Two years ago, your team built pipelines to chunk documents for RAG. Carefully tuned: 512 tokens per chunk, 20% overlap, semantic boundaries respected. It took three months to get right.
Then context windows exploded to 200K tokens. Suddenly, aggressive chunking was counterproductive. You needed larger segments, different overlap strategies, maybe no chunking at all for certain use cases.
So you started over. New pipeline. Another sprint. By the time it shipped, agents arrived and now you need the same data shaped as structured function calls, not embedded passages.
This is the AI infrastructure trap: AI-ready keeps changing, but pipelines are permanent. Every technique shift means another pipeline to build, test, and maintain. Your data engineering team isn’t building new features. They’re rebuilding the same data in different shapes.
Matterbeam’s architecture inverts the traditional model. Instead of building purpose-specific pipelines, you:
Collect raw data into immutable streams. Documents, events, database changes. Everything flows in once and stays in a replayable format. No premature optimization for RAG vs. agents vs. whatever comes next.
Define lightweight transforms. Need text chunked? That’s a transform. Need it vectorized? Another transform. Need it restructured for function calling? One more. These transforms are modular. You compose them like building blocks rather than rebuilding monolithic pipelines.
Materialize views on demand. When requirements change (and they will), you don’t rebuild extraction logic. You replay the original stream through a new transform and materialize a different view. Same source data, new shape, done in hours instead of months.
Here’s what this looks like in practice. A team needs product catalog data shaped three different ways: RAG for customer support, structured records for an agent, and embeddings for similarity search. The traditional approach meant building three separate pipelines. With Matterbeam, you set up one collector and three emitters. When a fourth experiment needs a different format, add another emitter and replay the stream. Total time: under two hours.
When data is replayable rather than pipeline-locked, your team can finally move as fast as AI techniques evolve:
Test new approaches in parallel. Run five different chunking strategies against the same corpus simultaneously. See which one actually performs better in your use case, not in a benchmark.
Adapt without engineering sprints. Product team wants to try a new model that needs data shaped differently? They don’t submit a ticket and wait for Q3. They define a new emitter and replay the stream.
Version your AI data like code. Traditional pipelines create a mess of data versions scattered across systems. Matterbeam’s replay model means every materialization is deterministic and traceable. You can see exactly which transform logic created which training set.
The real shift is psychological. Teams stop asking, “Can we afford to try this?” and start asking “Which of these five approaches works best?” When experimentation is cheap and fast, you actually experiment and that’s how you find the AI applications that deliver real value instead of dying in POC purgatory.
Ready to experiment at AI speed instead of pipeline speed?
Our AI Data Prep Guarantee: If your AI experiments aren’t running faster in the first 60 days, we refund 100%. Connect with a Matterbeam engineer.
