AI data collection services for models you can trust Damco gathers the text, images, audio, and video your models learn from
Data collection for AI, by the kind of data Gathered to the spread your model needs, then checked before delivery
How our AI data collection framework works Five steps, from your model's goal to a dataset ready to train
- Week 1Scope the projectWe agree what the model must do and the spread of data it needs.
- Week 1Estimate the resourcesWe set the sources, the contributors, the tools, and the timeline.
- Each batchCollect the dataAgents gather from approved sources, and contributors record what's missing.
- Each batchControl the qualityA person reviews every item the checks flag and fixes the gaps.
- Each batchDeliver the datasetOne named batch owner signs off the batch and its spread report.
When to outsource AI training data collection And when you need annotation of data you already hold
A good fit when
- Your model fails on accents, lighting, or cases your data doesn't cover
- You need consented recordings from people you can't reach yourself
- You want public web content gathered and filtered before training
Look elsewhere when
- You already have the raw data and only need it labeled
- A public benchmark dataset already covers your use case
- You need personal data nobody has agreed to share
AI data collection services in detail What each kind of collection covers, and how we keep the data usable
Text data collection for NLP and LLM models
Language models need text that sounds like your users: support tickets, product questions, contracts, and chat logs in the languages you serve. We gather public text, write new examples to your guide, and remove duplicates and personal details.
Each item carries its source, language, and topic, so you can balance the dataset before training.
Image data collection for computer vision
A vision model is only as good as the scenes it has seen. We collect photos across the lighting, angles, backgrounds, and devices your model will meet, from approved web sources and from contributors who shoot to a brief.
If your model needs to spot a damaged parcel, we make sure it sees damaged parcels in a warehouse, on a doorstep, and in the rain.
Video data collection
We gather footage of real scenes and actions, such as people walking, vehicles turning, or tools in use. Each clip is cut, logged by event, and checked for consent and quality before it reaches you.
Audio and speech data collection
Speech recognition fails on the voices it never heard. We record voice commands, conversations, and read sentences from contributors across accents, ages, and noise levels, with a transcript and speaker details for each clip.
Methods: web, crowd, on-site, and device data
We pick the method that fits the data. Public web content is gathered by agents and filtered by people. Contributors record speech, photos, and text to a brief from wherever they are.
- Web collection from sources you approve
- Remote contributors working to a written brief
- On-site collection in real environments, such as stores or plants
- Readings taken from sensors and edge devices
AI data collection for every industry
Each industry's models fail in their own way, so each dataset is built around the cases that matter there.
- Photos of defects and machine readings for manufacturing models
- Property photos and records for real estate valuation models
- Road and warehouse scenes in every weather for autonomous systems
- Documents and transactions for fraud and loan models in finance
- Product images, reviews, and questions for retail search and recommendations
- In-cabin audio, driving footage, and sensor data for automotive models
- Speech in many languages for media subtitles and search
- Meter and equipment data for energy forecasting and early warnings
Challenges in collecting data for AI training
The usual problems are gaps, bias, and consent. A dataset can look large and still miss the cases that matter, so we report its spread by accent, setting, or category with every batch.
Every recording from a contributor comes with their consent, and we drop any item whose source or consent we can't confirm.
Questions about AI data collection services What buyers ask us before they start
What types of data are collected for AI training?
Images for computer vision, text for language models, audio for speech recognition, and video for activity detection. Sensor readings, transaction records, and usage logs train models too. We collect all of these and deliver each with the metadata your training pipeline needs.
What tools are used for data collection for AI and ML?
Agents and scrapers gather public web content, and collection apps let contributors record speech, photos, and text to a brief. Validation checks test every item for format, quality, and duplicates. A person reviews whatever the checks flag.
How long does it take to collect datasets for AI training?
It depends on the size of the dataset and how hard the data is to find. A dataset from public sources can be ready in a few weeks. A dataset recorded by contributors across many accents or settings takes longer, so we deliver it in batches you can start training on.
How do I select the right AI data collection company?
Ask for a sample batch, a clear account of where the data comes from, and how consent is recorded. Check how the company measures the spread of a dataset, not only its size. Then compare prices per item for the same quality.
What challenges come up when collecting data for AI training?
Gaps in rare cases, bias toward the easiest sources, and missing consent are the most common. Labels can also drift when many people work on one dataset. We plan for each one before the first batch and report on it with every delivery.
Describe the data your model needs
Tell us what the model does and where it fails. We'll reply with how we'd collect the first batch and the price per item.