Why AI data preparation starts before the scrape
When we think of data prepartion, we often think about cleaning, balancing and annotation. However, data preparation starts at collection. Everyone on the project team is responsible for data curation and ensuring that datasets are properly prepared for production-grade software. When preparation begins at collection, time, money and labor can be focused on building instead of fixing pipelines.
The best data prep tools can provide you with all the following services:
- Scalability: Grow your collection pipeline alongside your application without the need for constant patchwork infrastructure.
- Cleaning: The best collection services will remove ads, duplicates and other unnecessary data that can skew results and feed into bias.
- Enrichment: Best-in-class providers offer annotation and labeling services to help models learn and better infer the patterns of the data.
- Stability: In today’s world, pipelines need to be adaptable and ready-to-go. Sentiments, product pricing and real-world context can change in the blink of an eye. Production-grade data pipelines continue the feed regardless of changes in the data landscape.
- Support: In software, things break. This isn’t a worst case scenario, it’s a given. When your pipeline experiences problems, your team needs a Service Level Agreement (SLA) that keeps you covered and corrects the problem quickly. Ideally, there should be someone able to assist you 24/7.
AI data preparation isn’t an afterthought, it’s the foundation on which your entire system is built. Let’s take a look at some companies that offer AI data preparation from all points of the curation pipeline. A provider should meet your needs for all the requirements listed above. When web infrastructure is lacking, it shows up at the production level. AI data preparation isn’t a small decision. It’s both a foundation and a lifeline for your application.
1. Bright Data
Bright Data offers a broad set of services from sourcing to AI preparation. Built to power enterprise applications at scale, teams can use Bright Data as an end-to-end solution for AI data preparation. Massive datasets come pre-cleaned and ready-to-use. For teams that need real-time data, Bright Data offers a variety of scraping tools like proxies, pre-built scrapers and remote browsing.
- Scalability: Their infrastructure powers thousands of concurrent headless browsers across the web. With Bright Data, teams get a production-grade pipeline from day one.
- Cleaning: Datasets arrive in your environment pre-cleaned and ready-to-go. Bright Data doesn’t offer standalone data cleaning — it’s baked into the process.
- Enrichment: Bright Data’s team can provide human-in-the-loop or automated annotation and enrichment to any dataset. No matter how messy the dataset is, they can help highlight the patterns within the data.
- Stability: Advertising a 99.99% uptime, all Bright Data services are built to handle applications as they scale. Bright Data aims to offer reliable service at scale, where smaller providers can struggle.
- Support: Bright Data offers 24/7 customer support for both Premium and Enterprise users. You can view their full SLA breakdown here.
Bright Data offers ready-to-use data from the start. This helps position them as a very strong end-to-end solution. With on-demand scrapers, historical datasets and AI-ready packages, even scraped datasets come prepared for AI. With enrichment, stability and customer support, and many teams can manage their entire AI data pipeline using their tools.
2. Diffbot
Diffbot converts raw, unstructured web pages into AI-ready datasets using Large Language Model (LLM) powered extraction. Their tools aren’t built for teams who need simple structured data but AI-ready pipelines. While not as extensive as Bright Data’s end-to-end offerings, Diffbot offers a large suite of features to help get your team up and running.
- Scalability: Their extraction and crawling features allow you to convert unstructured web content into standardized JSON objects with uniform fields. They offer features similar to Firecrawl at larger scale.
- Cleaning: Diffbot approaches data cleaning from a different lens than other providers. Rather than rigid rule-based collection and cleaning, Diffbot relies on AI-powered data cleaning. Teams get quick onboarding but this can come at the expense of tighter quality assurance (QA).
- Enrichment: Knowledge Graph is used to enrich data with nuanced context. This is not a replacement for human-in-the-loop labeling systems but does provide an efficient and cost-effective alternative for teams.
- Stability: Diffbot was built with enterprise in mind. For large-scale crawling and structured ingestion, they provide stable pipelines across a variety of data formats and modalities.
- Support: Chat and email support are offered in the standard tiers. On custom plans, teams receive custom support agreements as well.
For medium-scale projects, Diffbot provides a solid solution. They offer automated collection and enrichment. However, at enterprise-scale, Diffbot may face limitations compared to end-to-end systems.
3. Appen
Appen uses a human workforce to power both AI data collection and AI data prep. They’ve been providing and enriching AI data for decades. They don’t offer direct scraping sources but they do offer cleaning, human-in-the-loop annotation and even real-world data collection. They can’t scrape your data but they can assist you with the data you’ve already scraped.
- Scalability: Appen’s annotation services span over 170 languages and countries worldwide. They’ve been preparing AI data since the 1990s.
- Cleaning: Their human workforce powers all of their offerings. Data cleaning is done meticulously using real human workers. Datasets can be very well-cleaned but it often takes longer than automated data cleaning.
- Enrichment: Appen is widely recognized as one of the pioneers in data annotation and enrichment. They offer both human and AI-powered annotation.
- Stability: Appen’s been in business for almost three decades. This company helped shape the AI industry and it likely won’t be disappearing any time soon.
- Support: SLAs are available for dedicated enterprise clients. However, standard customers receive ticket-based support.
Appen is a strong choice for enterprise teams with specific needs. They don’t provide scraping or web data infrastructure but their workforce is more than capable of providing enterprise-grade data cleaning and enrichment services. If your team can handle the costs of external web scraping, Appen provides a unique solution for enterprise applications.
4. Snorkel AI
Snorkel takes a programmatic approach. With Snorkel, teams use functions and AI agents to connect your team with expert service fast. AI agents can sometimes lack the finer nuance of human-in-the-loop data preparation but provide a fast and effective solution to a variety of teams. When teams already have access to data scraping, Snorkel gives everything they need to make their datasets AI-ready.
- Scalability: Labeling functions and LLMs make it easy to annotate millions of records quickly without the time spent on human-in-the-loop data preparation.
- Cleaning: Snorkel’s automated systems integrate cleaning straight into the labeling workflow. Teams can use rules and filters to remove large chunks of noisy and biased records from data.
- Enrichment: Snorkel allows teams to label massive datasets programmatically. Like all automated labeling systems, QA might need some external human supervision.
- Stability: Their infrastructure is designed for repeatable workflows. Labeling functions can be reused, adapted and repurposed without needing to start from scratch. Snorkel guarantees 99% uptime over a six month rolling period.
- Support: Enterprise customers receive formal SLA as you see with other enterprise providers.
If your team already has a scraping solution in place, Snorkel AI provides scalable cleaning and enrichment infrastructure to take things to the next level. Even without collection infrastructure, Snorkel AI can turn messy web data into AI-ready datasets efficiently.
5. Labelbox
Labelbox excels at downstream AI data preparation. It’s known for its intuitive labeling platform and collaborative tooling. They don’t provide scraping or collection support but they provide powerful tools to teams who already have it.
- Scalability: Labelbox lets teams manage large-scale annotation with collaborative efforts with minimal friction.
- Cleaning: Data Factory provides solutions for teams to remove bad data but in more nuanced circumstances, your team might want more specialized cleaning software.
- Enrichment: Enrichment is where Labelbox really shines. Using their Data factory, you can enrich virtually any dataset regardless of modality for granular model performance control.
- Stability: Labelbox is built for enterprise enrichment. They don’t offer much for collection or cleaning specifically but their annotation platform is built for large workflows.
- Support: With Labelbox, SLAs are optional. When implemented, they require monitoring from your team as well.
If your team already has an existing data pipeline, Labelbox is a strong offering for teams in need of labeling and annotation services.
6. Scale AI
Scale AI is best known for providing high-quality data at scale. These offerings are powered by their large annotation network and advanced QA systems make Scale AI a trusted name for collection, curation and annotation.
- Scalability: Scale’s platform allows teams to handle massive labeling operations at scale.
- Cleaning: Data cleaning isn’t a standalone product. Data Engine gives you access to pre-balanced datasets.
- Enrichment: High quality annotation for text, image, video and 3D vision data.
- Stability: Scale has been used by a variety of top enterprise corporations. Scale AI is known for consistent delivery and reliable operations.
- Support: SLAs for enterprise and managed services are available.
For teams who need highly reliable labeling. Scale Data Engine is built for projects needing end-to-end pipeline assistance.
7. AWS SageMaker Data Wrangler
Data Wrangler is the AWS solution to data preparation. Teams can upload any dataset for quick cleaning and transformation.
- Scalability: Built on top of AWS, Data Wrangler is made for enterprise scale.
- Cleaning: Data Wrangler enables transformation, validation and preprocessing all from within the data pipeline.
- Enrichment: SageMaker Ground Truth, a separate offering by AWS, gives teams access to both automated and human-in-the-loop labeling services.
- Stability: AWS is renowned for stable cloud infrastructure. They power everything from indie development to defense contracts. Data Wrangler likely inherits this stability.
- Support: AWS offers tiered support levels ranging from limited to extensive technical support.
Data Wrangler is not an end-to-end solution. It’s a viable choice for teams already tightly integrated into the AWS ecosystem. Prepare your AI data without altering your existing AWS pipeline.
AI data prep provider comparison
| Provider | Collection | Cleaning | Annotation / Enrichment | Scalability | Stability / SLA | Ideal Use Case |
|---|---|---|---|---|---|---|
| Bright Data | ✅ Full-scale scraping infra | ✅ Baked-in structured delivery and optional cleaning | ✅ Enrichment & annotation | Enterprise | 99.99% uptime, 24/7 support | Well suited for all around data prep workflows |
| Diffbot | ✅ AI-based structured extraction | ✅ Automated (vision/NLP structuring) | ⚠️ Knowledge Graph enrichment (not full labeling) | High | Enterprise API support | Automated structured extraction + enrichment |
| Appen | ❌ | ✅ Human-powered downstream cleaning | ✅ Human-in-the-loop | High (global workforce) | SLA for enterprise | Downstream cleaning and enrichment |
| Snorkel AI | ❌ | ✅ Integrated into labeling workflow | ✅ Programmatic enrichment | High | 99% uptime, 24h disaster recovery | Programmatic cleaning and labeling |
| Labelbox | ❌ | ⚠️ Basic (expandable via partners) | ✅ Strong annotation tools | High | Optional SLA | Teams with existing pipelines |
| Scale AI | ✅ (Data Engine) | ✅ Embedded in collection/curation | ✅ High-quality labeling | High | Enterprise SLAs | End-to-end labeling and curation |
| AWS Data Wrangler | ❌ | ✅ Built-in cleaning via Wrangler | ✅ Ground Truth labeling | Enterprise | AWS cloud stability | Teams already on AWS infra |
Conclusion: Start clean, stay clean
Data preparation isn’t just a technical step, it’s foundational to your entire AI system. The best tools will minimize friction and make preparation easier at every part of your pipeline. Solutions like Bright Data can give your team access to top quality data with cleaning baked in.
Annotation and enrichment services provide your AI models with much needed context for inference — Appen, Snorkel and Labelbox really shine in this step of the pipeline. AWS, Diffbot and Scale AI offer powerful services but Bright Data stands out as the most comprehensive option based on our evaluation criteria.
Data preparation starts at collection. When choosing a provider, think carefully and choose one that gives your team scalability, cleaning, enrichment, stability and support.