Data pipelines for AI/NLP providers
Accelerate customer model deployment by streamlining data pipelines
Reduce speed to deployment with rapid integration of surrounding sources and destinations for your products.
Built to support real-time processing of data
Datastreamer is what engineering teams at model providers use to help customers get up and running faster.
Instead of asking them to wrangle scrapers, clean messy inputs, or build enrichment pipelines around your models, Datastreamer accelerates how your models plug into your customers’ existing systems and workflows.
Make your models easy to adopt, and tough to replace
- Increasing value of the models
Shorten integration timelines, reduce drop-off during onboarding, and increase usage by increasing data reliability
- Faster deployments
- Connect output models to downstream tools
- Support more diverse use cases
Built for teams that need more than scrapers & scripts
Ready-to-use components allow complex multi-source data pipelines to be assembled in minutes. Deployment happens instantly and is fully managed, allowing low-latency, high volume processing.
Common use cases
Unstructured inputs -> Normalized
Continuous data collection and structuring of varied social data sources, melded to existing schemas.
Data Routing -> To Model
Deliver raw or enriched data into the models from customer sources with managed pipelines.
Data Routing -> From Model
Route enriched data from models back to customer endpoints or storage solutions.
Proserv -> Automation
Reducing custom integration work using pre-built library of connectors.
Existing workflows -> Adaptability
Storage free -> Processing
With storage-free processing by default, meet adherence to compliance and sub-processing requirements.
Why product and engineering teams use Datastreamer
6 weeks
reduced build time per new source or enrichment connection
$285k
average annual benefit per Datastreamer customer
6,373
average annual “people hours” of pipeline work saved
80M
average pieces of web content consumed monthly per pipeline
7+
average datasources or enrichments used per pipeline
38,000+
ready-to-deploy capabilities available in the Datastreamer registry
Data pipelines for AI/Model providers
Using Datastreamer, your engineering teams can rapidly deliver the data pipelines for complex use cases, reducing speed-to-launch of new features by 85%
FAQ’s
Usually no. Datastreamer runs alongside your offering or as a pre-integrated piece of your onboarding toolkit to help them connect data and route outputs.
Many providers expose Datastreamer-branded abilities, others embed it invisibly into their platform.
Reviews, forums, blogs, social media, feeds, scrapes, and more. Datastreamer handles structure and consistency across formats.
We abstract each source into a common schema, so your downstream systems don’t have to handle structural drift, missing fields, or format mismatches.
Yes. You can create pipelines that prep content for your model and then structure outputs for downstream consumption.
Yes. Stream data continuously or in batches. You and your customers choose what fits their workflow.
It’s fully managed. There is no infrastructure to deploy or maintain. We handle scaling, updates, and availability so your team can stay focused on building product.
Yes, we partner with many providers for ETL, database, and similar offerings. ElasticSearch, Google Cloud, Fivetran, and more are listed partners of Datastreamer.
We also partner with a number of NLP, and AI model providers including: PrivateAI, Trisane, Anthropic, Google Cloud (Gemini), and many others.
