Data pipeline lineage interview desk

For data engineering consultants, turn authorized pipeline descriptions and owner notes into engineer-reviewed lineage draft. Address this specific problem: undocumented lineage requires repeated stakeholder interviews. The aim: separate documented lineage from team recollection. The pilot tests whether that benefit holds up against reviewer effort and real operating costs.

The buyer creates a project, supplies authorized pipeline descriptions and owner notes, and confirms scope and access. Users correct extracted facts, resolve flagged uncertainties and approve the final engineer-reviewed lineage draft before use. Retain source links and a version history for the next cycle.

How the AI works

AI assists these bounded tasks: structure lineage claims; link supporting artifacts; flag unverified edges. Use only authorized pipeline descriptions and owner notes and preserve uncertainty in engineer-reviewed lineage draft. Keep model suggestions separate from verified facts. Link factual outputs to authorized input evidence and show missing information explicitly. Use deterministic checks for counts, dates, identifiers and arithmetic where applicable. A designated reviewer validates consequential outputs and signs off the delivered result.

Safeguards

Protect secrets, customer data and source code. Use controlled environments, technical review and a recoverable deployment process. One organization, one defined input format and one representative pilot batch using authorized pipeline descriptions and owner notes. Professional judgment, physical inspections, live external actions and production certification remain outside this prototype. Require appropriate access and publication approval. Preserve source material, label AI drafts and make corrections traceable. Measure false positives and missed cases alongside speed.

What to build first

Costed pilot: One organization, one defined input format and one representative pilot batch using authorized pipeline descriptions and owner notes. Professional judgment, physical inspections, live external actions and production certification remain outside this prototype. Start with one buyer organization and a bounded set of representative inputs. Implement the first two modules: structure lineage claims; link supporting artifacts. Support the third task through an assisted review queue: flag unverified edges. Handle the remaining required functions manually until validated. Include input upload, source references, user correction, a reviewer approval step and export of engineer-reviewed lineage draft. Authentication, account isolation, deletion controls and basic operational logging are included. Specialized production certification, live write integrations and broader rollout are not included unless explicitly stated.

What it can connect to

Authorized repositories, technical documentation, application APIs and logs. Case management, customer records, document storage and notification systems. Begin with an exportable review pack before automating destination writes. Begin with uploads and exports of authorized pipeline descriptions and owner notes. Any named system or connector is a candidate requiring current access and compatibility checks; no live connection is included by default.

The screens in detail

Give submitters a mobile-friendly step-by-step form with document uploads and a visible completeness checklist. Staff see a queue with missing items and extracted fields. Place the original document beside each uncertain value. Show submitted, clarification required and ready-for-review states. Open with brief and sources; move into data pipeline lineage interview desk for the detailed task; finish in review and delivery for review and handoff. Show the source record, uncertainty and approval status beside each proposed output.