Service
Architecture review before implementation
Implementation scope and rollout planning
Clear next-step recommendation
Integrate real-time, multimodal deepfake detection APIs to verify video, audio, and image authenticity, protecting brand reputation and preventing fraud.
Deepfakes are no longer a theoretical threat. They are a direct attack on enterprise trust, enabling fraud, misinformation, and reputational damage. We integrate real-time detection APIs that analyze video, audio, and images for synthetic manipulation, delivering >99.5% accuracy with sub-200ms latency.
Our integration service delivers:
This service is a core component of a comprehensive Digital Provenance and Disinformation Security strategy. It works in concert with our AI Watermarking and Fingerprinting Development for proactive content signing and Enterprise Disinformation Defense Architecture for holistic threat monitoring.
Outcome: Move from reactive damage control to proactive verification. Enable secure user onboarding, protect executive communications, and ensure the integrity of all public-facing media with a scalable, API-first defense layer.
Integrate real-time, multimodal deepfake detection to protect your brand, secure communications, and prevent fraud. Our API delivers measurable security and compliance outcomes.
Automatically scan and flag synthetic media impersonating your brand across social platforms and digital channels. Prevent reputational damage and financial fraud from sophisticated deepfake attacks.
Integrate verification into video conferencing, internal comms, and executive communications. Ensure the authenticity of video and audio in sensitive negotiations and corporate announcements.
Meet regulatory requirements for digital evidence and communications under frameworks like the EU AI Act. Maintain a verifiable audit trail of media authenticity for legal defensibility.
Prevent CEO fraud, synthetic identity scams, and authorized push payment fraud driven by deepfake audio and video. Direct integration into transaction workflows blocks high-risk interactions.
Rapidly identify and assess deepfake incidents with detailed forensic reports. Our API provides confidence scores, tamper indicators, and provenance data to accelerate your security team's response.
Deploy with minimal disruption using our well-documented REST APIs and SDKs for major platforms. We handle the complex model orchestration, allowing your team to focus on core business logic.
A clear breakdown of project phases, deliverables, and timelines for integrating our enterprise-grade Deepfake Detection API, ensuring alignment with your security and compliance goals.
| Phase & Key Activities | Timeline | Starter | Professional | Enterprise |
|---|---|---|---|---|
Initial Scoping & API Key Provisioning | Week 1 | |||
Core API Integration & Sandbox Testing | Weeks 2-3 | |||
Multimodal Detection (Video + Audio + Image) | Weeks 3-4 | |||
Custom Model Fine-Tuning on Proprietary Data | Weeks 4-6 | |||
Real-time Streaming Integration & Latency Optimization (< 500ms P99) | Weeks 5-6 | |||
Security Audit & Penetration Testing (MITRE ATLAS Framework) | Week 7 | |||
Full Production Deployment & Load Testing | Week 8 | |||
Ongoing Support & Model Updates | Post-Launch | Priority SLA | Dedicated Engineer | |
Integration with Other Services (e.g., AI Watermarking, Disinformation Defense) | Optional Add-on | |||
Typical Project Duration | 8 weeks | 8-10 weeks | 10-12 weeks |
Our deepfake detection API is engineered to address the unique integrity challenges and compliance requirements of high-stakes industries. We deliver domain-specific models and workflows that integrate directly into your operational stack.
Enabling Efficiency, Speed & Accuracy
We build AI systems for teams that need search across company data, workflow automation across tools, or AI features inside products and internal software.
Answers to common questions about integrating real-time deepfake detection into your enterprise platforms, covering timelines, security, and support.
Standard integration projects are completed in 2-4 weeks. This includes API configuration, model fine-tuning on your data, and end-to-end testing. Complex multi-platform deployments or custom model training may extend to 6-8 weeks. We provide a detailed project plan within the first 3 days of engagement.
About the author
CEO & MD, Inference Systems
Prasad Kumkar is the CEO & MD of Inference Systems and writes about AI systems architecture, LLM infrastructure, model serving, evaluation, and production deployment. Over 5+ years, he has worked across computer vision models, L5 autonomous vehicle systems, and LLM research, with a focus on taking complex AI ideas into real-world engineering systems.
His work and writing cover AI systems, large language models, AI agents, multimodal systems, autonomous systems, inference optimization, RAG, evaluation, and production AI engineering.
How We Work
One-fit-all AI don't work for modern businesses. At Inferensys, we aim to understand your business & custom requirements; which we use to define most efficient agentic workflows, the data, and the tools for your business.
The first call is a practical review of your use case and the right next step.
