AI Pipeline Dev & QA Test Data

The problem

The two common approaches to test data both fail, in opposite directions. Copying production data into lower environments is the fastest route to realistic tests and also a standing compliance incident: the copy is rarely as well protected as the original, it spreads to laptops and CI logs, and it ages into a liability nobody owns. The alternative — a handful of hand-built fixtures — is safe but hollow. It covers the cases someone thought of on the day they wrote it, drifts out of sync with the schema, and quietly stops exercising the edge cases that actually break the pipeline. Neither gives you the thing you want, which is realistic data whose expected output you already know.

The SymageDocs approach

Generate the corpus instead of copying or hand-writing it. A job produces as many documents as the test needs, across 83 API-addressable form definitions spanning tax, healthcare, financial, onboarding, insurance, and commercial documents, rendered as typed or handwritten PDF and PNG, and returns a bundle that always includes per-instance annotated JSON and a tabular CSV. Those two artifacts are what turn generated documents into a test suite: they are the expected values. Partial-fill scenarios let you generate the awkward inputs deliberately — forms with sections still blank, or captured at an intake stage rather than completed — which is usually where pipelines actually break. Seeds are recorded per instance, so when a test fails you can trace the exact input that caused it.

What you actually get

• On-demand corpora sized to the test, from a handful of documents for a unit test to large batches for load testing.
• Per-instance annotated JSON and a tabular CSV in every bundle — the expected values your assertions compare against.
• Partial and staged fills, so incomplete and intake-stage documents are a case you generate rather than a case you hope never happens.
• Typed and handwritten renderings, in PDF and PNG, from the same job.
• Zero real personal data, so the corpus can sit in CI, in staging, and on developer machines without a data-access review.

How it works

1. 1

   Describe the corpus

   Pick the form types and quantity. The library covers 83 definitions across tax, healthcare, financial, onboarding, insurance, and commercial documents — all addressable through the API, with a curated subset documented on the public /forms pages — so most pipelines can be exercised with real layouts rather than a stand-in.

2. 2

   Shape the awkward cases

   Use partial-fill scenarios to control which groups of fields arrive completed, which are blank, and which get filled at a later stage. Presets cover common shapes such as a fully completed document, a payer-intake document, and a prefilled form awaiting handwritten completion.

3. 3

   Wire the expected values into tests

   Read the bundle's CSV or per-instance JSON as the source of truth for what should be extracted. Assertions compare pipeline output against generated values, so the suite stays correct when the corpus is regenerated.

4. 4

   Regenerate rather than maintain

   When the schema changes, generate a new corpus instead of editing fixtures. Seeds are recorded per instance for tracing a specific failure. Bundles are not retained indefinitely, so store the corpus your suite depends on as a build artifact rather than planning to re-download it later.

By the numbers

Field and page counts are read from the shipped form definitions in the SymageDocs form library. Library-size figures count every definition the API can generate; the public form reference pages under /forms document a curated subset of them, so that page shows a smaller number. Output-format availability reflects the current API contract; the ML export formats (BIO, YOLO, COCO, Donut) sit behind a feature flag that is not enabled in production, so treat them as a preview rather than a generally available option.

Who this is for

Engineering and QA teams building document processing pipelines who need realistic inputs in CI, staging, and demo environments; platform teams trying to get production data out of lower environments without losing test fidelity; and anyone maintaining a fixture set that has quietly stopped representing reality. It also works well for sales engineering, where a realistic demo corpus is needed and a customer's real documents are not an option.

Where SymageDocs is not the right tool

Synthetic data does not reproduce your production distribution. It reflects the modeled population, not your actual customers, so it will not surface the specific skew, the legacy records, or the peculiar data-entry habits that make your real corpus what it is — load and performance testing against it is representative only insofar as your production documents resemble the modeled ones. It is also not a substitute for staging against real integrations, and it will not help with bugs whose cause is a genuine production data quirk. If your test failure only reproduces with one specific real record, generated data will not find it for you.

Relevant forms

Frequently asked questions

How does this replace production data in staging?

By supplying the property that made production data attractive — realistic, coherent, structurally valid documents — without the property that made it a liability. Generate a corpus of the document types your pipeline handles, at the volume your environment needs, and point staging at that. Because the bundle includes a tabular CSV and per-instance JSON describing exactly what was generated, you also gain something a production copy never gave you: a known correct answer for every document.

Can I use this in CI?

Yes, and it is a natural fit because the output contains no real personal data, so a corpus can be cached in a CI artifact store without the handling controls a production extract would demand. The common pattern is to generate a fixed corpus once, store it as a build artifact, and have the test suite consume it, rather than calling the API on every run — that keeps CI fast and hermetic while still letting you regenerate deliberately when the schema moves.

How do I test how my pipeline handles incomplete documents?

Generate them on purpose with partial-fill scenarios. Each group of fields can be marked as prefilled, completed, or blank, and presets cover common real-world shapes such as a payer-intake document or a prefilled form awaiting handwritten completion. This matters more than it sounds: incomplete and partially captured documents are one of the most common causes of production extraction failures, and they are almost never represented in a hand-built fixture set.

Will the same job give me the same documents every time?

Within a single generator version a seed reproduces the generated data, and the seed for each instance is recorded in the JSON so you can trace a specific failing document. Two things it does not cover, and pretending otherwise would set you up for a confusing test failure later. Across generator versions the same seed yields different output as form definitions and distributions change. And handwritten rendering is not seeded — the writer is drawn independently on each render — so handwritten pixels vary run to run even when the field values do not. When a suite needs byte-identical inputs, generate the corpus once and store it as an artifact rather than regenerating it per run.

How large a corpus can I generate for load testing?

Quantity is a job parameter, capped at 10,000,000 documents per request on the public generation API, and large jobs are chunked across workers and assembled at completion. Two neighbouring endpoints have tighter ceilings worth knowing if you are scripting against them directly: raw identity generation and tabular row generation each stop at 10,000 per request. Past roughly two gigabytes uncompressed the download switches to a sharded layout — a preview archive, a manifest, and per-shard archives — so a large corpus can be fetched incrementally and inspected before the full transfer finishes. The preview is capped by a size budget rather than a document count, which keeps it a fast download regardless of how heavy the individual artifacts are.

Does the generated data exercise the same edge cases as production?

It exercises structural edge cases well, and you have direct control over them: dense multi-page returns, handwritten entries, and partially completed forms are all things you can ask for deliberately rather than wait for. What it cannot do is reproduce your production distribution, including whatever historical oddities and data-entry habits live in your real corpus. Treat it as the way to cover the cases you can enumerate, and keep a real-data path for the ones you cannot.

Related use cases