The Hidden Scaffolding of Enterprise AI
Techstrong.ai, Tuesday, June 30th, 2026
Preprocessing unstructured data at ingestion cuts enterprise AI token costs and speeds inference.
Enterprise AI systems struggle with massive volumes of unstructured data such as contracts, audio, and video, which drive prohibitive token and network egress costs when sent directly to models. CTERA proposes shifting processing to ingestion time by automatically generating derivative artifacts, where incoming files trigger background services that extract text, run OCR, transcribe audio, and produce structured outputs.
The system stores cleaned markdown, JSON metadata, and vector embeddings in a hidden .meta folder beside the originals, letting agents query lightweight data instead of downloading raw content. These artifacts inherit the same access controls as source files, embedding security at the storage layer.