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20X faster data labelling and annotation

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Tekbod AI

Domain Experts & Generalists for All Industries

Tekbod AI focuses on RLHF and Multimodal Labelling & Annotation, ensuring your models and automations run as expected. We bring human-in-the-loop to eliminate errors in your data. We vet, recruit, rigorously train and test our Domain Experts and Generalists to prepare them for all AI projects. We have a big team of experts and generalists in different industries, ensuring that we meet any project requirements.

  • Highly-trained Domain Experts and Generalists
  • 98%+ quality and satisfaction rate
  • 100% confidentiality guarantee

Why Tekbod AI

Tekbod AI

About Tekbod AI

Tekbod AI focuses on Multimodal Annotation & RLHF, ensuring your models and automations run as expected. We bring human-in-the-loop experience to eliminate errors in your data. We vet, recruit, rigorously train and test our Domain Experts and Generalists to prepare them for all AI data projects. We have a big team of specialists and generalists in different industries, ensuring that we meet all project requirements.

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Our Faq

Explore Our Informative FAQ Section

We have 0% tolerance for any project information leaks. This is one of the points of emphasis during the training period of our specialists and generalists. We assure you that your project details will not leak to any third parties.

We sign NDA contracts with our specialists and generalists, and we keep remininding them about this serious policy

At Tekbod AI, we combat Annotator Drift using "Gold-Standard Insertion." We periodically inject "control" data (pre-verified samples) into our workers' queues without their knowledge. If an annotator's performance on these control samples dips below the threshold, they are automatically flagged for recalibration. This ensures that the "Helpfulness" and "Safety" standards we set on Day 1 are identical to those on Day 100.

Absolutely. We don't just act as a "passive" labeling force. Through our Active Discovery Workflow, our senior annotators are trained to tag "Edge-Case Ambiguity." If our team encounters a data point where the current guidelines are insufficient or the model's output is strangely inconsistent, we flag it as a "Boundary Case." We then provide you with a weekly Strategic Edge-Case Report, helping your research team identify where the model's reasoning is brittle, which often informs the next iteration of your architecture or training set.

We are always here and ready to respond to any issues that you may have pertaining to your project quality and solve it as soon as possible. Just reach us via any of the available channels that is convenient for you and we will sort the issue immediately

You can reach us via any of our contacts or chat and we will discuss your project.