Most document automation pilots stall before production, and the failure mode is specific: pipelines built around document templates break when formats shift, so field mappings fail and engineering teams rebuild. LandingAI Agentic Document Extraction (ADE) separates the stable business logic, the extraction schema, from the variable document structure, which is handled at parse time.
Why Template-Based Extraction Breaks at Scale
- Template-based extractors tie field positions to specific layouts, so a vendor changing an invoice format or a counterparty sending a different version of a standard form leaves the template unmatched, and extraction fails silently or returns empty fields.
- At production volume this compounds, because new variants arrive continuously and each one needs a template update, a review cycle, and a redeployment.
- ADE parses structure semantically rather than by position, preserving tables, headers, multi-column layouts, and form fields regardless of how the source is formatted.
- The extraction schema then works against parsed output rather than raw document coordinates.
The Schema as the Stable Contract
The extraction schema names the fields you want and their types, describing what to extract rather than where to find it. When a document changes layout, the parsing layer absorbs the variation and the schema stays as it is.
- Typed fields: string, number, integer, boolean, array, and object types return consistently structured output however the value appears in the source.
- Nested objects and arrays: multi-row tables and repeating structures, such as invoice line items or multiple policy holders, come back as typed arrays rather than flattened strings.
- Restricted values: an
enumlimits a field to an approved set, so downstream systems receive one of a known list rather than free text. - Field descriptions: natural-language descriptions guide extraction, letting one schema handle wording that varies between versions.
- Alternative names: listing the other labels a field appears under helps ADE find the same value when documents title it differently.
- Predictable misses: a field ADE cannot find comes back as
nullfor primitives and as an empty array for lists, so downstream code can tell "not present in this document" apart from an extraction error.
Design and test a schema interactively in the Schema Wizard before production. Once it extracts consistently, it moves into pipelines through the Python and TypeScript libraries or direct API calls without modification.
Pipeline Architecture for Production Volume
Two processing paths scale to enterprise volumes.
| Processing path | Best for | How you get the result |
|---|---|---|
| Synchronous | Real-time workflows where someone waits on the answer | Returned in the response |
| Asynchronous jobs | Batch pipelines and large or long-running documents | Poll the job, or have output written to storage you control |
- Asynchronous jobs are the path for documents too large or slow for a live request, and both client libraries include a wait helper that polls until a job finishes.
- Both libraries retry connection errors and transient failures automatically with exponential backoff, and the retry count is configurable.
- Both ship async clients for teams running high concurrency.
- Rate limits apply at the organization level, scale with plan tier, and are customizable on Enterprise plans.
Grounding-Based Review Routing
ADE links every extracted value back to where it came from in the source document, and parsing returns page and coordinate grounding for each element, so pipelines can triage without a human reading every file.
- Fields that pass business validation route straight to downstream systems.
- Fields that fail validation go to a review queue, with grounding pointing the reviewer at the exact location in the original.
- Empty results are a distinct signal that the document lacks the field, rather than an extraction failure.
- Routing decisions rest on grounding and your own validation rules, which keeps the logic explicit and auditable.
FAQ
How does schema-based extraction avoid rebuilds when formats change? The schema defines which fields to extract and their types, not where they sit in a layout. Parsing identifies structure semantically and produces the output the schema works against, so a new vendor format or a shifted layout is absorbed at the parsing layer, provided the same fields are present.
What happens when a field is missing? Primitive fields come back as null and list fields as an empty array, so downstream code can distinguish a document that lacks the field from a processing error. That distinction is what makes automated routing safe at volume.
How does ADE handle very large document volumes? Asynchronous jobs handle large and long-running documents, with a wait helper in both libraries that polls until completion, and output can be written directly to storage you control. The libraries retry transient failures with exponential backoff, and rate limits are customizable on Enterprise plans.
When does the schema still need to change? Layout drift is absorbed by parsing, but a format that introduces a field the schema does not define, or drops one the pipeline depends on, needs a schema update. Schema-first extraction removes rebuilds caused by layout change, not changes to what data you need.