From Sketchy Data to Trusted AI: Fresh Context Meets Field-Level Citations

Let’s be real: AI systems only work when they work accurately. And a lot of our customers in high-stakes industries, like financial services and healthcare, need to trust that the structured output is not only accurate, but verifiable.

Across the product, our content, and supporting our customers, our mission is to provide data that you can trust. With our newest release of the Citations options, your results have fewer hallucinations and you have more quality control through verifiable structured output.

🆕 Verifiable Structured Output with Citations

If there’s one feature I’ve been waiting to share with you, it’s this: Citations are here 🎉

It’s not good enough to build workflows where the answer you get is “the balance is $425,372,” and you just have to take it on faith. With no verification, how can you know if the data was hallucinated? Or pulled from the wrong page? Or maybe even a typo? You don’t know, and that’s not good enough.

With field-level citations in Tensorlake, every extracted value comes with a receipts-level audit trail:

• Exact page number where it was found

• Bounding box coordinates pointing to the precise spot in the PDF or scan

That means every answer is provable, traceable, and defensible. Whether you’re reconciling accounts, investigating fraud, or processing healthcare referrals.

This isn’t just a new parameter; it’s the foundation of trustworthy AI document workflows.

No more “black box” parsing. No more hand-checking outputs. Every field is now backed by the original source.

This is the difference between
“the AI says so” and “here’s the evidence.”

Learn more in our blog post, notebooks included.

Trust through Correct and Fresh RAG Context

“Top-N cosine similarity” might be charming for demos, but in production it’s brittle. It ignores structure, mishandles tables, and often pulls stale or irrelevant context.

That’s why we focus on tools for Advanced RAG techniques. Customers tell us they need more than embeddings. They need workflows that are reliable, explainable, and audit-ready. What we need is RAG that is more advanced, that includes:

• Metadata filters to control freshness and relevance.

• Hybrid retrieval that blends semantic + structured search.

• Reranking for sharper, contextually relevant answers.

• Citations to verify every answer against the source.

• Idempotent vector store updates so your indexes stay consistent as documents change.

Tensorlake supports advanced RAG techniques in two critical ways:

1. Extracting correct data from documents that have complex formats, are dense with information, and have varied fragments (e.g. images, tables, text). We support file types from images and PDFs to raw text, CSV/spreadsheets, and even presentations. We preserve even complex layouts, accurately extract from tables containing over 1,500 rows spanning across pages, and even provide citations for auditing. Correctness is at our core.

2. Delivering your completely parsed documents as structured, page-aware fragments, full markdown parses, page classifications, structured output, and additional metadata like summaries of tables and figures - all in a single API call.

When backed by Tensorlake, your agents always have the complete and correct context, keeping answers sharp and citeable while keeping toekn usage to a minimum.

Learn more in our blog post, notebooks included.

Latest Integrations

Chonkie x Tensorlake

• Learn how Tensorlake parsing + Chonkie chunking fix broken context in RAG, turning 100-page PDFs into clean, structured, LLM-ready inputs. Read the blog.

• Try it yourself with a Colab notebook, combining Tensorlake + Chonkie to get reliable context for your RAG workflows.

Tensorlake TL;DR

Structured Output Citations

• Field-level traceability: Every extracted value now includes page numbers and bounding box coordinates, so you can prove exactly where it came from.

• Audit-ready outputs: Build workflows that are transparent, defensible, and production-safe across finance, healthcare, and compliance.

• Watch a short video on how to enable provide_citations and see citations in action.

• Read the announcement (colab notebook included)

Table Recognition now parses ~1,500-cell tables

• Robust large-scale parsing: Tensorlake now handles ~1,500-cell tables (even in scanned PDFs) while preserving header hierarchy, row/col spans, and cell boundaries.

• Faithful structured outputs: Reliable HTML/CSV exports with bounding-box citations for end-to-end traceability.

• Read the blog post on how Tensorlake makes particularly dense tables useable in RAG.

• Learn more on our Changelog (colab notebook included)

Why Trust Matters for developers

• Decisions ride on the data. In finance, healthcare, or legal tech, a single wrong field can derail compliance, cost millions, or put lives at risk. Trust means knowing your inputs are solid before acting on them.

• AI is only as good as its evidence. When answers aren’t backed by verifiable sources, teams spend hours second-guessing and re-checking outputs, slowing down the very automation they hoped to achieve.

• Transparency builds adoption. Leaders won’t greenlight AI in production until they can prove where the numbers came from. Traceability turns skeptics into advocates.

• Scale demands reliability. A process that works on one document but breaks on page 86 of 1,000 won’t survive in production. Trust means consistency across edge cases, layouts, and formats.

• Trust compounds. Every reliable parse, every citation-backed extraction, every faithful answer increases confidence, not just in Tensorlake, but in AI workflows overall.

Trust isn’t built with promises, it’s built with proof. Let’s make it stick.

—Sarah and the Tensorlake Team