Halt energy theft

Structure billing and usage data with our entity tagging and NLP workflows and build models that will flag suspicious anomalies between the two.



Automated energy-theft pilots have already demonstrated 65% accuracy in identifying potential energy fraud.



Eliminating energy theft could save utilities providers $100 billion per year.

Named-entity tagging

Train models to extract key elements of billing and usage data.

Semantic annotation

Enable models to Identify and categorizing text based on subject and intent.

Enhance or introduce predictive maintenance techniques

We teach models to process a wide array of input types so they can monitor signs of critical assets and flag small issues before they snowball into critical failures.


cost reduction

Robust predictive maintenance procedures have been shown to reduce overall operating costs by 1/5.


downtime reduction

A study by McKinsey found that predictive maintenance procedures could cut the average provider’s annual downtime in half.

Scripted speech collection & validation

Train models to listen for anomalies in audible frequencies.

Multimodal data collection

Enable models to process a large range of data types to understand overall asset health.

Image collection & validation w/ correction

Teach models to visually identify small signs of minor malfunctions.

Reduce customer service costs with our end-to-end chatbot

Expand the capabilities of your existing chatbot, or choose our end-to-end solution and let our team build one for you.



For routine inquiries, chatbots can reduce time-to-resolution from hours to just minutes.


routine inquiries

Automating routine interactions lets your trained service agents solve more complex issues.

Named-entity tagging

Train your chatbot to identify and act upon industry-specific keywords.

Text collection w/ correction

Teach your bot to understand the myriad of nuances in how different people might express the same idea or intent.

Speech transcription

Build the foundations for voice capabilities with speech-to-text transcription.

Success stories

A visionary Fortune 500 Tech company leveraged sentiment analysis models to dig beyond surface-level understandings to extract granular-level insights.

Smart companies see the pile of unstructured text floating through the digital realm as a strategic goldmine of consumer insights.

Mastercard’s R&D Labs needed unique, multi-lingual text data that covered 20 designated payment scenarios in English and Spanish, and they needed it fast.

Keeping a nation’s lights on means constantly inspecting electricity poles for damage. EDP partnered with DefinedCrowd to improve Asset Performance Management processes.

A global electronics maker came to DefinedCrowd with the goal of building more inclusive facial recognition models, requiring accurately annotated images with highly specific criteria.

A Fortune 500 Tech company needed comprehensive speech training data in French that accounted for a wide range of dialects, requiring diverse data in terms of age, gender and regional dialects.