Industries · CleanTech
Structured data for climate and recycling AI
Sensor events from smart recycling bins labeled every week, and emission activities from many customers standardized, ranked, and classified for country-specific carbon rules.
Overview
Recurring labeling for recycling sensors and emissions data
Climate and recycling companies are using AI for work that used to depend on manual counting and spreadsheets: identifying what goes into a recycling bin, tracking where emissions come from, and turning both into numbers that municipalities, customers, and regulators can act on. The data behind that work is unusual. Smart recycling bins produce sensor readings, not photos. Carbon platforms receive activity descriptions from many customers, each written in its own format and vocabulary.
Both kinds of data share one problem: they never stop arriving. A sensor network produces new events every week, and a model that isn’t retrained on them falls behind. A carbon platform keeps adding customers, industries, and countries, and each brings activities that have to be classified correctly against international and country-specific frameworks. Labeling done once, as a project, doesn’t fit either case. It has to run on a schedule, with versioning so that rejected and corrected data is never lost.
MLtwist runs cleantech labeling as a recurring pipeline. Data is ingested and prepared as it arrives, labelers work in interfaces built for the signal in front of them — including a custom view that shows acoustic sensor events visually — and automated JSON quality control flags anomalies before your team has to go looking for them. For emissions data, a workforce trained in emissions categorization standardizes, ranks, and classifies activities, with multi-layer review to keep results consistent across industries and regions.
Bring MLtwist in when your labeling volume grows every week, when your inputs are too inconsistent to compare, or when your classifications need to hold up for regulatory reporting. Candam Technologies uses it to keep its recycling models current. A carbon-reporting platform used it to build a ranked, classified database of emission activities that its customers act on country by country.
The problem
What makes cleantech data hard
Weekly volume
New sensor data arrives every week, and a model that isn't retrained on it falls out of date. Labeling that volume by hand is slow and labor-intensive, and it pulls your own team away from the models themselves.
Inconsistent inputs
Every customer reports activities in its own format, terminology, and level of detail. Until they're normalized, nothing compares cleanly across customers, industries, or regions, and a database built on them can't be trusted.
Regulatory stakes
Emission classifications must line up with international and country-specific carbon accounting frameworks. A wrong class isn't just a model error — it can change what a customer reports and which reductions it goes after first.
How it works
Two kinds of climate data, one process
Recycling sensors and emissions reports look nothing alike, but both need consistent labels on a schedule.
- 01
Ingest on a schedule
New sensor files or customer activity data are pulled in and prepared as they arrive. The same automated preprocessing runs on every batch, and the pipeline scales up as volumes grow without interrupting deliveries.
- 02
Show labelers the signal
Sensor readings are hard to judge as raw numbers. A custom interface shows acoustic events visually, so labelers can segment each recycling event into its slide, impact, and material, and check the label against what they see.
- 03
Standardize and classify
For emissions data, specialists trained in emissions categorization normalize activity descriptions into one structure, rank activities from highest to lowest impact, and assign each the class your framework requires. Multi-layer review keeps results consistent across industries and regions.
- 04
Check automatically
Automated JSON QC detects and reports anomalies on every delivery. Your team gets what it needs to make quality calls quickly, instead of hunting for problems by hand.
- 05
Track every version
Data is rejected, approved, renamed, and changed throughout its life. Versioning follows every file through those changes, so you can roll back to an earlier state or merge forward when you need to.
Case study · Candam Technologies
Recycling-bin sensor data, labeled and returned every day
Candam Technologies, a Barcelona cleantech company, makes RecySmart, a recycling bin with sensors that identify the type and quantity of waste deposited. Its models need new labeled data every week, and each recycling event — the slide down the ramp, the impact, the material — has to be segmented precisely.
MLtwist set up an automated pipeline that ingests and prepares the weekly data, a custom interface where labelers segment acoustic events visually, automated JSON quality control, and versioning that follows data as it's rejected, approved, and renamed. Existing tools couldn't handle a dataset that grew every week; this one was built to.
Validated datasets now come back on a daily delivery schedule, and the pipeline handles large weekly datasets without delays as volumes grow. Candam's internal QA team has 3× more time to review data quality, and reliable labeled data doubled the time the company can spend on AI itself, improving waste identification accuracy.
- more time for Candam's QA team to review quality
- 3×
What you get
Labeled data on your retraining schedule
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Daily or weekly deliveries
Validated datasets are returned on a schedule that matches your model retraining. For Candam, that's a daily delivery schedule.
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Segmented sensor events
Acoustic events are split into slides, impacts, and materials. Each segment is labeled precisely enough to train material identification.
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Ranked, classified activities
Emission activities are ranked from highest to lowest impact and mapped to international and country-specific frameworks. Customers can go after the biggest reduction opportunities first.
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Anomaly reports
Automated QC results arrive with every delivery. Problems are flagged for a decision, not buried in the data.
More work
Related programs
FAQ
Common questions
Can you label sensor data that isn't images or text?
Yes. For Candam, labelers worked in a custom interface that shows acoustic sensor events visually, so they could segment and label each part of a recycling event. As Candam's technology director put it, having both the visual representation and the data is essential to verify label quality.
Do your reviewers know carbon accounting?
MLtwist staffs reviewers trained in emissions categorization and environmental data handling. They rank and classify activities against international and country-specific carbon accounting frameworks, with multi-layer review for consistency.
Can you keep up with weekly data?
Yes. Candam's data is processed every week and returned on a daily delivery schedule, and the pipeline scaled up quickly as the dataset grew, with no delays or interruption in service.
Can you combine activity data from many customers?
Yes. For a carbon-reporting platform, MLtwist ingested activity data reported in many formats and standardized it into one structure. The company also gained a repeatable process for bringing new customer data into its platform.
What if labels need to change later?
Versioning tracks every rejected, approved, and renamed file, so you can roll back or merge forward. That matters when a model has to be retrained on a corrected set, or when you need to show what changed and when.
Bring us your cleantech data
Tell us the data type, volume, and timeline. We'll scope it with our team, yours, or both — and deliver it versioned, in your format.
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