Embedding Pipeline Service  ·  View 03 of 22  ·  People and journeys

Actors and Their Core Journeys

Five kinds of people and three machines, each with a goal in their own words and the journeys that goal turns into.

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The people the product exists for Knowledge worker 3 M monthly Goal — I edited that page an hour ago. When I search for it, I want today's version — not the one from last week. Core journeys Find what I just wrote most-run journey Ask a question of the workspace Find documents like this one Tenant admin 25,000 organisations Goal — When I revoke access to a document, I need it gone from search immediately — not after whatever batch job runs next. Core journeys Revoke access to a document Erase a departing employee's content Prove what the platform holds The people who build on the platform Product engineer 14 consuming teams Goal — I want retrieval over my corpus without learning what a chunker is or owning a GPU node pool. Core journeys Ship a new corpus onboarding journey Measure my retrieval quality Choose a freshness tier Platform engineer on call for the fleet Goal — A better embedding model shipped this week. I want to adopt it without a quarter-long project or a quality regression nobody notices. Core journeys Upgrade the embedding model the hard one Rebuild a corrupted index Drain a backlog after an outage Privacy officer one per region Goal — I need to show that an erasure actually happened, in every store, including the ones nobody remembers exist. Core journeys Audit an erasure end to end Check a vector export control Machines in the cast Corpus change feed 8 M versions/day Goal — Hand over every edit once, in order per document, and be told plainly when I am being throttled. Core journeys Deliver an edit Deliver a tombstone Reconciliation sweep nightly per corpus Goal — Find the documents the feed never mentioned, before a user notices they are missing from search. Core journeys Compare source to ledger Confirm a suspected deletion Quality harness per corpus, nightly Goal — Say whether retrieval got worse, and which of the three causes the evidence actually supports. Core journeys Score the frozen query set Gate a contract cutover Who the Platform Is For, and What They Get to Do Person or role Journey / task Security / platform External / third party Three journeys get their own map: find what I just wrote, ship a new corpus, and upgrade the embedding model. v 1.0 · owner Data & AI Platform Architecture · date 2026-10

What this view is for

  • The knowledge worker's goal — "when I search for it, I want today's version" — is the entire justification for the interactive freshness lane and for atomic version visibility.
  • The tenant admin's goal is the reason revocation is on the read path and not on the pipeline's freshness path: five seconds, not ten minutes.
  • The platform engineer's goal is the reason the critical design decision exists at all. Everything about contracts, aliases and dual-write is downstream of it.

Machines are actors too

  • The change feed is in the cast because its goal — "be told plainly when I am being throttled" — is a requirement: typed, retryable rejection rather than a silent drop.
  • The reconciliation sweep exists because the feed will miss documents, and the only alternative to a sweep is a user finding the gap.
  • The quality harness is an actor because somebody has to be able to say retrieval got worse, and no human watches 4.8 billion chunks.

Deliberately not here

  • The answer-layer prompt engineer: a real role, but they consume retrieval and do not change this architecture.
  • The data scientist choosing the embedding model: their output is a model digest, which enters as an input to a contract.