At Human Lambdas we have been busy building out the first version of our platform and iterating on user feedback. After quietly launching our Beta and opening up for sign ups, we are now thrilled to officially announce the release of the Human Lambdas Public Beta.

This article enumerates a series of recommendations that will enable you to successfully transition a process to a BPO, based on our expertise in the field.

Whether you have heard the words Human Review, Manual Review or Content Review: it all means the same thing. It is a process conducted by a person or a team of people which involves evaluating, editing and approving, or declining certain instances of data.

This guide compiles advice on how to implement QA in your processes, with a focus on measuring quality. In it, we elaborate on the different techniques that can be employed to measure decision accuracy and rep consensus, as well as evaluating the different trade-offs therein.

This article shows how we created a manual approvals process for Teacher Finder applicants with No Code tools. This is achieved by combining the power of Zapier with Human Lambdas, which we illustrate step by step.

In this article we describe a framework to systematically improve the performance of your routine processes. We draw inspiration from the OODA loop used in Military strategy to propose a 3 step process of understanding, improving and operating one's processes to drive KPI improvements.

If you are managing an Operations team engaging in routine manual workflows, whether that is in the context of a Human in the Loop system or a simple completely manual process, you are probably wondering how you can improve your team's performance. To do that, you need to define metrics that reflect your team's performance. Good metrics will help you easily understand where your team is at and how they're trending.

Human In The Loop (HITL) is traditionally defined as "a model that requires human interaction". Specifically, it implies a hybrid computation model whereby a human can intervene to overrule decisions taken by a machine where they are less likely to be correct. This is determined via the machine's confidence in every decision it makes.

Whether you're a social network or a marketplace looking into implementing moderation, you will most likely have to design an effective policing system that takes different enforcement actions depending on the severity of each detected instance of bad behaviour. In this post we propose a framework to help navigate the process of designing an effective, consistent and fair tiered enforcement strategy for moderation teams to apply against violating behaviour.

As companies ramp up their moderation efforts, they can implement different strategies to scale their efforts and improve their efficacy. Those strategies tend to combine human and machine capabilities to deliver best results.