Tensorlake

@tensorlake.ai

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Two dense document pages flank a skeptical person’s sticker-style portrait against a green gradient, link text centered below.
Promotional banner for the Qdrant Essentials Course featuring Tensorlake. Text reads: ‘Improve collection querying with knowledge graphs.’ On the left is the Qdrant logo and course title; on the right is a smiling woman with long curly brown hair wearing a cream-colored top, set against a purple gradient grid background.
Tensorlake interface showing parsed Word document with tracked changes preserved as HTML tags, displaying an insurance claim report
Comparison of document header detection. Left side "Just OCR" shows incorrect hierarchy with section 2.2 at wrong indent level. Right side "Header Correction" shows proper nesting where 2.2 is correctly indented under section 2. Bottom shows Python code: doc_ai.parse_and_wait() with cross_page_header_detection=True parameter. Green gradient background with Tensorlake logo.
RAG citation workflow diagram on dark green background showing document processing pipeline: Document (PDF/Image) → Tensorlake Document AI → Parsed Elements (Text, Tables, Figures, and Bounding Box) → merge and insert anchors → Chunks and Anchors (Clean text and citation IDs) → splits to Citation Metadata (page, bounding box, citation IDs) and Vector DB (embeddings, text, and metadata). URL: https://tlake.link/blog/rag-citations
Before and after comparison of document chunking on dark green background. Top panel "Without Contextualized Chunking" shows plain text: "SMOTE creates a broader decision region for the minority class...". Bottom panel "With Contextualized Chunking" shows same text with citation anchor "<c>2.1</c>" and metadata: {"2.1": {"page": 23, "bbox": {...}}}. URL: https://tlake.link/blog/rag-citations
Tensorlake Document AI interface showing document layout analysis with JSON output on left displaying fragment types, content, and bounding box coordinates, and PDF preview on right with highlighted text regions and yellow bounding boxes overlaid on research paper content
Side-by-side comparison of a dense healthcare data table in PDF format and its structured DataFrame output. A green background with the Tensorlake logo shows an arrow pointing from the PDF to the DataFrame. The caption reads “Parse Dense Tables Reliably” with the link “tlake.link/blog/dense-tables” at the bottom.
Code snippet styled in a terminal window with green background. The Python code defines a Pydantic model NewsArticle with two fields: article_key_points as a list of strings describing key points of the article, and article_summary as a string summarizing the article. It then defines structured_extraction_options with a StructuredExtractionOptions object using the NewsArticle schema. Finally, it calls doc_ai.parse_and_wait with file=article and the structured extraction options, assigning the result to article_result.
Code snippet styled in a terminal window with green background. The Python function create_query(state: State) uses llm.invoke to generate a natural language query for searching a vector database of Tesla SEC filings. It prints the query content and returns a dictionary containing a messages list with an AIMessage holding the query, along with the query string itself.
Code snippet styled in a terminal window with green background. The Python dictionary chunk_data is being built, containing keys such as id, pdf_url, chunk_index, text, start_index, end_index, and metadata. The metadata includes nested fields like source_type set to 'tesla_sec_filing', pdf_url, chunk_id, total_chunks, chunk_index, filing_date pulled from structured_data, key_points, and page_classifications.
Code snippet displayed in a terminal-style window with green background. The Python code shows a call to doc_ai.parse_and_wait passing arguments: file=filing_url, structured_extraction_options=structured_extraction_options, and page_classifications=page_classifications, with the result assigned to a variable named result.
Screenshot of a Tensorlake blog post titled Accelerate Advanced RAG with Tensorlake by Dr. Sarah Guthals, dated August 19, 2025. The header image reads ‘RAG isn’t dead, undisciplined retrieval is’ with a green wave design and Tensorlake logo. The page shows the TL;DR summary emphasizing the need for a retrieval plan over naive Top-N cosine RAG, and a table of contents with sections on the Freshness Principle, Accelerate Advanced RAG, a real-world application fact-checking Tesla SEC filings, and treating context as a hard requirement.
Bright green graphic with the Product Hunt logo featuring a cat wearing Google Glass at the top. Below it, large bold black text reads: “We Are Live on Product Hunt.” At the bottom of the image is the Tensorlake logo and name in white.
Bright green graphic with the Tensorlake logo at the top. Below it is the iconic Product Hunt cat wearing Google Glass, surrounded by a black circle. The text beneath the cat reads: “We are live on Product Hunt” in bold, black uppercase letters.