Data labelling
by data scientists

Take the weight of building datasets off your data
team, so you can focus on the high-impact work.

Balance scale & quality

Utilise the scale of a distributed crowd without the risk to quality

Highly configurable

Design and deploy workflows focused on your specific problem

Solve the tough problems

Data doesn’t always fit into neat boxes, that’s where we come in

Proven solutions

We lean on 10 years of experience for world leading research

Build your models with data training

Move past the pain of manual labelling by harnessing our distributed crowd, supported by more than 10 years' experience of running the world's largest citizen science platform.

  • Precise, custom focus
  • Robust process paths
  • Flexible, infinite scaling
  • Blended human input

Model output validation at scale

Test and improve your data models with the highest quality human validation at scale. We help you monitor your deployed models at a greater speed and lower cost than other options.

  • Assess model suitability
  • Increase performance
  • Understand issues
  • Reduce assumptions

Understand the ground truth

Gather data from direct observation and quickly synthesize it to gain powerful insight.

  • Create strong foundations
  • Scale human validation
  • Handle subjective content
  • Gain confidence in outputs

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
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
Derek Langley - Product Line Design Authority @ Thales
Trusted by data teams at
  • Thales
  • University of Oxford
  • Nesta
  • Amplyfi
  • Codemill
  • Geospatial Insight
  • Hummingbird
  • Satellite Catapult

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.