Human-in-the-loop AI
Elevate AI accuracy with reinforcement learning with human feedback
Empowering AI with human expertise
At SynthVision, we leverage Reinforcement Learning from Human Feedback (RLHF) to enhance AI decision-making with real-time human expertise. Our structured multi-tiered review flows ensure precision, scalability, and cost efficiency—tailored to your needs.
Human-in-the-loop AI
Elevate AI accuracy with human expertise
Bridging the gap between AI and real-world complexity
AI models often struggle with edge cases—those unpredictable, unfamiliar scenarios that can impact performance. At Synthvision, we ensure your AI makes the right decisions by introducing the human-in-the-loop concept.
Real-time edge case monitoring
Refining AI, one edge case at a time
AI models often struggle with unpredictable scenarios. Our human-in-the-loop edge case monitoring system ensures real-time intervention, reducing model failures and enhancing decision-making. With expert validation at critical points, we transform edge cases into learning opportunities—making AI smarter, faster, and more reliable.
STEP 1
Detection
When AI encounters an ambiguous or unfamiliar scenario, it flags the data and sends it to our system via API. This ensures that potential failures are identified before they impact real-world performance.
STEP 2
Resolution
Expert annotators analyze the flagged edge cases, providing accurate resolutions in seconds. Their insights help fine-tune AI decision-making, ensuring models adapt to complex real-world conditions.
STEP 3
Optimization
The corrected data is fed back into the AI model via API, continuously refining its accuracy. Over time, the model learns from expert feedback, reducing the occurrence of similar edge cases and improving overall performance.
Precision at every level
Multi-tier quality control for AI accuracy
To ensure flawless annotations and highly accurate AI training data, Scematics employs a structured, multi-tier quality control system. Our expert-led validation process eliminates errors, refines datasets, and enhances model performance at every stage.
Quality analyst
The first line of defense in edge case monitoring. Quality Analysts evaluate AI predictions and ensure accuracy before advancing.
Offline auditor
A deep-dive specialist who conducts in-depth offline assessments of reviewer's judgments to ensure quality compliance.
Online auditor
A real-time accuracy enforcer who reviews decisions on the spot, ensuring only the highest-quality annotations.
Optimize AI validation
Precision in every decision
AI alone can’t always make the right call. Our system ensures human oversight at critical moments, allowing you to fine-tune how edge cases are reviewed, resolved, and integrated back into your AI pipeline.
- Set consensus thresholdsSet precise agreement rules for expert validation. Choose whether decisions require unanimous approval, a majority vote, or a custom-defined threshold. This ensures only high-confidence resolutions are sent back to refine AI performance.
- Plug in your modelEnhance decision-making by incorporating your AI model into the validation process. Compare model predictions against human expert resolutions to fine-tune performance and continuously improve accuracy.
- Seamless deploymentEffortlessly integrate our edge case monitoring system into your workflow via REST API or a lightweight Python SDK. Whether handling images, videos, or other vision data, setup is fast, scalable, and built for real-time AI improvement.
Experience our innovative approach to partnership and service delivery
Maximizing AI potential
Faster, Smarter, Cost-Effective AI
Unlock the full potential of your AI with a streamlined edge case monitoring system. Our approach reduces development time, enhances model performance, and ensures cost-effective scalability—all while delivering a seamless AI experience.
Accelerated AI deployment
Bring AI solutions to market faster with a structured validation process that enhances model accuracy and reliability from day one.
Reduced iteration cycles
Minimize development bottlenecks with continuous refinements, improving model efficiency and reducing the time required for retraining.
Cost efficiency
Optimize resources and cut operational costs without compromising on precision, ensuring a sustainable AI pipeline.
Enhanced satisfaction
Deliver more consistent, high-performing AI solutions that meet customer expectations and drive business success.