The Challenge
Your team is testing OpenAI embeddings, Anthropic’s Claude, and a custom fine-tuned model. Each needs customer data in a slightly different format. The traditional approach: build three separate pipelines, each with its own failure modes and maintenance overhead.
Every AI workload expects data its own way. Your RAG pipeline needs chunked documents for Pinecone. Your fine-tuning needs JSONL for OpenAI. Your analytics needs Parquet for Snowflake.
Standard ETL forces a choice: pick one destination and commit, or maintain separate pipelines for every use case. Want to add a second AI tool? Build another pipeline. Want to test a new vector database? Rebuild everything. Each integration duplicates data processing, multiplies failure points, and drains engineering ca…
The Challenge
Your team is testing OpenAI embeddings, Anthropic’s Claude, and a custom fine-tuned model. Each needs customer data in a slightly different format. The traditional approach: build three separate pipelines, each with its own failure modes and maintenance overhead.
Every AI workload expects data its own way. Your RAG pipeline needs chunked documents for Pinecone. Your fine-tuning needs JSONL for OpenAI. Your analytics needs Parquet for Snowflake.
Standard ETL forces a choice: pick one destination and commit, or maintain separate pipelines for every use case. Want to add a second AI tool? Build another pipeline. Want to test a new vector database? Rebuild everything. Each integration duplicates data processing, multiplies failure points, and drains engineering capacity.
The AI landscape evolves monthly. New models launch. Better embedding approaches drop. Your competitors ship. Meanwhile, you’re stuck maintaining brittle point-to-point jobs while engineering backlogs stretch into quarters.
The Fix
Matterbeam’s Unified Stream eliminates duplication with one replayable log that feeds any AI destination simultaneously. The result: test three embedding models in parallel instead of picking one and hoping it works. Your OpenAI fine-tuning, Pinecone vectors, and Snowflake analytics all pull from the same source. No duplicate pipelines. No brittle integrations.
Point-and-click emitters transform data for any AI destination. Vectors for Pinecone. JSON for your copilot API. Parquet for Snowflake — instantly.
The Iteration Engine adapts data for each tool. RAG systems get chunked documents. Training pipelines get behavioral features. Analytics get aggregated metrics — all with full replay and lineage to trace results back to source.
Changed your mind about embedding models? Replay with new transformations. Want to test a different vector database? Point a new emitter at your existing stream. No rebuilding. No waiting on engineering sprints.
The Unlock
Every AI workload taps live and historical data, perfectly shaped, without new pipelines. When OpenAI ships their next model, you’re testing it same-day. When a better embedding model arrives, you replay and compare in hours.
Data scientists stop waiting on engineering to prep datasets. Product teams ship AI features without cross-team dependencies.
Everyone moves at model speed, not pipeline speed.
Test across multiple tools or models, ship what works. One team went from “pick one vendor and hope” to “test five approaches and ship what works.”
See how Matterbeam accelerates your AI experiments. Talk to an engineer >
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