TL;DR
- Buy extraction that preserves page and table evidence, not merely readable text. A number without year, unit and reporting scope is unsafe account research.
- LlamaParse is a candidate for complex layout and table-aware parsing. Textract fits managed AWS document analysis. Google Document AI fits a Google Cloud parser workflow. Unstructured is relevant for partitioning documents into elements.
- Use a representative report fixture with scanned pages, multi-column text and tables spanning pages. Check consequential fields against the original before accepting the account brief.
- Price processed pages, chosen parser features, model extraction and exception review. Reprocessing a whole report to fix one table can dominate a nominal per-page saving.
The output is an evidence record
Annual reports contain useful account evidence: operating segments, geography, revenue, strategic priorities and risks. They also contain prior-year comparisons, footnotes and consolidated figures that can be misread. A parser can accurately extract “240” while a later model incorrectly labels it as current revenue in millions of dollars.
Retain document URL, report date, reporting entity, page, table/section, original label, value, unit and period. Keep the text or permitted excerpt that supports the field. Distinguish printed page number from PDF page index, because covers and appendices often make them differ.
This is downstream of web collection. Fetching the correct PDF solves access, not interpretation. Our pre-call research guide explains how accepted evidence should reach the seller.
Four extraction routes
| Route | Useful capability | Cost basis | Evaluation risk |
|---|---|---|---|
| LlamaParse | Complex document parsing and structured tables | Credits by product/mode | Table structure and parsing concerns need inspection |
| Textract | Text, forms, tables and Queries | Pages and selected features | A query answer needs reporting context |
| Google Document AI | OCR and layout parsing | Processor/page pricing | Processor choice changes output and cost |
| Unstructured | Element partitioning | Deployment/API offer | Element order and table settings must fit the report |
LlamaParse
LlamaIndex's Parse overview addresses complex layouts and document structures. Its response format describes tables, rows, HTML/CSV and parsing-concern fields. Those are useful when account research requires inspectable structure rather than a plain-text stream.
Choose it for a fixture with difficult multi-column pages and tables, then inspect the returned concerns and source location. A neatly formatted Markdown table can still attach a value to the wrong year. Keep parsing and field extraction as separate acceptance steps.
The pricing page distinguishes credits across products and modes. Select the required parse/extract tier and price reprocessing, storage and output retention. Avoid treating the open-source application framework as the price of the hosted document service.
Amazon Textract
Textract's documentation describes extraction of text, forms and tables. Its Queries reference describes specialised query responses. This fits an AWS pipeline where document analysis and structured outputs can feed your own validation layer.
Use table relationships and page context when extracting financial values. A question such as “What is revenue?” is too broad for a report containing group, segment and regional figures. Ask for the intended entity/period and verify the response against its source evidence.
The pricing page separates features; its US West (Oregon) example lists $0.020 per page for Tables and Queries within the first one million pages. Confirm region and selected operations before applying that figure. Choose it for compatible managed analysis, not as a guarantee that the resulting research interpretation is correct.
Google Document AI
Google's Document AI description separates OCR and parser choices. Its layout parser documentation describes structured output from complex files, including OCR and generative capabilities. This is relevant to a Google Cloud estate wanting a managed document pipeline.
Select the actual processor and version in the fixture. OCR, layout parsing and specialised extraction have different purposes and prices. A page count alone does not define the service bought.
Choose it when the processor returns usable evidence for your report shapes and the cloud governance fits. Avoid a specialised form parser whose assumptions do not match annual reports. Preserve page locations and original units rather than letting a downstream summary erase them.
Unstructured
Unstructured's legacy Partition API SDK documentation describes submitting a PDF for partitioning. Confirm the supported current product and migration path before making a new hosted implementation depend on that legacy endpoint. An element-based output is useful when the research application needs to handle headings, text and tables differently.
Check the current PDF strategy, table inference and metadata settings in the applicable documentation. Choose the route where the element contract simplifies your pipeline. Avoid assuming partitioned elements automatically establish financial meaning or perfect reading order. Obtain the current hosted/deployment tariff for the required strategy rather than assigning an unverified universal page price.
Work through a revenue table
Suppose a hypothetical annual report shows “Revenue, £m” with 2025 and 2024 columns, 240 and 210 respectively. The accepted field is group revenue of £240 million for the year labelled 2025, with the table and page retained. It is not $240 million, quarterly revenue or the revenue of every subsidiary.
The arithmetic growth is 14.3%, rounded: (240 − 210) / 210. That calculation is valid only if the periods and accounting scope are comparable. A footnote about a discontinued business or restated prior year can change the interpretation. Keep the footnote with the evidence.
For a table spanning two pages, inspect repeated headers and carried totals. A parser may treat the second page as a new table or attach a row to the preceding year. Use a field-level fixture that requires entity, label, unit, period and value together, not only a numerical match.
If the report says management plans to expand, retain it as a plan. Do not rewrite it as an achieved expansion or confirmed buying project. Use the ABM Account Scoring Template to distinguish a supported report fact from the commercial judgement it informs.
Price the report workload
Assume 100 reports averaging 80 pages: 8,000 pages. At the Textract example's $0.020 Tables-and-Queries page rate, the illustrative analysis component is $160, subject to current region and operation terms. This does not include model calls, retrieval, storage, tax or additional features.
If 10% of pages need a second pass under the same rate, analysis becomes $176. Add assumed model/extraction costs of $40, storage/orchestration $20 and 50 review exceptions taking five minutes each at $60 per hour. Review costs $250, for an illustrative total of $486.
If 90 reports pass the declared field contract, cost per accepted report is $5.40. That acceptance count is an assumption, not measured performance. A cheaper parser can lose if it creates more financial-table exceptions; a more expensive mode can lose if it adds no useful improvement in the relevant fields.
Use page selection where the service and task permit it. Retain the original report and parser version so the team can reprocess the specific failed section without buying the whole workflow again. Check file/page limits and result retention before designing a large backfill.
Choose on accepted fields
Use a fixture with born-digital and scanned reports, several currencies, multi-column text, merged cells and multi-page tables. Score evidence location, numerical meaning and missing/uncertain fields separately. Do not call a complete-looking summary accepted until its consequential figures reconcile with the source.
Choose the parser that supplies enough structure for your application to verify the required fields at sustainable cost. Our ABM research guide covers the broader research tool purchase. A document API earns its place when it makes report evidence inspectable rather than simply making more fluent account briefs.
FAQ
Is OCR enough for annual reports?
It can recover text from images, but tables and reading order still need interpretation. Check whether the output preserves labels, columns and page relationships. The required evidence contract determines whether plain OCR is sufficient.
Can we trust a high confidence score?
It is one signal under the provider's documented meaning. A recognised number can still belong to the wrong year or unit. Verify the combined field and source context rather than treating confidence as a truth guarantee.
Should we parse every page?
For broad research, that may be useful. For a defined field set, selective pages can reduce cost where the service supports it. Preserve the document index and ensure omitted footnotes or definitions do not change the extracted meaning.
Can the API infer missing financial data?
Keep inferred values separate and explicitly labelled, with a defensible calculation. An unavailable field should remain unavailable if the source does not support it. A plausible estimate is not a reported fact and should not silently enter a CRM evidence field.
