Multimodal Retrieval API

Most-accurate Multimodal Retrieval Infrastructure with Semantic Memory — for your AI agents

$pip install polyvia

Core Engine

Capabilities of Polyvia

Organizing scattered multimodal and visual data into a unified knowledge graph queryable by AI agents.

Structured output

From visual documents to structured facts

~80% of enterprise knowledge lives in visual form — charts, slides, financial reports, complex tables. Polyvia extracts the actual data, not just descriptions. "Revenue of Apple | Q4 2025 | $10.67B | bar chart | page 6" — not "this chart shows revenue."

14 data points extracted Confidence: 99.8%

Ontology Knowledge Graph

Facts connected, not scattered

Every extracted fact linked by entity, time period, source, and metric type. Cross-reference EBITDA across 500 counterparty reports in sub-50ms. The graph compounds — every new document enriches the entire corpus.

Audit-ready answers

Every answer traced back to source

Every response grounded in verifiable visual evidence — document, page, section, bounding box. Your agents produce answers teams can actually trust and audit.

Which segments show the fastest growth?

Cloud services led growth (+42%) driven by enterprise usage expansion. cite: 10-K p.42

Consumer subscriptions rose 18%, with Asia-Pacific up 31% year-over-year. cite: Deck p.7 cite: 10-K p.58

Ask across files...⏎

Built for scale

From 5 files to 50,000 documents

Drag-and-drop into ChatGPT works for 5 files — not 5,000. When you don't know which documents contain the answer, you need an infrastructure layer that organizes data before it reaches your AI agent. That's Polyvia.

50,000+

Documents indexed

50ms

Query latency

2,847

Facts per corpus

99.8%

Extraction confidence

Developer experience

Three ways to use Polyvia

TRY IT NOW

Agent Skills Python Node.js

$pip install polyvia

Python & JS SDKs Python

REST API Python SDK TypeScript SDK

# pip install polyvia

from polyvia import Polyvia

client = Polyvia(api_key=API_KEY)

# Batch ingest & wait for indexing

batch = client.ingest.batch(

["q4.pdf", "q3.pdf", "10k.pdf"],

group_id="g_earnings",

)

for item in batch:

client.ingest.wait(item.task_id)

# Query scoped to a group

answer = client.query(

"Compare EBITDA across all filings",

group_id="g_earnings",

).answer

MCP Server JSON

{ "mcpServers": { "polyvia": { "type": "http", "url": "https://app.polyvia.ai/mcp", "headers": { "Authorization": "Bearer poly_" } } } }

Polyvia Studio Web

// No code needed

1. Sign up at

app.polyvia.ai

2. Upload PDFs, slides

& reports

3. Search with

natural language

4. Get cited answers

across your corpus

Integrations

Works with your stack

AWSS3

GoogleGoogle

SnowflakeSnowflake

SharePoint

CRM

ERP

ClaudeClaude

CursorCursor

NotionNotion

Slack

Dropbox

OpenAIOpenAI

🛡 Enterprise-Ready

Polyvia for Enterprise

Same product, on your infrastructure. Private deployment with direct integrations and full data control.