Use the Pipeless API to launch

Instant infrastructure

for powering

Power real-time personalized recommendations and activity feeds using a simple API

Real-time personalized recommendations and activity feeds using a simple API

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Map Your Data

Sending us object-relationship-object events, we build out an efficient graph database that maps your data.

Choose an Algorithm

Ready-to-go algorithms make it easy to build smart features using your mapped data, without the complex pipeline.

Get Real-Time Results

Using our API, when you request the algorithm to run on your data, results get generated in real-time.

Our Solutions


Sorted Content

Get a personalized sorting of a pre-defined set of content based on a user’s engagement and interests.
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Activity Feeds

Following Feed

Get a timeline of content from accounts a user is following.
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Activity Feeds

Following Action Feed

Get a feed of activity using multiple signals of user engagement from accounts a user is following.
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Activity Feeds

Recent Activity

Get recent user activity related to a specific item (account, product, tag, post, video, etc.)
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Benefits of Pipeless


It's cost-effective.

Pipeless takes advantage of the efficiency of connected data with graph databases vs. building massive multiple tables for every user and algorithm. We offer a low monthly fee with no upfront investment vs. the cost of hiring data engineers and building and maintaining your own infrastructure.

It's flexible.

With Pipeless, multiple algorithms run on the same framework and database vs. having to set up different infrastructure for every algorithm. Pipeless is easily extensible so future algorithms and functionality will work on the same dataset vs. having to build a whole new system/model for each use case you may want.

It's fast to get up and running.

You can get various recommendations and activity feeds ready in days when using Pipeless vs. the months it might take to build a single algorithm from scratch.

It's responsive to active users.

Pipeless adjusts results in real-time based on current user behaviors, keeping content fresh vs. using pre-calculated results for every user and algorithm.
devRant case study
Pipeless Case Study
A social network of over 100,000 software engineers utilized Pipeless to build a subscriber (follower) activity feed and a module with personalized recommended users to subscribe to.
Increase in users subscribing to other users
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