VishwanathVish
Dugani.

The project supports roughly 500 document types through reusable configurations. In this capacity model, grow an import, change the document mix, and balance worker capacity against the shared API budget. Retried requests and human review add different kinds of work.

1. 01 / Ingest & parse500 documents
2. 02 / Classify & extract2 workers0.5 docs/s capacity
3. 03 / Shared model API2 requests / secondShared across workers
4. 04 / Validate & reviewReview needs people, not more workers

The decision: scale the limiting stage, keep retries within the API budget, and plan human review separately. Supporting more document types also needs reusable configuration and evaluation, beyond adding compute.

Illustrative workload, not production volumes or timings. Each attempt takes 4 worker-seconds; retry and review percentages come from the selected example mix. Extraction time = total attempts ÷ min(workers ÷ 4, API budget). Parsing, startup, and human review time are excluded; review count is not an accuracy score.

An analyst is checking lot A12 on 1 September 2026. Grow the example archive while keeping the answer context small. Remove the site filter to see a similarly worded certificate consume that context, then add the renewal and check the evidence again.

1. 01 / Indexed archive
2. 02 / Matching candidatesSearch to inspect
3. 03 / Answer contextNo passages yet
4. 04 / Check the evidenceNo answer yet

The decision: keep the answer context bounded as the archive grows, match evidence to the correct entity, check validity, and leave unsupported questions unanswered.

Synthetic archive counts and four fictional excerpts, with fixed ranking and answer rules. The archive count illustrates scale; this page does not index 100,000 documents or run an LLM. The project uses embeddings and vector search. The displayed passages and decisions explain the retrieval boundary, not a latency benchmark.

A new document uses unfamiliar field names. Suggest a mapping, check it against the sample, and turn it into a reusable import configuration.

No validated fields yet.

The change: a new format can start from a suggested configuration, with ambiguous fields resolved before import. This illustrates the manual setup dependency removed by the onboarding work.

Fictional documents, repeated sample layouts, and fixed suggestions; no live LLM or file import. The batch shows configuration reuse and a deliberately changed field. A changed layout is held for review, without silently modifying the validated configuration.

A client agent can read certificate CERT-104. Try an extraction, grant its required scope, and then request a document from another workspace. Send a batch to see that a denied request creates no work, even when repeated.

No request sent.

The agent has read access. An extraction also requires extractions:run.

The decision: check both the action and the resource boundary before starting work. Every request has an auditable decision; granting an extraction scope does not grant another client’s data.

Local simulation with fictional clients, workspace boundaries, tool names, and scopes. No API calls or persisted jobs. Counters cover all attempts; the scrollable audit shows the last eight.

Send a fault from charger AMS-017, then deliver an older charging update. Pause processing to see why an operator’s fleet view can lag behind the device. Each event button resends the same update; press it again to test duplicate handling.

FOLLOW ONE CHARGER / ORDERING AND DUPLICATES

Queue empty.

The decision: leave headroom to recover a backlog while new traffic continues. At the individual charger, preserve ordering and reject duplicates so higher throughput still produces a correct fleet view.

Synthetic charger events. Sequence numbers illustrate ordering; this is not an EVBox protocol implementation. The example queue holds 20 events and the history shows the last eight.

Follow one module through measurement, saved failure, adjustment, and retest. Then run the remaining sample lot: first-pass yield and release status are different measures, and a successful retest must not erase the original failure.

The decision: make saved results part of the station workflow. Track first-pass yield across the lot, release each unit only after a saved pass, and retain its earlier failures for investigation.

Fictional lot of 12 modules, with three low-output fixtures. These readings, limits, and yield figures are illustrative. Bulk testing saves each untested module’s first result; it never adjusts failed modules or overwrites saved attempts. No hardware is connected.