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DeepTable

an API that converts messy Excel files into structured data

Details

External ID
47588776
Source
HN
Company
—
Product
DeepTable
Website domain
deeptable.com
Launched
March 31, 2026
Cohort
—
Upvotes
8
Upvotes percentile
0.491389913899139
Tags
—
Fetched at
Sept. 7, 2026, 9:26 p.m.
Updated at
Sept. 7, 2026, 9:26 p.m.

Description

We tried to build an Excel error checker. To achieve that, we needed to actually understand the semantic structure of a spreadsheet first. So we built that, and it turned out to be the harder, more general problem.The core issue: most real-world spreadsheets aren't relational tables. Merged cells, multi-level headers, multiple tables per sheet, totals mixed in with data. You can't just dump them to CSV and call it done. LLMs handle the easy cases but fall apart on complex workbooks at scale.Our approach uses an agent-guided compilation pipeline that produces SQL-ready relational tables with full cell-level provenance. This demo visualizes what we do: https://storage.googleapis.com/deeptable-public/deeptable_an...We have a handful of early customers but honestly don't know yet whether this is a real market or a niche problem. We're posting this to hear from people who've dealt with arbitrary spreadsheet ingestion. Whether you solved it, gave up, or are still living with the pain.If you want to try it on your own files, email me (see my profile for my email) and I'll give you API access.

Enrichment

Theme
spreadsheet and tabular data tools
Vertical
Horizontal
Function
Data infrastructure
Audience
B2B
AI stance
AI feature
Project type
Commercial product
Normalized one-liner
api to convert excel files to structured data
Manually corrected
False

Could you build this?

Partial A web wrapper or simple spreadsheet converter is trivial, but accurately resolving arbitrary non-relational spreadsheet topologies (merged cells, hierarchical headers, multi-table sheets) into normalized relational data is an open computer vision / heuristics problem.

What it would actually take: The core backend requires an ingestion pipeline (Python/OpenPyXL/Rust calamine) that extracts cell coordinates, styles, formulas, and merged ranges into an intermediate graph representation. The difficult component is the semantic decomposition engine, which must use layout analysis algorithms or fine-tuned vision/table-transformer models to segment multiple implicit tables, identify nested multi-level headers, and handle ragged rows. Training and maintaining such parsing models across thousands of edge-case corporate spreadsheet formats requires specialized document AI and data pipeline engineering.

Discussion

No comments on this launch.

Competitors

Other products that read as similar to this one — 127 launches clear the similarity bar, closest 8 shown.

Attention rank: #60 of 128 (itself plus its competitors, highest first — normalized so YC and Product Hunt are compared fairly).

Launched 151 days after the earliest competitor.

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