Extend AI
A capable PDF-to-markdown API for mixed and scanned documents that keeps structure and most visuals, but stumbles on the hardest table headers.
Strong structure preservation, mixed results on complex tables
Extend AI handled all three tested PDFs through a fully automated API flow and returned downloadable markdown. In the report, it consistently preserved section hierarchy, readable prose flow, scanned-page OCR, and chart/logo content through structured figure blocks with captions. Its main weakness was table fidelity at the hardest edge cases: compound and multilevel headers could collapse, and vertical notes placed between columns were merged into nearby cells instead of being preserved cleanly.
In-Depth Review
Our detailed analysis of Extend AI — features, performance, and real-world testing.
Feature-by-Feature Breakdown
Document hierarchy preservationExtend AI consistently kept headings, section boundaries, and paragraph flow understandable across native, hybrid, and scanned pages.▾
Feature tested: Document hierarchy preservation
Result: Passed
Verdict: Extend AI consistently kept headings, section boundaries, and paragraph flow understandable across native, hybrid, and scanned pages.
Expected behavior: Extend AI reflowed full document pages into readable markdown-style text while preserving section titles, bullets, and nearby body content. This was exercised on a Target annual report page, a Sumitomo financial notes page, and a scanned two-column research page.
Test case: Image → Image
Input type: Image
Input used: Input artifact (Image): Target annual report page titled 'A Growth Story Again' with a portrait image, paragraphs, and bullet points. — landing-ai-target-annual-report-growth-story-page.png
Observed output: Output artifact (Image): On the Target annual report page, Extend AI preserved the title, report label, paragraphs, and bullet list in readable order, and it inserted a textual descript — extend-ai-target-annual-report-extracted-text-page.png
Input artifact: Input artifact (Image): Target annual report page titled 'A Growth Story Again' with a portrait image, paragraphs, and bullet points. — landing-ai-target-annual-report-growth-story-page.png
Output artifact: Output artifact (Image): On the Target annual report page, Extend AI preserved the title, report label, paragraphs, and bullet list in readable order, and it inserted a textual descript — extend-ai-target-annual-report-extracted-text-page.png
What changed: Image transformed into Image
Test case: Image → Image
Input type: Image
Input used: Input artifact (Image): Sumitomo Heavy Industries 'Additional Notes' page with numbered items and share counts. — extend-ai-sumitomo-heavy-industries-additional-notes-page-1.png
Observed output: Output artifact (Image): For the financial notes page, Extend AI kept the 'Additional Notes' heading and the nested numbered structure, including subitems such as consolidation changes — extend-ai-hierarchy-extracted-notes-page.png
Input artifact: Input artifact (Image): Sumitomo Heavy Industries 'Additional Notes' page with numbered items and share counts. — extend-ai-sumitomo-heavy-industries-additional-notes-page-1.png
Output artifact: Output artifact (Image): For the financial notes page, Extend AI kept the 'Additional Notes' heading and the nested numbered structure, including subitems such as consolidation changes — extend-ai-hierarchy-extracted-notes-page.png
What changed: Image transformed into Image
Test case: Image → Image
Input type: Image
Input used: Input artifact (Image): Scanned two-column research page with the heading 'STUDY AREA' and dense prose continuing across columns. — landing-ai-scanned-two-column-text-study-area.png
Observed output: Output artifact (Image): On the scanned research page, Extend AI reconstructed the 'STUDY AREA' section into coherent paragraphs with the heading preserved. The output stayed readable d — extend-ai-parsed-study-area-section.png
Input artifact: Input artifact (Image): Scanned two-column research page with the heading 'STUDY AREA' and dense prose continuing across columns. — landing-ai-scanned-two-column-text-study-area.png
Output artifact: Output artifact (Image): On the scanned research page, Extend AI reconstructed the 'STUDY AREA' section into coherent paragraphs with the heading preserved. The output stayed readable d — extend-ai-parsed-study-area-section.png
What changed: Image transformed into Image
Why it matters / Conclusion: Hierarchy and reading order were a clear strength in this report, including on scanned and multi-column pages.
Extend AI reflowed full document pages into readable markdown-style text while preserving section titles, bullets, and nearby body content. This was exercised on a Target annual report page, a Sumitomo financial notes page, and a scanned two-column research page.
Target annual report page titled 'A Growth Story Again' with a portrait image, paragraphs, and bullet points.
On the Target annual report page, Extend AI preserved the title, report label, paragraphs, and bullet list in readable order, and it inserted a textual description of the portrait image instead of dropping the visual entirely. The result still reads like a structured report page rather than a flat OCR dump.
Sumitomo Heavy Industries 'Additional Notes' page with numbered items and share counts.
For the financial notes page, Extend AI kept the 'Additional Notes' heading and the nested numbered structure, including subitems such as consolidation changes and accounting-policy notes. The extracted text remained organized enough to follow the original note hierarchy.
Scanned two-column research page with the heading 'STUDY AREA' and dense prose continuing across columns.
On the scanned research page, Extend AI reconstructed the 'STUDY AREA' section into coherent paragraphs with the heading preserved. The output stayed readable despite the original two-column scan, showing that the tool could recover section flow from scanned layout.
Table extractionExtend AI handled many tables well, but header relationships weakened on the most complex layouts.▾
Feature tested: Table extraction
Result: Partial
Verdict: Extend AI handled many tables well, but header relationships weakened on the most complex layouts.
Expected behavior: Extend AI extracted financial and research tables into structured text layouts that usually preserved rows, columns, and numeric values. The same capability was also stress-tested on harder cases with grouped headers, multirow structure, and vertical annotations between columns, where accuracy dropped.
Test case: Image → Image
Input type: Image
Input used: Input artifact (Image): Target 2015 Annual Report financial summary table with year columns from 2015 to 2011. — landing-ai-target-annual-report-financial-summary-table-2.png
Observed output: Output artifact (Image): For the Target financial summary, Extend AI preserved the year columns, row labels, and most numeric values in a clean tabular layout. This is a strong example — extend-ai-target-annual-report-parsed-financial-table.png
Input artifact: Input artifact (Image): Target 2015 Annual Report financial summary table with year columns from 2015 to 2011. — landing-ai-target-annual-report-financial-summary-table-2.png
Output artifact: Output artifact (Image): For the Target financial summary, Extend AI preserved the year columns, row labels, and most numeric values in a clean tabular layout. This is a strong example — extend-ai-target-annual-report-parsed-financial-table.png
What changed: Image transformed into Image
Test case: Image → Image
Input type: Image
Input used: Input artifact (Image): Orders Received table with grouped time-period headers and year-over-year change columns. — landing-ai-segment-results-table-2025-first-quarter.png
Observed output: Output artifact (Image): In the 'Orders Received' table, Extend AI retained the segment rows, the previous-versus-present quarter grouping, and the Y/Y change columns. The parsed result — extend-ai-parsed-orders-received-table.png
Input artifact: Input artifact (Image): Orders Received table with grouped time-period headers and year-over-year change columns. — landing-ai-segment-results-table-2025-first-quarter.png
Output artifact: Output artifact (Image): In the 'Orders Received' table, Extend AI retained the segment rows, the previous-versus-present quarter grouping, and the Y/Y change columns. The parsed result — extend-ai-parsed-orders-received-table.png
What changed: Image transformed into Image
Test case: Image → Image
Input type: Image
Input used: Input artifact (Image): Segment results table with compound header cells across A, B, C, D, Subtotal, Other1, Total, E2, and F3. — extend-ai-financial-segment-reporting-table.png
Observed output: Output artifact (Image): This harder segment table stayed broadly tabular, but Extend AI did not cleanly split compound header roles. The output preserved the numbers and row labels, ye — extend-ai-parsed-segment-reporting-table.png
Input artifact: Input artifact (Image): Segment results table with compound header cells across A, B, C, D, Subtotal, Other1, Total, E2, and F3. — extend-ai-financial-segment-reporting-table.png
Output artifact: Output artifact (Image): This harder segment table stayed broadly tabular, but Extend AI did not cleanly split compound header roles. The output preserved the numbers and row labels, ye — extend-ai-parsed-segment-reporting-table.png
What changed: Image transformed into Image
Test case: Image → Image
Input type: Image
Input used: Input artifact (Image): Scanned 'Stand structure before and after cutting' table with before-cut and after-cut column groups. — extend-ai-scanned-stand-structure-table.png
Observed output: Output artifact (Image): On the scanned stand-structure table, Extend AI preserved treatment rows, diameter-class columns, and before/after groupings well enough for the table to remain — extend-ai-parsed-stand-structure-table.png
Input artifact: Input artifact (Image): Scanned 'Stand structure before and after cutting' table with before-cut and after-cut column groups. — extend-ai-scanned-stand-structure-table.png
Output artifact: Output artifact (Image): On the scanned stand-structure table, Extend AI preserved treatment rows, diameter-class columns, and before/after groupings well enough for the table to remain — extend-ai-parsed-stand-structure-table.png
What changed: Image transformed into Image
Test case: Image → Image
Input type: Image
Input used: Input artifact (Image): Scanned table with multirow year groupings and treatment rows for tree mortality. — extend-ai-scanned-table-tree-mortality-multirow.png
Observed output: Output artifact (Image): For the multirow scanned mortality table, header-level relationships were not fully preserved. The output split or displaced structural cues such as the '12-inc — extend-ai-parsed-tree-mortality-multirow-table.png
Input artifact: Input artifact (Image): Scanned table with multirow year groupings and treatment rows for tree mortality. — extend-ai-scanned-table-tree-mortality-multirow.png
Output artifact: Output artifact (Image): For the multirow scanned mortality table, header-level relationships were not fully preserved. The output split or displaced structural cues such as the '12-inc — extend-ai-parsed-tree-mortality-multirow-table.png
What changed: Image transformed into Image
Test case: Image → Image
Input type: Image
Input used: Input artifact (Image): Scanned table with yearly columns and a vertically printed 'cut completed' note between the 1980 and 1981 columns. — extend-ai-scanned-table-mountain-pine-beetle-mortality.png
Observed output: Output artifact (Image): Extend AI rebuilt the main rows and year columns of this scanned table, but it failed on the vertical 'cut completed' annotation placed between columns. That no — extend-ai-parsed-table-text-between-columns.png
Input artifact: Input artifact (Image): Scanned table with yearly columns and a vertically printed 'cut completed' note between the 1980 and 1981 columns. — extend-ai-scanned-table-mountain-pine-beetle-mortality.png
Output artifact: Output artifact (Image): Extend AI rebuilt the main rows and year columns of this scanned table, but it failed on the vertical 'cut completed' annotation placed between columns. That no — extend-ai-parsed-table-text-between-columns.png
What changed: Image transformed into Image
Why it matters / Conclusion: Reliable on many standard financial and scanned tables, but not trustworthy for perfect preservation of compound headers, multirow grouping, or between-column annotations.
Extend AI extracted financial and research tables into structured text layouts that usually preserved rows, columns, and numeric values. The same capability was also stress-tested on harder cases with grouped headers, multirow structure, and vertical annotations between columns, where accuracy dropped.
Target 2015 Annual Report financial summary table with year columns from 2015 to 2011.
For the Target financial summary, Extend AI preserved the year columns, row labels, and most numeric values in a clean tabular layout. This is a strong example of the tool keeping a conventional financial table readable in markdown-oriented output.
Orders Received table with grouped time-period headers and year-over-year change columns.
In the 'Orders Received' table, Extend AI retained the segment rows, the previous-versus-present quarter grouping, and the Y/Y change columns. The parsed result kept the relationship between major column groups and their values understandable.
Segment results table with compound header cells across A, B, C, D, Subtotal, Other1, Total, E2, and F3.
This harder segment table stayed broadly tabular, but Extend AI did not cleanly split compound header roles. The output preserved the numbers and row labels, yet the semantic meaning of some grouped headers was reduced because the header layers were flattened together.
Scanned 'Stand structure before and after cutting' table with before-cut and after-cut column groups.
On the scanned stand-structure table, Extend AI preserved treatment rows, diameter-class columns, and before/after groupings well enough for the table to remain interpretable. This shows the parser could recover a substantial amount of table structure from a scanned source, not just a digital one.
Scanned table with multirow year groupings and treatment rows for tree mortality.
For the multirow scanned mortality table, header-level relationships were not fully preserved. The output split or displaced structural cues such as the '12-inch cut' grouping and introduced OCR artifacts like stray '19' values, which made the reconstructed layout less faithful than the simpler table cases.
Scanned table with yearly columns and a vertically printed 'cut completed' note between the 1980 and 1981 columns.
Extend AI rebuilt the main rows and year columns of this scanned table, but it failed on the vertical 'cut completed' annotation placed between columns. That note was absorbed into nearby values as stray characters such as '8', 'a', and '3', so the contextual annotation was lost and some cells became noisier.
Visual element captioningExtend AI preserved charts and logos as structured figure blocks with descriptions, but usually translated visuals into text instead of keeping the original visual form inline.▾
Feature tested: Visual element captioning
Result: Partial
Verdict: Extend AI preserved charts and logos as structured figure blocks with descriptions, but usually translated visuals into text instead of keeping the original visual form inline.
Expected behavior: Extend AI kept non-text visuals by converting them into figure-style elements with extracted labels and generated captions. This was tested on a waterfall chart, a scanned bar chart, and a logo from the hybrid earnings report.
Test case: Image → Image
Input type: Image
Input used: Input artifact (Image): Waterfall chart showing SG&A rate changes from 2013 to 2015. — extend-ai-sg-and-a-rate-waterfall-chart.png
Observed output: Output artifact (Image): For the SG&A waterfall chart, Extend AI emitted a figure block that preserved the chart labels, percentage values, and a generated caption explaining the moveme — extend-ai-sg-and-a-waterfall-extracted-text.png
Input artifact: Input artifact (Image): Waterfall chart showing SG&A rate changes from 2013 to 2015. — extend-ai-sg-and-a-rate-waterfall-chart.png
Output artifact: Output artifact (Image): For the SG&A waterfall chart, Extend AI emitted a figure block that preserved the chart labels, percentage values, and a generated caption explaining the moveme — extend-ai-sg-and-a-waterfall-extracted-text.png
What changed: Image transformed into Image
Test case: Image → Image
Input type: Image
Input used: Input artifact (Image): Scanned grouped bar chart of tree mortality by year and cut treatment. — landing-ai-tree-mortality-by-year-and-cut-bar-chart-1.png
Observed output: Output artifact (Image): On the scanned mortality chart, Extend AI captured the chart title, cut-method labels, years, and a generated caption summarizing the trend. However, it did not — extend-ai-parsed-chart-tree-cutting-methods.png
Input artifact: Input artifact (Image): Scanned grouped bar chart of tree mortality by year and cut treatment. — landing-ai-tree-mortality-by-year-and-cut-bar-chart-1.png
Output artifact: Output artifact (Image): On the scanned mortality chart, Extend AI captured the chart title, cut-method labels, years, and a generated caption summarizing the trend. However, it did not — extend-ai-parsed-chart-tree-cutting-methods.png
What changed: Image transformed into Image
Test case: Text prompt → Image
Input type: Text prompt
Input used: Input artifact (Text prompt): INPUT
Observed output: Output artifact (Image): Extend AI preserved the Target logo as a structured 'figure type="logo"' element with a caption describing the bullseye design. The logo was not kept in its ori — extend-ai-parsed-logo-figure-target-bullseye.png
Input artifact: Input artifact (Text prompt): INPUT
Output artifact: Output artifact (Image): Extend AI preserved the Target logo as a structured 'figure type="logo"' element with a caption describing the bullseye design. The logo was not kept in its ori — extend-ai-parsed-logo-figure-target-bullseye.png
What changed: Text prompt transformed into Image
Why it matters / Conclusion: Good if you want charts and logos retained in markdown as descriptive elements; weaker if you need the original visual presentation preserved inline.
Extend AI kept non-text visuals by converting them into figure-style elements with extracted labels and generated captions. This was tested on a waterfall chart, a scanned bar chart, and a logo from the hybrid earnings report.
Waterfall chart showing SG&A rate changes from 2013 to 2015.
For the SG&A waterfall chart, Extend AI emitted a figure block that preserved the chart labels, percentage values, and a generated caption explaining the movement from 20.2% in 2013 to 19.6% in 2015. The semantic content survived, but the output became a text description of the chart rather than the original chart graphic.
Scanned grouped bar chart of tree mortality by year and cut treatment.
On the scanned mortality chart, Extend AI captured the chart title, cut-method labels, years, and a generated caption summarizing the trend. However, it did not preserve the bar shapes or full visual encoding, so the output was useful for meaning retention but weaker for chart fidelity.
Extend AI preserved the Target logo as a structured 'figure type="logo"' element with a caption describing the bullseye design. The logo was not kept in its original page context, but it was not dropped; the tool converted it into a descriptive reference instead.
OCR for signatures, stamps, and faint markingsExtend AI successfully captured low-visibility non-body-text elements that many parsers skip.▾
Feature tested: OCR for signatures, stamps, and faint markings
Result: Passed
Verdict: Extend AI successfully captured low-visibility non-body-text elements that many parsers skip.
Expected behavior: Beyond standard body text, Extend AI extracted scanned signature blocks, a blurry audit-firm stamp, and faint handwritten markings from the research paper title page.
Test case: Image → Image
Input type: Image
Input used: Input artifact (Image): Printed signature block from the Target annual report. — extend-ai-target-annual-report-signature-block.png
Observed output: Output artifact (Image): From the Target sign-off block, Extend AI captured 'Brian C. Cornell,' the title 'Chairman and Chief Executive Officer,' and the date 'March 11, 2016.' It also — extend-ai-parsed-signature-block-brian-c-cornell.png
Input artifact: Input artifact (Image): Printed signature block from the Target annual report. — extend-ai-target-annual-report-signature-block.png
Output artifact: Output artifact (Image): From the Target sign-off block, Extend AI captured 'Brian C. Cornell,' the title 'Chairman and Chief Executive Officer,' and the date 'March 11, 2016.' It also — extend-ai-parsed-signature-block-brian-c-cornell.png
What changed: Image transformed into Image
Test case: Image → Image
Input type: Image
Input used: Input artifact (Image): Blurred Ernst & Young LLP stamp. — extend-ai-blurred-ernst-young-stamp.png
Observed output: Output artifact (Image): From a blurred Ernst & Young LLP stamp, the output still recovered the firm name and added a page-number tag. That shows Extend AI can pull useful text from low — extend-ai-parsed-ernst-young-stamp-page-number.png
Input artifact: Input artifact (Image): Blurred Ernst & Young LLP stamp. — extend-ai-blurred-ernst-young-stamp.png
Output artifact: Output artifact (Image): From a blurred Ernst & Young LLP stamp, the output still recovered the firm name and added a page-number tag. That shows Extend AI can pull useful text from low — extend-ai-parsed-ernst-young-stamp-page-number.png
What changed: Image transformed into Image
Test case: Image → Image
Input type: Image
Input used: Input artifact (Image): USDA cover page with faint handwritten marginal notes. — extend-ai-usda-forest-service-research-note-cover-handwritten.png
Observed output: Output artifact (Image): On faint handwritten markings, Extend AI did not truly transcribe the cursive. It output 'USDA Semaine' and a caption stating that the remaining handwriting app — extend-ai-parsed-handwriting-ocr-output.png
Input artifact: Input artifact (Image): USDA cover page with faint handwritten marginal notes. — extend-ai-usda-forest-service-research-note-cover-handwritten.png
Output artifact: Output artifact (Image): On faint handwritten markings, Extend AI did not truly transcribe the cursive. It output 'USDA Semaine' and a caption stating that the remaining handwriting app — extend-ai-parsed-handwriting-ocr-output.png
What changed: Image transformed into Image
Why it matters / Conclusion: A notable strength: Extend AI kept signatures, stamps, and faint markings in the extracted output instead of silently omitting them.
Beyond standard body text, Extend AI extracted scanned signature blocks, a blurry audit-firm stamp, and faint handwritten markings from the research paper title page.
Printed signature block from the Target annual report.
From the Target sign-off block, Extend AI captured 'Brian C. Cornell,' the title 'Chairman and Chief Executive Officer,' and the date 'March 11, 2016.' It also added signature tags, including an extra parsed signature tag beyond the printed block, which suggests some over-reading around signature content.
Blurred Ernst & Young LLP stamp.
From a blurred Ernst & Young LLP stamp, the output still recovered the firm name and added a page-number tag. That shows Extend AI can pull useful text from low-clarity printed markings.
USDA cover page with faint handwritten marginal notes.
On faint handwritten markings, Extend AI did not truly transcribe the cursive. It output 'USDA Semaine' and a caption stating that the remaining handwriting appears to be illegible cursive, which preserves the presence of the note but not its exact content.
API-based markdown exportThe tested workflow was fully automated and ended in downloadable markdown output.▾
Feature tested: API-based markdown export
Result: Passed
Verdict: The tested workflow was fully automated and ended in downloadable markdown output.
Expected behavior: Across all three tested documents, Extend AI accepted PDF uploads, processed them without manual correction, and returned markdown files. The report also shows a Developers area with API key creation, supporting programmatic use.
Test case: PDF document → Text/code file
Input type: PDF document
Input used: Input artifact (PDF document): 84-page hybrid earnings report used as the main mixed-content stress test. — llamaparse-hybrid-earnings-pdf-1.pdf
Observed output: Output artifact (Text/code file): For the hybrid report, Extend AI returned a markdown file as a downloadable artifact after a fully automated API flow. The researcher did not report any manual — extend-ai-extendai-hybrid-earnings-pdf-output-6.md
Input artifact: Input artifact (PDF document): 84-page hybrid earnings report used as the main mixed-content stress test. — llamaparse-hybrid-earnings-pdf-1.pdf
Output artifact: Output artifact (Text/code file): For the hybrid report, Extend AI returned a markdown file as a downloadable artifact after a fully automated API flow. The researcher did not report any manual — extend-ai-extendai-hybrid-earnings-pdf-output-6.md
What changed: PDF document transformed into Text/code file
Test case: PDF document → Text/code file
Input type: PDF document
Input used: Input artifact (PDF document): 18-page table-heavy financial report used to test complex tables. — llamaparse-sumitomo-financial-pdf-1.pdf
Observed output: Output artifact (Text/code file): The table-heavy financial report was also returned as markdown, showing the same export pattern on a different document type. This supports the claim that markd — extend-ai-extendai-financialpdf-output-5.md
Input artifact: Input artifact (PDF document): 18-page table-heavy financial report used to test complex tables. — llamaparse-sumitomo-financial-pdf-1.pdf
Output artifact: Output artifact (Text/code file): The table-heavy financial report was also returned as markdown, showing the same export pattern on a different document type. This supports the claim that markd — extend-ai-extendai-financialpdf-output-5.md
What changed: PDF document transformed into Text/code file
Test case: Text prompt → Image
Input type: Text prompt
Input used: Input artifact (Text prompt): INPUT
Observed output: Output artifact (Image): The Developers section shows an API Keys page with a 'Create new key' button, API documentation references, and request-log sections. That confirms the product — extend-ai-extendai-developers-api-keys-empty-state.png
Input artifact: Input artifact (Text prompt): INPUT
Output artifact: Output artifact (Image): The Developers section shows an API Keys page with a 'Create new key' button, API documentation references, and request-log sections. That confirms the product — extend-ai-extendai-developers-api-keys-empty-state.png
What changed: Text prompt transformed into Image
Why it matters / Conclusion: The report supports Extend AI as a real hosted API workflow: upload PDF, process automatically, and retrieve markdown output.
Across all three tested documents, Extend AI accepted PDF uploads, processed them without manual correction, and returned markdown files. The report also shows a Developers area with API key creation, supporting programmatic use.
The Developers section shows an API Keys page with a 'Create new key' button, API documentation references, and request-log sections. That confirms the product exposes an API-oriented workflow rather than being only a manual UI tool.
Document structure reconstructionStrong across hybrid, table-heavy, and scanned PDFs.▾
Feature tested: Document structure reconstruction
Result: Passed
Verdict: Strong across hybrid, table-heavy, and scanned PDFs.
Expected behavior: Converts mixed PDF pages into readable markdown-like text with headings, paragraphs, and section flow largely intact. This was exercised on a Target annual report narrative page, a Sumitomo Heavy Industries notes page, and a scanned two-column research-paper section.
Test case: Image → Image
Input type: Image
Input used: Input artifact (Image): Target annual report narrative page titled 'A Growth Story Again'. — landing-ai-target-annual-report-growth-story-page.png
Observed output: Output artifact (Image): On the Target 'A Growth Story Again' page, Extend AI kept the title, narrative paragraphs, and bullet-style highlights in readable order, and it even inserted a — extend-ai-target-annual-report-extracted-text-page.png
Input artifact: Input artifact (Image): Target annual report narrative page titled 'A Growth Story Again'. — landing-ai-target-annual-report-growth-story-page.png
Output artifact: Output artifact (Image): On the Target 'A Growth Story Again' page, Extend AI kept the title, narrative paragraphs, and bullet-style highlights in readable order, and it even inserted a — extend-ai-target-annual-report-extracted-text-page.png
What changed: Image transformed into Image
Test case: Image → Image
Input type: Image
Input used: Input artifact (Image): Sumitomo Heavy Industries 'Additional Notes' page. — extend-ai-sumitomo-heavy-industries-additional-notes-page-1.png
Observed output: Output artifact (Image): On the Sumitomo notes page, it preserved the main heading and numbered note structure with clean line breaks. The visible extracted output shown in the proof re — extend-ai-hierarchy-extracted-notes-page.png
Input artifact: Input artifact (Image): Sumitomo Heavy Industries 'Additional Notes' page. — extend-ai-sumitomo-heavy-industries-additional-notes-page-1.png
Output artifact: Output artifact (Image): On the Sumitomo notes page, it preserved the main heading and numbered note structure with clean line breaks. The visible extracted output shown in the proof re — extend-ai-hierarchy-extracted-notes-page.png
What changed: Image transformed into Image
Test case: Image → Image
Input type: Image
Input used: Input artifact (Image): Scanned two-column research-paper section headed 'STUDY AREA'. — landing-ai-scanned-two-column-text-study-area.png
Observed output: Output artifact (Image): On the scanned two-column page, Extend AI rebuilt the 'STUDY AREA' section into clean linear text, correcting scan-era hyphenation breaks such as 'between' and — extend-ai-parsed-study-area-section.png
Input artifact: Input artifact (Image): Scanned two-column research-paper section headed 'STUDY AREA'. — landing-ai-scanned-two-column-text-study-area.png
Output artifact: Output artifact (Image): On the scanned two-column page, Extend AI rebuilt the 'STUDY AREA' section into clean linear text, correcting scan-era hyphenation breaks such as 'between' and — extend-ai-parsed-study-area-section.png
What changed: Image transformed into Image
Why it matters / Conclusion: If your top priority is getting readable markdown with section hierarchy preserved across mixed PDFs, this was one of Extend AI's strongest behaviors in the test.
Converts mixed PDF pages into readable markdown-like text with headings, paragraphs, and section flow largely intact. This was exercised on a Target annual report narrative page, a Sumitomo Heavy Industries notes page, and a scanned two-column research-paper section.
Target annual report narrative page titled 'A Growth Story Again'.
On the Target 'A Growth Story Again' page, Extend AI kept the title, narrative paragraphs, and bullet-style highlights in readable order, and it even inserted a description of the headshot image. The result reads like a reconstructed report page rather than a flat OCR dump.
Sumitomo Heavy Industries 'Additional Notes' page.
On the Sumitomo notes page, it preserved the main heading and numbered note structure with clean line breaks. The visible extracted output shown in the proof reaches the start of item (4) 'Number of shares issued (share capital),' so this proof supports structural retention more clearly than full-page completeness.
Scanned two-column research-paper section headed 'STUDY AREA'.
On the scanned two-column page, Extend AI rebuilt the 'STUDY AREA' section into clean linear text, correcting scan-era hyphenation breaks such as 'between' and 'extremely' and preserving the heading-to-paragraph relationship despite paper texture and skew.
Chart and figure retentionGood at semantic retention, weaker at preserving original visual presentation.▾
Feature tested: Chart and figure retention
Result: Passed
Verdict: Good at semantic retention, weaker at preserving original visual presentation.
Expected behavior: Retains charts and some visual elements by converting them into figure-style markup with extracted labels, values, and generated captions. This was exercised on annual-report charts, a scanned research chart, and a logo element.
Test case: Image → Image
Input type: Image
Input used: Input artifact (Image): SG&A rate waterfall chart from the Target annual report. — hybrid_earningspdf_sga_chart.png
Observed output: Output artifact (Image): The SG&A waterfall chart was converted into a figure block that kept percentage labels and named drivers such as Cost Saving Initiatives, Technology, Marketing — extend-ai-sg-and-a-waterfall-extracted-text.png
Input artifact: Input artifact (Image): SG&A rate waterfall chart from the Target annual report. — hybrid_earningspdf_sga_chart.png
Output artifact: Output artifact (Image): The SG&A waterfall chart was converted into a figure block that kept percentage labels and named drivers such as Cost Saving Initiatives, Technology, Marketing — extend-ai-sg-and-a-waterfall-extracted-text.png
What changed: Image transformed into Image
Test case: Image → Image
Input type: Image
Input used: Input artifact (Image): Scanned grouped bar chart on tree mortality by year and treatment. — landing-ai-tree-mortality-by-year-and-cut-bar-chart-1.png
Observed output: Output artifact (Image): On the scanned tree-mortality chart, the output preserved the chart title, legend labels, years, and a generated explanation that the Check Area is highest and — extendai_hybridearningspdf_parsed_waterfall_chart.png
Input artifact: Input artifact (Image): Scanned grouped bar chart on tree mortality by year and treatment. — landing-ai-tree-mortality-by-year-and-cut-bar-chart-1.png
Output artifact: Output artifact (Image): On the scanned tree-mortality chart, the output preserved the chart title, legend labels, years, and a generated explanation that the Check Area is highest and — extendai_hybridearningspdf_parsed_waterfall_chart.png
What changed: Image transformed into Image
Test case: Image → Image
Input type: Image
Input used: Input artifact (Image): A logo element embedded in the hybrid earnings report. — hybrid_earningspdf_target_logo.png
Observed output: Output artifact (Image): Instead of keeping the logo visually in document context, Extend AI emitted a logo figure block whose caption identifies it as the Target bullseye symbol. The e — hybrid_earningspdf_parsed_logo.png
Input artifact: Input artifact (Image): A logo element embedded in the hybrid earnings report. — hybrid_earningspdf_target_logo.png
Output artifact: Output artifact (Image): Instead of keeping the logo visually in document context, Extend AI emitted a logo figure block whose caption identifies it as the Target bullseye symbol. The e — hybrid_earningspdf_parsed_logo.png
What changed: Image transformed into Image
Why it matters / Conclusion: Extend AI does not drop charts and logos, but it preserves them mainly as structured descriptions and extracted values rather than original visuals embedded in reading context.
Retains charts and some visual elements by converting them into figure-style markup with extracted labels, values, and generated captions. This was exercised on annual-report charts, a scanned research chart, and a logo element.
SG&A rate waterfall chart from the Target annual report.
The SG&A waterfall chart was converted into a figure block that kept percentage labels and named drivers such as Cost Saving Initiatives, Technology, Marketing Expense, and Other. It also added a caption explaining the progression from a 20.2% SG&A rate in 2013 to 19.6% in 2015.
Scanned grouped bar chart on tree mortality by year and treatment.
On the scanned tree-mortality chart, the output preserved the chart title, legend labels, years, and a generated explanation that the Check Area is highest and values drop after cut completion. But the result is textual markup rather than a faithful recreation of the bar geometry, so relationship reading depends more on the caption than on the original visual structure.
A logo element embedded in the hybrid earnings report.
Instead of keeping the logo visually in document context, Extend AI emitted a logo figure block whose caption identifies it as the Target bullseye symbol. The element is preserved semantically, but not as an in-place image.
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Pricing as of June 2026
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