Stop parsing with prompts
Use models for judgement and reasoning, not routine document extraction.
File intelligence for AI agents
Turn file chaos into agent-ready data.
Why burn an LLM call on parsing? vena8 lets agents inspect, extract, filter and retrieve the parts of complex files they actually need.
Inspect firstMap documents, emails, archives and embedded items before expanding them.
Agent speedSkip slow parsing prompts and hand the model usable context.
Export optionalReturn JSON by default, or produce text, assets, tables and Parquet-ready data.
The problem
Agents should reason over files, not fight them.
Most agentic workflows still waste time getting files into a usable shape. vena8 moves extraction out of the prompt and into a purpose-built API that exposes the internal structure of files before the agent decides what to read next.
Normalise messy inputs
Different file types become a stable data model: files, text, tables, assets, items, metadata and errors.
Expose hidden content
Surface embedded files, images, tables, emails, folders, attachments and metadata before the agent acts.
Fail cleanly
When something cannot be processed, the workflow gets a structured reason instead of bad context.
The output
A data model your agent can actually use.
Send a file. Get back a map of the content: clean text, document structure, tables, assets, embedded items, metadata and searchable chunks.
The important part: this is not limited to one output format. JSON is the control plane; selected content can later be returned as text, extracted files, table rows, chunks, or Parquet-ready data.
PDFDOCXXLSXPSTEmailsArchivesImagesParquet-ready
{ "file": { "name": "claims-mailbox.pst", "type": "container", "bytes": 894120392 }, "content": { "text": "available", "tables": [/* sheets, CSVs, extracted tables */], "items": [/* emails, attachments, nested files */] }, "query": { "filterable": ["sentAt", "from", "folder", "hasAttachments"], "expand": "on_demand" }, "exports": ["json", "text", "assets", "parquet"] }
Embedded data
Inspect first. Expand only what matters.
Large containers should not explode into one giant response. vena8 can index nested content first, then let agents filter by type, date, folder, sender, attachment name, table column or other structured fields.
Email archives
Find messages by sent date, sender, subject, folder, attachment type or conversation before extracting bodies and attachments.
Nested files
List archive contents, skip irrelevant binaries, and recursively process selected documents only when needed.
Structured data
Expose sheets, columns and detected types so workflows can query rows or export clean table data.
Selective retrieval
Ask for metadata first, filtered items second, and full text or assets only for the content that is relevant.
Built for agents
Predictable enough to automate against.
The API is designed for machine consumption first: stable fields, repeatable extraction, selective expansion, composable exports and first-class errors.
Stable schemas
The same concepts across formats, so workflows do not drift with inputs: files, items, tables, assets and errors.
Repeatable extraction
Same file in, same result out. The parsing layer does not need creative interpretation.
Pipeline friendly
Push selected data into RAG, search, analytics, Parquet conversion or downstream tools.
Honest failures
Structured failure states keep agents from confidently acting on broken or partial context.
Early access
Make file extraction the boring part of your agent workflow.
vena8 turns unpredictable file inputs into structured, queryable data that agents can trust, route, filter and reason over.