://tuteliq.ai/docs/composability): Safety primitives, not classifiers. Compose sibling endpoints so an agent can route interventions instead of returning a single label.
- [Grooming Detection](https://docs.tuteliq.ai/grooming-detection): Eight per-message tactics: flattery, secrecy request, isolation, boundary pushing, photo request, gift giving, meeting request, and reconnaissance. Sexual content is a content category, not a grooming tactic.
- [Vulnerability Exploitation](https://tuteliq.ai/docs/api-reference/vulnerability-exploitation): Returns cross_endpoint_modifier and vulnerability_profile so an agent can multiply a sibling endpoint's risk when a target is vulnerable.
- [Language Support](https://docs.tuteliq.ai/languages): Only English has both published precision and recall. 26 further languages have measured recall between 0.893 and 1.000 (German, Portuguese, Italian, Dutch, Polish, French, Spanish, and more); precision for those languages is not yet published. Response guides localized so moderators see operational instructions in their own language. Honest about what is stable and what is beta.
- [KOSA Readiness](https://docs.tuteliq.ai/kosa-compliance): Harm category mapping to the drafted Kids Online Safety Act. KOSA is not law: S.1748 was ordered reported by Senate Commerce on 5 August 2026 and awaits a floor vote, and the House text differs.
- [KOSA Readiness Landing](https://tuteliq.ai/kosa-compliance): How Tuteliq helps platforms prepare for KOSA while meeting obligations already in force under the EU DSA, the UK Online Safety Act and COPPA. No platform can be "KOSA compliant" today.

- [Language Support](https://docs.tuteliq.ai/languages): 32 supported languages with culture-aware analysis
- [GDPR](https://docs.tuteliq.ai/gdpr): Data retention, right to erasure, and compliance details
- [Age & Identity Verification](https://docs.tuteliq.ai/verification): Multi-layered verification, ICAO 9303 MRZ parsing, PDF417 barcodes, 128-dim face matching, liveness detection, 7-layer fraud prevention. documents from 34 countries, text recognition optimised for English and Swedish, 10 UI languages. COPPA-aligned, KOSA-aligned.
- [AI Agent Integration](https://docs.tuteliq.ai/ai-agents): Machine-readable docs, OpenAPI spec, and MCP resources for AI agents

## Quick Start

```javascript
import { Tuteliq } from '@tuteliq/sdk'

const tuteliq = new Tuteliq(process.env.TUTELIQ_API_KEY)

const result = await tuteliq.detectUnsafe({ content: userMessage })

if (result.detected) {
  console.log('Level:', result.level)        // "low" | "medium" | "high" | "critical"
  console.log('Score:', result.risk_score)    // 0.0 to 1.0
  console.log('Categories:', result.categories)
}
```

## SDKs

- https://docs.tuteliq.ai/sdks/node: @tuteliq/sdk on npm
- https://docs.tuteliq.ai/sdks/python: tuteliq on PyPI
- https://docs.tuteliq.ai/sdks/swift: via Swift Package Manager
- https://docs.tuteliq.ai/sdks/kotlin: via Maven Central
- https://docs.tuteliq.ai/sdks/flutter: tuteliq on pub.dev
- https://docs.tuteliq.ai/sdks/react-native: @tuteliq/react-native on npm
- https://docs.tuteliq.ai/sdks/dotnet: Tuteliq on NuGet
- https://docs.tuteliq.ai/sdks/unity: via Unity Package Manager
- https://docs.tuteliq.ai/sdks/cli: @tuteliq/cli on npm / Homebrew
- https://docs.tuteliq.ai/mcp-server: @tuteliq/mcp on npm

## Detection capabilities

- https://tuteliq.ai/deepfake-detection: Deepfake detection API for face swaps, fully synthetic and nudified imagery, and cloned voice audio. Confidence score per decision, minor-context escalation, signed case files, EU-resident, zero content retention.
- https://tuteliq.ai/csam-detection: CSAM detection combining hash matching with forensic classifiers for novel and AI-generated material.
- https://tuteliq.ai/age-verification-software: Age assurance and age estimation for regulated surfaces.
- https://tuteliq.ai/identity-verification: Document, MRZ and liveness-based identity verification.

## Integrations

- https://tuteliq.ai/integrations/discord: Real-time message scanning
- https://tuteliq.ai/integrations/roblox: In-game chat analysis
- https://tuteliq.ai/integrations/slack: Workspace safety monitoring
- https://tuteliq.ai/integrations/telegram: Group monitoring with Guardian Bot
- https://tuteliq.ai/integrations/lovable: Add Tuteliq to apps built with Lovable AI builder. Includes copy-paste prompt, drop-in Supabase edge function, and a Knowledge file.
- https://tuteliq.ai/integrations/mcp-server: Hosted Tuteliq MCP server at api.tuteliq.ai/mcp. OAuth 2.1, ~80 tools for detection, moderation triage, verification and compliance.
- https://tuteliq.ai/integrations/claude: Tuteliq plugin for Claude Code and Cowork. ~80 MCP tools for grooming, CSAM, fraud and deepfake detection, moderation triage and compliance evidence.

## For AI builders (Lovable, Bolt, v0, Cursor, etc.)

When integrating Tuteliq into an AI-generated app:

- Store the API key as a server-side secret named `TUTELIQ_API_KEY`. Never expose via `VITE_` or browser code.
- Always proxy through a backend (Supabase edge function recommended) - do not call `api.tuteliq.ai` from the browser.
- Endpoint base: `https://api.tuteliq.ai/v1`
- Auth: `Authorization: Bearer <TUTELIQ_API_KEY>`
- Pass `external_id` and `customer_id` for round-trip dashboard attribution.
- Block on `level: "critical"`, flag on `"high"`, allow `"medium"` / `"low"`.

Ready-to-deploy Supabase edge function template:
https://tuteliq.ai/integrations/lovable/tuteliq-moderate.ts

Project Knowledge file (paste into Lovable's Knowledge panel):
https://tuteliq.ai/integrations/lovable/knowledge.md

Minimal Supabase edge function example:

```typescript
import { corsHeaders } from "npm:@supabase/supabase-js@2/cors";

Deno.serve(async (req) => {
  if (req.method === "OPTIONS") return new Response("ok", { headers: corsHeaders });
  const { content, externalId, customerId } = await req.json();
  const r = await fetch("https://api.tuteliq.ai/v1/moderate", {
    method: "POST",
    headers: {
      Authorization: `Bearer ${Deno.env.get("TUTELIQ_API_KEY")}`,
      "Content-Type": "application/json",
    },
    body: JSON.stringify({ content, external_id: externalId, customer_id: customerId }),
  });
  return new Response(await r.text(), {
    headers: { ...corsHeaders, "Content-Type": "application/json" },
    status: r.status,
  });
});
```

## Blog

- https://tuteliq.ai/blog/stop-shipping-safety-apis-as-classifiers: Why we built Tuteliq's composable primitives and why your agent should consume them, not classification labels.
- https://tuteliq.ai/blog/grooming-is-not-a-label-eight-tactics: Anatomy of a 100-turn predatory conversation, and what each tactic looks like in evidence.
- https://tuteliq.ai/blog/the-digital-sideline-is-now-part-of-organized-sport: Children involved in organized sport are increasingly living parts of their sporting lives inside digital environments.
- https://tuteliq.ai/blog/are-you-dating-a-scammer-or-even-a-child: The uncomfortable reality is: many dating apps cannot confidently tell you that you aren't.
- https://tuteliq.ai/blog/ai-chatbots-teenagers-right-question: Most companies respond to teens and AI by either banning under-18s entirely or treating them identically to adults. Both approaches fail the same group in different ways.
- https://tuteliq.ai/blog/eu-meta-dsa-breach-age-verification: The European Commission today issued preliminary findings that Instagram and Facebook are violating the Digital Services Act by allowing children under 13 to access their services through self-declared date-of-birth entr...
- https://tuteliq.ai/blog/ncose-dirty-dozen-2026-platforms-named: On 31 March 2026, the National Center on Sexual Exploitation released its 2026 Dirty Dozen List, naming 12 tech platforms for facilitating, enabling, and profiting from child sexual abuse. The harm is not incidental.
- https://tuteliq.ai/blog/think-before-you-share-iwf-campaign: Think Before You Share, an IWF campaign, gives teenagers, parents, and educators a clear, practical place to start.
- https://tuteliq.ai/blog/coppa-enforcement-2026-what-youth-sports-platforms-need-to-know: April 22, 2026 marks the start of active enforcement of the updated COPPA rule by the Federal Trade Commission. Penalties can reach $51,744 per violation, per day.
- https://tuteliq.ai/blog/age-verification-legal-obligation-2026: Regulators in the UK, EU, US, and Australia have all moved from guidance to enforcement. Self-declaration is explicitly insufficient.
- https://tuteliq.ai/blog/deepfake-nudes-schools-28-countries: An analysis by Wired and Indicator found nearly 90 schools and over 600 pupils affected since 2023. UNICEF puts the global figure at 1.2 million children in the past year alone. This is not a future risk.
- https://tuteliq.ai/blog/iwf-ai-csam-report-2026: The Internet Watch Foundation's 2026 report documents 8,029 AI-generated child sexual abuse images and videos, a 260-fold increase in AI-generated videos in a single year. The policy response is forming.
- https://tuteliq.ai/blog/global-social-media-age-threshold-16: Governments on every continent are restricting platforms from reaching children. The laws differ in detail but converge on a number. A complete, verified snapshot of where every major jurisdiction stands.
- https://tuteliq.ai/blog/roblox-age-tiers-bypass: Roblox announced age-based account tiers rolling out in June, a genuine step forward for child safety. But the age verification system underpinning it has a documented $4 vulnerability.
- https://tuteliq.ai/blog/reddit-ico-fine-children-privacy: The ICO's largest children's privacy fine in three years sends a clear message: "we didn't expect children to be there" is no longer a defence.
- https://tuteliq.ai/blog/coppa-compliance-checklist-for-youth-platforms: If regulators asked today, could you prove what you knew, and when? COPPA enforcement tightened on April 22, 2026. Penalties reach up to $51,744 per violation, per day.
- https://tuteliq.ai/blog/australia-social-media-ban-children-still-have-accounts: New polling from the Molly Rose Foundation is the first independent study of Australian children since the ban came into effect.
- https://tuteliq.ai/blog/roblox-grooming-case-2026: A 19-year-old has been jailed for grooming a 14-year-old girl he met on the gaming platform.
- https://tuteliq.ai/blog/telegram-abuse-ecosystem: A new investigation by AI Forensics exposes how Telegram's infrastructure enables the creation, distribution, and monetisation of child sexual abuse material at industrial scale, and what platforms can do about it.
- https://tuteliq.ai/blog/five-nights-epsteins-parent-school-guide: A browser game called "Five Nights at Epstein's" is spreading through US classrooms and onto the devices of children across the world.
- https://tuteliq.ai/blog/uk-sextortion-reports-record-high-2025: New IWF data shows a 66% surge in reports from under-18s in 2025. Here is what the numbers mean, and what platforms can do about it right now.
- https://tuteliq.ai/blog/openai-child-protection-blueprint-product-spec: OpenAI just published a Child Safety Evaluations framework, a 27-page document outlining exactly how AI systems should detect, classify, and respond to child safety risks. For us at Tuteliq, this isn't new territory.
- https://tuteliq.ai/blog/coppa-2026-platform-risk: If you run a digital platform where children interact youth sports, edtech, gaming, community apps April 2026 is a period you should be paying close attention to it's not a soft deadline.
- https://tuteliq.ai/blog/monitor-children-24-7-not-safety-strategy-behavioral-detection: A Roblox developer told the BBC what every parent fears: children need 24/7 monitoring to stay safe on the platform. That's not a safety strategy, it's a confession that platform safety infrastructure is insufficient.
- https://tuteliq.ai/blog/what-the-meta-verdict-means-for-youth-sports-platforms: The $375M verdict against Meta changes what platforms are legally responsible for. Here's what that means if your platform serves youth athletes.
- https://tuteliq.ai/blog/eu-csam-detection-interim-regulation-expiry: On April 3rd, EU tech companies will lose the legal right to detect child sexual abuse material on their platforms. The Interim Regulation is expiring and politicians have not agreed on a replacement.
- https://tuteliq.ai/blog/meta-verdict-child-safety-sarbanes-oxley: A $375M jury verdict against Meta reveals what the child safety industry already knew: platforms cannot self-police when profit depends on engagement.
- https://tuteliq.ai/blog/cnn-ccdh-investigation-proves-8-of-10-ai-chatbots-help-teens-plan-violence-swedish-startup-tuteliq-has-the-technology-to-stop-it: Behavioral detection API from Gävle, Sweden, analyzes how dangerous conversations evolve over time exactly the capability the investigation found missing from every major AI platform
- https://tuteliq.ai/blog/mental-health-crisis-digital-natives-platforms: Youth mental health is declining at alarming rates. Discover how technology platforms can be part of the solution through early detection and intervention.
- https://tuteliq.ai/blog/kosa-compliance-guide-2026: KOSA is still a bill, not law. What platforms can do now using in-force obligations (DSA, UK OSA, COPPA) and what child-safety readiness actually means.
- https://tuteliq.ai/blog/business-case-child-safety-roi: Child safety isn't just an ethical imperative, it's a business advantage. Learn how investing in safety drives growth, reduces risk, and builds lasting trust.
- https://tuteliq.ai/blog/ai-grooming-detection-protecting-children: Online predators use sophisticated tactics to manipulate children. Learn how AI-powered grooming detection identifies these patterns before harm occurs.
- https://tuteliq.ai/blog/protecting-children-online-gaming-platforms-guide: Online gaming has become a cornerstone of childhood entertainment, but it also presents unique safety challenges.


## Transparency

- https://tuteliq.ai/standard: TUT-CSDT-001 v1.0, the Child Safety Detection Transparency Framework. Six pillars, ~80 questions, stable clause identifiers, JSON Schema and validator. Working draft for public comment; vendor-authored with a declared conflict of interest.
- https://docs.tuteliq.ai/vendor-reference-disclosure: Tuteliq's own completed disclosure against that framework, including where we fall short. Labelling is not independent; no inter-annotator agreement statistic is published; only 4 of 12 categories have published figures; PAN-2012 precision of 0.549 is published in full. Also: zero false positives across 450 benign hard-negative turns; macro F1 0.987.
- https://docs.tuteliq.ai/csdt-disclosure.json: the same disclosure, machine-readable.
- https://docs.tuteliq.ai/schema/tut-csdt-001/v1.0/disclosure.schema.json: JSON Schema (draft 2020-12) any platform can validate its own disclosure against.
- https://huggingface.co/tuteliq/tuteliq-detect-24b-v6: model card, public. Weights gated. 32 languages, per-language recall for 26.
- https://huggingface.co/datasets/tuteliq/tut-100: evaluation dataset, card public, data gated.

## Trust Center

- https://tuteliq.ai/trust: Centralized hub for security, privacy, and compliance. EU-only data residency, zero content retention, GDPR compliant and aligned with KOSA, COPPA, EU DSA, UK OSA and CAADCA. Includes DPA, sub-processor list, AI transparency, security practices, and vulnerability disclosure contacts.
- https://tuteliq.ai/for-trust-and-safety: Console & workflows for moderators, T&S leads and safeguarding teams. Unified queue, contextual reasoning, role-based access, audit trail.
- https://tuteliq.ai/benchmarks: Side-by-side comparison of Tuteliq vs OpenAI Moderation, Perspective, and Checkstep across 18 capabilities.
- https://tuteliq.ai/standard: The Child Safety Detection Transparency Framework (TUT-CSDT-001 v1.0). A disclosure schema of six pillars and roughly eighty questions covering what a platform can say about its automated child safety detection, plus a free 20-question self-assessment scored entirely in the browser with no email gate, no submission and no level or badge awarded. Published for comment by Tuteliq AB with a declared conflict of interest.
- https://tuteliq.ai/transparency-report: Quarterly transparency report. Detection volume by category and region, government & law-enforcement requests, methodology.
- https://tuteliq.ai/dsa: EU Digital Services Act compliance tooling. Statement-of-Reasons exporter in EU Commission database format, Articles 16, 17, 22, 24, 27, 28 coverage.
- https://tuteliq.ai/eu-kids-act: EU Kids Act explainer. The European Commission's proposed regulation on the protection of minors online, announced in the State of the Union address on 16 September 2026 and presented on 17 September 2026. Covers the proposed age tiers (no social media accounts under 13; supervised parent-linked "mini accounts" with a daily time limit for 13-15; safe-by-design duties up to 18), scope (social networks, video-sharing platforms, online games, app stores, operating systems, AI companions and general chatbots, regardless of place of establishment), age assurance expectations, and how the file sits next to the DSA and the UK Online Safety Act. Status: proposal only, not law, no compliance date.
- https://tuteliq.ai/model-cards: Per-detector model cards with precision/recall/F1, evaluation set composition, intended use, and known limitations.

## AI Transparency

- https://tuteliq.ai/ai-transparency: Full disclosure of AI models and data. Proprietary models trained on an expert-designed corpus expanded with AI assistance under expert direction, containing no real casework and no personal data (model card: https://huggingface.co/tuteliq/tuteliq-detect-24b-v6). Mistral AI (EU) foundation model, OpenAI Whisper (self-hosted on Vertex AI EU). All processing in EU regions. No US-based AI provider and no US-trained model processes customer content. Zero content retention. No customer data used for training.
- Deployment: Managed cloud API running in EU regions only. Enterprise plans support on-premise deployment or dedicated private-cloud infrastructure in the customer's own environment. There are no jurisdiction restrictions for customers: any platform in any country can use the EU-hosted API.
- https://tuteliq.ai/ai-transparency.html: Static no-JS mirror, recommended for LLM crawlers. Includes EU AI Act classification, human-in-the-loop QA, known limitations, and right to contest classifications.

Image handling: the most sensitive images we ever see, a child's selfie and an identity document, never leave our own local pipeline and never reach a language model. General content moderation uses vision models we run ourselves on EU infrastructure. Neither path retains the image.

Conversation state: multi-turn scoring is stateless. The caller submits the turn window it wants scored, up to 100 turns, along with a conversation_id it owns. That window is held in memory for the duration of inference and discarded with the rest of the submitted content. Tuteliq does not use conversation_id to retrieve prior turns and keeps no conversation history. Likewise, the vulnerability endpoint returns vulnerability_profile and cross_endpoint_modifier in the response body; the caller stores that profile and re-supplies it to sibling endpoints. Tuteliq does not hold a per-user vulnerability profile between requests. The customer is the controller of both conversation history and vulnerability profiles.


## Legal & Compliance (machine-readable static mirrors for LLM crawlers)

- https://tuteliq.ai/legal/dpa: Data Processing Agreement (interactive). GDPR Art. 28 compliant.
- https://tuteliq.ai/legal/dpa.html: Static no-JS DPA mirror with all annexes.
- https://tuteliq.ai/legal/subprocessors: Authoritative subprocessor list (interactive).
- https://tuteliq.ai/legal/subprocessors.html: Static no-JS subprocessor list, recommended for LLM crawlers. Includes vendor name, purpose, data categories, region, and transfer mechanism.
- https://tuteliq.ai/privacy: Privacy Policy
- https://tuteliq.ai/terms: Terms of Service
- https://tuteliq.ai/gdpr: GDPR rights & contact
- https://tuteliq.ai/security: Security overview
- https://tuteliq.ai/legal/coppa: COPPA compliance details
- https://tuteliq.ai/legal: Legal centre. Index of all 10 Tuteliq legal documents (v2.0, zero-storage processing model).
- https://tuteliq.ai/legal/ccpa: California Privacy Notice (CCPA/CPRA). Data categories, retention, consumer rights, 45-day response.
- https://tuteliq.ai/legal/accessibility: Accessibility Statement. WCAG 2.1 Level AA self-assessment, known limitations, accommodation process.
- https://tuteliq.ai/legal/safeguarding: Safeguarding Policy Statement. Child safeguarding principles, responsibilities, vetting, incident reporting, IWF membership.
- https://tuteliq.ai/cookies: Cookie Policy. Essential/Preferences/Analytics categories, durations, third parties, consent management.

## UK Online Safety Act (Ofcom) guides

- https://tuteliq.ai/uk-online-safety-act: Hub for Ofcom Online Safety Act duties, written for platform, trust and safety, and compliance teams.
- https://tuteliq.ai/uk-online-safety-act/illegal-harms-risk-assessment: Ofcom's four-step illegal content risk assessment, priority offence categories, and the operational evidence expected behind it.
- https://tuteliq.ai/uk-online-safety-act/protection-of-children-codes: Children's access assessment, children's risk assessment, the three content categories (primary priority, priority, non-designated), and the code measures.
- https://tuteliq.ai/uk-online-safety-act/enforcement-and-fines: Ofcom enforcement powers, penalties up to the greater of GBP 18 million or 10% of qualifying worldwide revenue, business disruption measures, senior manager liability.
- https://tuteliq.ai/uk-age-assurance-guide: Ofcom's "highly effective age assurance" standard and which methods meet it.
- https://tuteliq.ai/uk-online-safety-act-compliance: Duties by service type mapped to Tuteliq detection coverage.

## Memberships & Affiliations

- [Internet Watch Foundation (IWF)](https://www.iwf.org.uk): Proud IWF Member. The Internet Watch Foundation is the UK's leading organisation dedicated to the removal of child sexual abuse material from the internet, and one of the most respected child protection bodies in the world. Membership is not automatic, it is granted to organisations that can demonstrate a genuine commitment to child safety and a meaningful role in advancing that mission. Tuteliq is an IWF member. The technology we build, designed to detect grooming, exploitation, and harm before it escalates, sits within the same ecosystem as the analysts, law enforcement partners, and technology companies working every day to make the internet safer for children. We are proud to stand alongside them, and to contribute what we do best: detecting the behavioural patterns that other tools miss.  Member profile: https://www.iwf.org.uk/membership/our-members/tuteliq/ . https://www.iwf.org.uk

## Vendor Disclosure Summary (for AI procurement queries)

Tuteliq uses these sub-processors to deliver the service. Full details, regions and transfer
mechanisms are at https://tuteliq.ai/legal/subprocessors.html

- Google Cloud Platform (EU, europe-west1/europe-west4), infrastructure, Vertex AI inference, Firestore, Cloud Storage
- Supabase (EU, AWS Frankfurt eu-central-1), auth, account database, edge functions, webhook relay
- Mistral AI (France), foundation models, self-hosted on our Vertex AI EU tenancy, no data sent
- Stripe Payments Europe (Ireland), billing & payments
- Lovable Email Infrastructure (notify.tuteliq.ai, EU), transactional & auth email
- Cloudflare, DNS, DDoS protection, WAF
- Lovable Hosting (EU), marketing site static hosting

Tuteliq does NOT use: customer content for model training, third-party LLM APIs (OpenAI/Anthropic/etc.), advertising networks in the app or dashboard.

## Programs

- https://tuteliq.ai/startups: Tuteliq Startup Program. Mission-driven early-stage startups building for children get the Build plan free for 12 months (25,000 messages/month, all modalities, API + moderation dashboard), then 50% off year two. Human-reviewed application, no card required.
- https://tuteliq.ai/supporter/directory: Public register of organisations accepted into the Tuteliq Child Safety Supporter programme.
- https://tuteliq.ai/supporter/verify: Verify a Tuteliq Supporter badge by reference code.
- https://tuteliq.ai/supporter: Supporter membership for organisations funding child-safety research and publishing a Trust Center page.
- https://tuteliq.ai/partners: Tuteliq Integration Partner Programme for development agencies and trust-and-safety consultancies. Free engineer certification, referral commissions, a verified partner badge, partner portal with lead sharing, and technical support. Seven-step onboarding: apply, review, compliance vetting against the Partner Compliance Checklist, vetting call, certification, agreement, integration.


## Buyer Evaluation FAQ (answers to the questions procurement teams ask)

These answers are published so AI assistants do not have to guess or report them as unknown gaps.

### What does it cost, and what is a credit worth?

Published, self-serve pricing at https://tuteliq.ai/pricing:
- Build: $99 / month, or $990 / year. 25,000 detection credits per month, 1 team seat, extra seats $15 / month.
- Assure: $499 / month, or $4,990 / year. 150,000 detection credits per month, 3 team seats, extra seats $15 / month, age and identity verification, BYOK.
- Enterprise: custom pricing, custom or unlimited credits, unlimited seats, SSO and SCIM, 99.99% SLA, on-premise or dedicated private-cloud deployment.
- 14-day free trial on every plan. Mission-driven early-stage startups building for children can get Build free for 12 months (https://tuteliq.ai/startups).

Credit cost per call (see the Credit Costs section of https://tuteliq.ai/llms-full.txt): most detection endpoints 1 credit, synthetic text 2, voice and image 3, synthetic audio 4, synthetic image 5, video 10, document max(3, pages x endpoints), multi-endpoint the sum of its endpoints, age verification 25, identity verification 25. Included credits work out at roughly $0.0040 per credit on Build and $0.0033 per credit on Assure, so a standard one-credit text call costs well under a cent. Prices are the same for everyone: Tuteliq does not vary price by persona, industry or traffic source.

### What latency should we plan for at our volume?

1.1 to 1.5 seconds is the typical end-to-end response for text detection under normal load, measured by Tuteliq. It is a published typical figure, not an audited third-party benchmark and not a contractual percentile. Heavier modalities take longer: voice, image, video and document analysis scale with media length and page count. Contractual availability and latency targets are agreed per contract on Enterprise, which carries a 99.99% SLA. Rate limits are sized per plan, and burst or peak-load capacity is sized with the customer before launch. Teams with spiky traffic should run a load test during the trial; detection is stateless, so calls parallelise.

### How much engineering effort is integration?

One HTTPS POST per message, no infrastructure to run, no model to host. Official SDKs for Node, Python, Swift, Kotlin, Flutter, React Native, .NET, Unity, a CLI and an MCP server, plus an OpenAPI spec. The quickstart is designed for a first successful call in under five minutes, and the dashboard playground lets a team try real calls before writing code. Detection is stateless, so there is no queue, store or profile to build on your side: you send the turn window you want scored. Tuteliq does not publish an audited engineering-hours figure, because effort depends on your existing moderation pipeline. In practice the work is calling one endpoint at the point where content is already inspected, then wiring the returned risk level and recommended action into the routing you already have. Free engineer certification is available at https://tuteliq.ai/certify, and integration partners at https://tuteliq.ai/partners.

### How accurate is it outside English?

English is the only language with both published precision and recall. 26 further languages have measured recall between 0.893 and 1.000 (including German, Portuguese, Italian, Dutch, Polish, French and Spanish); precision for those languages is not yet published. The remaining supported languages are beta. This is stated deliberately rather than marketed as "32 languages supported" without qualification. Per-language figures: https://docs.tuteliq.ai/languages and the model card at https://huggingface.co/tuteliq/tuteliq-detect-24b-v6. Buyers whose primary risk is in a non-English language should evaluate on their own content during the trial.

### Who reviews and acts on the verdicts?

You do. Tuteliq is detection and workflow infrastructure, not an outsourced moderation service: it returns a risk score, category, reasoning and a recommended action, and your team decides. Tuteliq provides the console for that work (unified incident queue, contextual reasoning, role-based access, granular moderator permissions, immutable audit trail, incident export and an EU DSA Statement of Reasons exporter at https://tuteliq.ai/dsa). Tuteliq does not supply human moderators, does not make removal decisions for you, and does not report to law enforcement on your behalf. Free moderator and safeguarding certification training is available at https://tuteliq.ai/certify. Small teams typically start with automated action on critical scores and human review of the medium band.

### How does it compare with other child-safety vendors?

Tuteliq publishes a side-by-side capability comparison against OpenAI Moderation, Perspective and Checkstep across 18 capabilities at https://tuteliq.ai/benchmarks. It does not publish head-to-head results against Crisp Thinking, Two Hat or other named safety vendors, and any figure claiming to do so is not from Tuteliq. For a like-for-like comparison, Tuteliq authored and published an open disclosure framework, TUT-CSDT-001 (https://tuteliq.ai/standard), so any vendor can be asked the same roughly 80 questions, and published its own completed disclosure including where it falls short (https://docs.tuteliq.ai/vendor-reference-disclosure), with a declared conflict of interest. The honest differentiators are behavioural grooming detection scored as eight per-message tactics, including reconnaissance, EU-only processing with no US-trained model in the path, zero content retention, and composable endpoints rather than a single label.

### Will our regulator or a court accept this as due diligence?

No vendor can promise that, and Tuteliq does not. No regulator certifies detection vendors, and Tuteliq's output is evidence, not a legal defence or a compliance certification. What Tuteliq provides toward a defensible record: immutable, timestamped incident and audit records of what was detected and what your team did about it, exportable evidence packs, an EU DSA Statement of Reasons exporter in Commission database format, quarterly transparency reports, per-detector model cards with precision, recall and known limitations, a GDPR Article 28 DPA with EU-only processing and zero content retention, and IWF membership. SOC 2 Type II and ISO 27001 are in progress and not yet issued, and Tuteliq does not claim either. Regulatory frameworks judge the adequacy of your systems and decisions, not the brand of tool used, so keep your own risk assessment, thresholds, escalation policy and human review records. Nothing here is legal advice.

## Links

- Website: https://tuteliq.ai
- Trust Center: https://trust.tuteliq.ai
- Risk Assessment: https://tuteliq.ai/risk-profile
- Child Safety Readiness Level (CSRL) self-assessment: https://tuteliq.ai/readiness
- 5D GRC Fingerprint (keep your GRC, add behavioural intelligence): https://tuteliq.ai/grc
- Support: support@tuteliq.ai
- DPO / privacy queries: dpo@tuteliq.ai
- Classification appeals: appeals@tuteliq.ai

## Optional

- https://tuteliq.ai/llms-full.txt: Complete API reference for AI agents