Polyvia
Polyvia · Image · Coding
Polyvia is a multimodal document retrieval API built for developers who need AI agents to reason over large document sets. It runs files through a visual extractor, a knowledge ontology, and a self-improving retrieval agent, so you can ask one question across PDFs, charts, slides, scans, and audio and get an answer tied back to the exact page. The company ships it as two products: the Polyvia API for agent builders and the Polyvia Platform for knowledge workers in enterprises.
About Polyvia
What Is Polyvia
Polyvia solves a specific problem that shows up once your documents stop fitting in a normal workflow. Agentic, file-by-file search tools like Claude Code or Codex work fine up to around 100 multimodal files. Past that, they get slow. At enterprise scale you still need real retrieval infrastructure. Polyvia is that retrieval layer, sold as an API first.
Think of this as visual document intelligence for teams that have outgrown keyword search. Connect your unstructured and visual documents, then query them in plain language and get answers with citations. The company behind Polyvia describes the engine as an end-to-end pipeline rather than a stack of separate parsers and extractors you have to wire together. That matters if you've ever tried to bolt a PDF parser onto a vector database and watched citations fall apart on tables and charts.
The biggest limit is scope. Polyvia is a developer and enterprise tool, not a consumer app. It's marketed mainly at teams connecting large internal datasets, and enterprise deployment happens inside your own cloud or VPC through Polyvia for Enterprise. So who should skip it? If you just need to summarize a handful of personal PDFs, this is more machinery than you need.
Getting Started
- Create an API key in the Polyvia Platform by opening the API tab in the sidebar and clicking Create API Key. Keys start with
poly_and are shown once. - Install the SDK with
pip install polyviafor Python ornpm install polyviafor TypeScript. - Ingest documents as a batch, scoped to a named group, then wait for each ingestion task to finish.
- Query the group in natural language and read the answer, which cites the exact page inside each source document.
- Optionally connect Polyvia to Claude, Cursor, or another MCP client to let an agent call retrieval directly.
Product Information
A quick look at Polyvia's pricing, supported platforms, and performance.
Best for
The users, tasks, and scenarios where this tool fits best.
Users
- AI agent developers
- Data and finance teams working in enterprises
- Knowledge workers in the Polyvia Platform
Tasks
- Cross-filing KPI comparison
- Data-room due diligence
- Image-based claim processing
- Reading charts and tables
Scenarios
- You've outgrown file-by-file search
- Compliance and audit work
- On-prem requirements
Key features
Multimodal retrieval, not just text search
Polyvia indexes PDFs, DOCX, PPTX, TXT, Markdown, images, and audio into the same knowledge graph, and calls the result visual document intelligence. It reads tables, charts, infographics, handwriting, and multi-column layouts rather than only the text layer, which is where most document indexing tools quietly fail on real-world filings. Text search alone won't cut it.
Cited, paragraph-level answers
Every answer comes back with a citation to the source page, and the company reports 99.8% citation coverage. That's the point. For anyone doing due diligence or audit work, cited output is the difference between a useful tool and a liability. You can check the claim yourself instead of trusting the model. No blind trust required.
Fast at scale
Polyvia claims sub-200ms query latency over 100K+ files, against minutes for file-by-file agent search. That speed gap is the whole argument for adding a retrieval layer once a corpus gets large. Big difference at scale. The numbers come from the vendor, so treat them as their claim, not an independent benchmark.
Built for developers
The API ships with Python and TypeScript SDKs, both with sync and async clients, batch ingestion, idempotent groups, and structured error handling. There's also an MCP server and agent skills, so Polyvia plugs into Claude, Cursor, and LangChain-style frameworks without a custom adapter. Less glue code to write.
Native integrations
You can import documents from Google Drive, Dropbox, OneDrive and SharePoint, Amazon S3, Notion, and Slack. Notion and Slack use per-user OAuth, so each teammate connects their own account, and all sources land in one Import dropdown. One place to pull files from.
Enterprise deployment and security
Polyvia for Enterprise runs the agents inside your own cloud or VPC, so documents stay within your perimeter. Your files never leave. That's the option that makes the product viable for teams with strict data-residency rules, who'd otherwise have to rule out a hosted API entirely.
Knowledge graph exploration
The Platform includes a graph view showing entities, claims, and evidence extracted from your documents. You can filter by group and click any claim to jump to its source page, which turns the index into something you can actually browse.
Pros and cons
Pros
- Handles multimodal documents, including charts, tables, and scanned pages, that trip up text-only retrieval.
- Paragraph-level citations on every answer make it usable for compliance and audit workflows.
- SDKs, MCP support, and agent skills mean low integration overhead for agent builders.
- On-prem and VPC deployment covers teams that can't send files to a hosted service.
- Reported sub-200ms latency at 100K+ documents addresses the scale ceiling of file-by-file search.
Cons
- Public pricing isn't published on the site, so you have to contact the company to size a budget.
- It's an API and enterprise tool, not a consumer app, so casual users are better served elsewhere.
- Some advertised modalities, including CAD, video, and heatmaps, are still on the roadmap and not live yet.
- If you only work with a small set of documents, the retrieval layer adds complexity you don't need.
Frequently asked questions
It indexes your documents, including visual and multimodal ones, and gives you a retrieval endpoint you can query in plain language. Answers come back with citations to the exact source page, so you can verify each claim.
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