A guided, AI-assisted onboarding pipeline that ingests your existing data — files, databases, protocols, even years of email and chat — and turns it into structured, connected records.
The hardest part of adopting any lab platform is the decade of data already in spreadsheets, an old LIMS, a compound registry, and a hundred protocol PDFs. Halffield’s migration engine is built to absorb exactly that. Drop files in bulk; an LLM classifier figures out what each one is, maps its columns to your schema, detects entity types, and defers anything ambiguous to a review queue.
It goes beyond structured data: it can mine protocol documents, campaign history, historical runs, and even communication archives for institutional knowledge. You end up with your history inside the platform — deduplicated, identity-matched, schema-mapped, connected to the knowledge graph — not stranded in a legacy system.
Sources → Files → Mappings → Identity → Schema Gaps → Deferred Queue → Validation → one-shot ETL, all in one hub.
Compound & entity registries, sample exports, instrument lists, facility trees, assay endpoints, campaign history, historical runs, protocol docs, MES/ERP batch records, and more.
Automatic file classification, column mapping, entity-type detection, and identity matching — with a human review queue for the uncertain cases.
Mine protocol docs, campaign history, and communication archives (email, Slack, wiki) for the tacit knowledge that never hit a database.
One-shot ETL baseline, plus embedded-UI, API-mirror, and federated live-integration options going forward.
Production-grade guardrails: versioned snapshots, PII detection, reverse export, and project-scoped access — migration a regulated org can actually approve.