Most companies have been hoarding data for years. Event logs from 2019. Customer interactions from three product versions ago. Raw sensor data that nobody ever queried. This dark data sits in S3 buckets and data lakes, technically accessible but practically useless.
The irony? This is exactly what AI models need. Historical patterns. Edge cases. Real-world messiness. But when your data science team asks for “all customer support tickets from 2020–2025 to train a sentiment model,” the answer is usually: “That’ll take us two months to extract and clean.”
By the time the data’s ready, the project timeline is blown. Or worse, the model requirements have changed. Now they need the raw transcripts, not the summaries you spent six weeks preparing.
Traditional data architectures treat historical data as an archive, something you retrieve once and shape for a specific purpose. Matterbeam treats it as a replayable stream you can reshape infinitely.
Here’s how it works:
Store immutable facts once. When data flows into Matterbeam, it’s captured as close to raw as possible and never overwritten. That 2020 support ticket? It’s preserved with its original timestamp, metadata, and messy JSON payload intact.
Replay through any transformation. Need those tickets vectorized for RAG? Replay the stream through an embedding transform. Changed your mind and want them summarized by sentiment instead? Replay again with different logic. The original data never moves; you’re just materializing new views.
No more “we threw that field away.” Remember when you decided to drop that optional event attribute because “we’ll never need it”? With immutable storage, nothing gets discarded. When an AI technique six months from now needs that exact field, it’s still there.
By using this approach, teams can resurrect years of campaign data, customer interactions, and system logs that had been sitting unused. Within hours, data science teams can materialize multiple training datasets. Each one shaped differently for separate model experiments. Previously, accessing that historical data would have meant multi-week pipeline projects.
When history is replayable instead of archived, those years of dark data become living fuel for AI:
Train models on real edge cases. Production data includes all the weird scenarios synthetic data misses. Replay it to create training sets that actually match reality.
Test model changes against history. Before you deploy a new version, replay your historical stream through it. See how it would have performed last quarter, last year, or during that weird spike in March 2024.
Experiment without fear. Trying a risky transformation? Go ahead. The original data stays intact. If the experiment fails, you’ve lost nothing but a few minutes of compute time.
“Matterbeam isn’t just about moving data. It’s about transforming how we think about workflows,” says Promoboxx CTO Romi McCullough.
The shift from data hoarding to AI-ready isn’t about collecting more data. It’s about making the data you already have replayable, reshapeable, and actually usable when AI techniques inevitably change again next quarter.
Matterbeam’s architecture makes this shift measurable.
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