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Deep dive into the latest technologies and methodologies that are shaping the future of autonomous driving.
AI + Human
Empowering Innovation with
Generative AI & Data Precision
30M+
Data Point Labeled
500K+
Miles Mapped
2000+
Data Labelers
Digital Divide Data Generative AI Solutions
Focus on innovation with our comprehensive solutions to streamline your process,
and empower generative AI models.
Prompt Engineering and Dataset Generation
Data Enrichment: Identify and fill gaps in your datasets to create a richer, more comprehensive foundation for your AI models.
Prompt Engineering: We integrate synthetic text or image data tailored to your specific needs for fine-tuning your AI models.
Bias Mitigation: Our synthesized data helps tackle data sparsity, class imbalance, and bias to ensure your training data accurately represents the real world with unbiased AI models.
Red Teaming Solutions for Generative AI
Crafting Malicious Queries: We create specially designed queries intended to exploit potential weaknesses in your models to uncover vulnerabilities.
Exposing Biases and Fairness Issues: Our red teaming exercises can unearth biases by feeding prompts that target potential blind spots to ensure fair and responsible AI development.
Developing Mitigating
Strategies: Once vulnerabilities are identified, we collaborate with you to develop effective mitigation strategies and implement additional safety filters.
Exposing Compliance
Risks: Our team can simulate scenarios that test your model's ability to detect and prevent illegal activities.
Reinforcement Learning for Gen AI
Reinforcement Learning From Human Feedback (RLHF): We utilize human-in-the-loop feedback and AI outputs to align your models with real-world expectations.
Model Output Evaluation: We ensure outputs align with real-world scenarios, identifying and correcting logical inconsistencies to refine your generative models.
Fine-Tuning for Precision: Fine-tune pre-trained models to excel on specific tasks or adapt them to new domains for maximizing effectiveness.
Tackling Edge Cases & Hallucinations: We proactively identify and address edge cases and unrealistic model outputs such as hallucinations.
Quality Control For Gen AI Performance
Large Language Models (LLMs)
We utilize automated tools and human expertise to review, score, and correct chat prompts and outputs for the highest accuracy.
Large Vision Models (LVMs)
Our team meticulously reviews and rectifies image segmentation outputs to effectively identify and classify edge cases, leading to improved data quality.
Foundation Models
Our partners benefit from comprehensive validation, performance monitoring, and adherence to ethical guidelines for foundational models.
Build Superior AI Models with Superior Data Quality
Prompt Engineering
Data Curation and Labeling
Annotation for Gen AI
DPO and RLHF
Audit and Quality Control
Our data experts curate, generate, annotate, and evaluate custom datasets, ensuring the highest quality for your generative AI applications.
Security
At Digital Divide Data (DDD) confidentiality and security is our top priority. We are deeply committed to upholding trust with our clients and safeguarding their valuable and confidential information with the highest security standards.
Industries We Serve
Autonomous Driving
Legal
Government
Healthcare
Financial service
Content Services
Technology
Retail
What Our Clients Say
Reliable Data Annotaion for Autonomous Driving System Demands a Disciplined Methodology
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