VoiceGuardAI

DatapipeAI is an agentic platform that turns a plain-English request into a labeled, versioned dataset, built on Meta's Muse Spark. You describe the dataset in a chat. A Muse-powered spec agent drafts the labels, examples, guidelines and a cost estimate before anything runs. Muse Spark generates the items, two independent labelers (Muse Spark 1.3 and 1.2) label every one, and a judge settles disagreements. Uncertain items go to a human review queue, and what people approve becomes the gold set. QA removes duplicates and flags PII. The output is a versioned dataset (JSONL, CSV, Parquet) with a datasheet, a quality report and a cost receipt. Built for production: every model call is cost-tracked live and reconciled against the run, budgets pause a run automatically if the projected cost climbs, and a usage dashboard shows spend by day, project, model and step. It also has row-level security, hashed API keys, an access audit log and delete-my-data. Customer data runs only on Muse Standard models, never on models that train on it. In the demo: 100 labeled items in under 2 minutes for $0.10, exactly the estimate. Over 600 automated tests. Stack: Next.js, FastAPI, Celery and Redis, Supabase. Next: a Muse research crew that sources real web data, with provenance on every row.