Generative AI services involve building, training, fine-tuning, and evaluating models that can produce human-like content, from text and images to voice, video, and code. These services are essential for creating smarter digital assistants, content automation tools, and enterprise copilots across industries such as healthcare, legal, education, and e-commerce. At DDD, we power these services with high-quality datasets, ethical oversight, and scalable infrastructure.

Our approach centers on data excellence, ethical oversight, and platform flexibility. We bring:

• 500M+ data points labeled with P95 quality.
  

• SME-driven domain expertise.
  

• Support for LLMs, multimodal models, RAG pipelines, and RLHF.
  

• 91% pilot-to-production success rate.
  

We help clients build safe, scalable, and high-performance AI solutions, from concept to deployment.

Red Teaming is a proactive technique to stress-test Gen AI models for vulnerabilities by simulating adversarial or unsafe scenarios. Our Red Teaming Services include:

• Toxicity, bias, and hallucination testing.
  

• Security exploit simulations (e.g., jailbreak attempts).
  

• Prompt injection defense.
  

• Compliance verification and multimodal risk evaluation.
  

This ensures your model meets the highest standards of safety, ethics, and regulatory compliance before deployment.

RAG combines LLMs with a retrieval system to enhance factual accuracy by grounding responses in external knowledge. We support:

• RAG pipeline setup for domains like legal, healthcare, and customer support.
  

• RAG fine-tuning with verified, structured data to reduce hallucinations.
  

• Multilingual and domain-specific tuning to improve precision.
  

Whether you're building enterprise knowledge assistants or research copilots, we ensure your RAG systems are trustworthy and scalable.

Yes. Our fine-tuning services include:

• Domain specialization (e.g., finance, healthcare, legal).
  

• Task optimization (e.g., summarization, translation, code generation).
  

• Bias/safety alignment and hallucination reduction.
  

• Multilingual and personalization training.
  

We also annotate synthetic data and user feedback for continuous model improvement.

Absolutely. RLHF is a method where human judgments guide a model’s behavior through reinforcement learning. At DDD, we:

• Collect and rank human feedback across use cases (e.g., tone, factuality, empathy).
  

• Power RLHF pipelines with expert-labeled data and user preference scoring.
  

• Apply RLHF for chatbots, content creation, safety alignment, and personalized tutoring systems.
  

This ensures models align more closely with human expectations and values.

Yes. We collect, annotate, and evaluate text, image, video, and audio data for training multimodal models. We also:

• Generate and label synthetic data for model training and augmentation.
  

• Support multimodal red teaming for visual misinformation and deepfake detection.
  

Our capabilities cover models like GPT-4o, Gemini, and Sora.