Data validation

High quality, rapid
speed model validation

Test and improve your data models with the highest quality human validation at scale

Assess suitability

Quickly validate if a model will add value to your data

Understand issues

Gain insight into exactly where and how your model is failing

Increase performance

Utilise high quality human decisions to increase model performance

Reduce assumptions

Quickly course correct and prevent complex, layered assumptions

Model performance is only
as good as its validation

Proper training is essential to good data models, and human validation is still the best way to do it. It helps artificial intelligence effectively replicate human behaviour and prevents the complex, layered assumptions created when models train other models.

However, human validation is too often expensive and difficult to scale. The greater scale, the more likelihood of false information introducing errors into the foundations of your algorithm. 1715 Labs provide thoroughly tested human validation for supervised learning.

A virtuous cycle of
active learning

01. Initial training & input

After initial training, we feed the model specific examples

02. Human observation

The model’s decisions are marked as correct/incorrect

03. Re-training

Focusing on errors, the model is retrained with correct decisions

04. Testing

The model is fed both the initial and new data to monitor improvement

One solution,
all your data types

Image detection

Detect objects and add context to analyse imagery

Shape recognition

Detect and categorise shapes and patterns in imagery

Video enhancement

Categorise, label and annotate video in real time

Boundary & route analysis

Understand and map out boundaries and routes

Scale your labelling with no compromise on quality

A core challenge of data labelling is scaling your team. While small pools of workers can’t handle large datasets and increase the risk of individual error, large pools are hard to manage and likely to be less focused or knowledgable.

We have a ten year track record of using existing pools to take the weight of crowd management off your shoulders of world class companies. Participant input is statistically weighted based on their track record to combining the results of many workers for any given task and ensure the highest quality results.

Focus on unique problems with high level configuration

We provide well-tested solutions that can be rapidly implemented for more straight forward tasks, helping you focus on the more important and high impact work.

For more difficult tasks, unknown problems or new areas of research, our flexible internal toolkit and extensive experience in crowd labelling allows us to design and deploy workflows which focus crowd effort onto your specific problem. No more generic ‘place a bounding box’ results.

Labelled data from 1715 Labs helped our model improve robustness and consistency on real world noisy documents

Lorenzo Bongiovanni - Lead Machine Learning Scientist @ Amplyfi

1715 Labs' human-led approach unlocks hard to reach value in complex datasets

Derek Langley - Product Line Design Authority @ Thales
Trusted by data teams at

Contact us to
get your AI out of the lab

We'll guide you through the best solution and implementations to achieve your data goal and make the most of your artificial intelligence.