Turn documents and voice into structured intelligence
Invoices, contracts, medical records, call recordings — extracted, structured, validated, and delivered into your business systems.
At a glance
PaperIQ.ai (Pomsoft LLC) is a multi-tenant business web application for document and voice intelligence. It uses multimodal models to recover tables, charts, and layout-aware structure from visually rich PDFs—not only plain text—and supports voice-centric workflows described across this site. Outputs can be checked against tenant-defined JSON Schema during generation where configured, aiming for records that downstream systems can accept without brittle post-processing. Extractions route into spreadsheets or databases and can integrate with CRMs and accounting tools through exports and optional Model Context Protocol (MCP) tool registration rather than stopping at conversational summaries alone. Typical buyers are operations and platform teams in document-heavy sectors such as contracts and leases, professional services, regulated paperwork, and organizations with large call-recording archives. The public story highlights tenant isolation, modern access controls, and paths to use customer-controlled models and keys when required; detail lives on the Security page. Primary entry for evaluation is self-serve signup from calls to action on this page.
Concise machine-readable overview for assistants: llms.txt
Except most of that data is trapped.
PDFs lock content in visual formats. Voice recordings sit unsearchable in your PBX. And text-only extraction misses the parts that matter most.
What you lose with copy-paste
Charts and diagrams — invisible. You get nothing.
Table structures — flattened into meaningless lines of text.
Spatial relationships — the layout that gives data meaning disappears entirely.
Voice recordings — completely unsearchable, un-analyzable audio files sitting in your PBX.
The cost of doing nothing
Manual data entry — someone re-types what the document already contains.
Incomplete AI context — your LLMs make decisions on partial information.
Lost institutional knowledge — years of call recordings with zero searchability.
Integration gap — data stays in documents instead of flowing into your systems.
So we built AI that sees documents the way you do
Multi-modal vision models that read text, interpret charts, parse tables, and understand layouts — simultaneously
Vision + Text
AI models that "see" and "read" simultaneously — charts, tables, diagrams, handwriting — nothing is missed.
Context Preserved
Spatial relationships, visual hierarchies, and table structures survive extraction — your LLMs get complete context.
Any Model, Your Choice
Ollama locally, OpenAI, Anthropic, AWS Bedrock, or Google Gemini. Use your models, your keys, your infrastructure.
AI is useless if the output doesn't fit your database.
If you have a pile of leases, contracts, or voice call transcripts, you don't need a "summary." You need structured data that fits your existing systems.
Tell the AI what you need. It builds the schema for you.
No JSON knowledge required. Describe what you want in plain English — our AI helper generates the schema, then validates every extraction against it as it writes.
Describe What You Need
"I need the tenant's full name, unit number, monthly rent amount, security deposit, lease start and end dates, whether pets are allowed, the landlord name, and any special clauses or addendums."
Upload Your Leases
unit_4B_lease.pdf
smith_renewal_2025.pdf
sublease_agreement.pdf
...any document type
Get Valid JSON
{
"tenant_name": "John Smith",
"unit_number": "4B",
"monthly_rent": 2500,
"security_deposit": 2500,
"lease_start": "2024-01-01",
"lease_end": "2025-12-31",
"pets_allowed": false,
"landlord_name": "Greenfield Properties",
"special_clauses": ["No subletting",
"60-day renewal notice"]
}
✓ Schema validatedNo post-processing. No correction loops. No cleanup scripts.
Structured output stays aligned with your schema while it is produced — so you can trust it for production systems.
From a junk drawer of PDFs to a clean spreadsheet.
One click turns hundreds of extracted records into a structured SQLite database you can open in Excel, Google Sheets, or any BI tool.
lease_4B.pdf → JSON ✓ lease_7A.pdf → JSON ✓ lease_12C.pdf → JSON ✓ sublease.pdf → JSON ✓ renewal.pdf → JSON ✓ ...47 more documents
Hundreds of documents, each extracted and validated
| tenant_name | unit | rent | lease_end |
|---|---|---|---|
| John Smith | 4B | $2,500 | 2025-12-31 |
| Maria Garcia | 7A | $1,800 | 2026-03-15 |
| David Chen | 12C | $3,200 | 2025-09-01 |
| ...49 more rows | |||
Structured, sortable, queryable — ready for Excel or Google Sheets
Your schema becomes your columns. Your documents become your rows.
Every field you defined — tenant name, rent amount, lease dates, pet policy — becomes a column in a clean database table. Export once, or set it up to accumulate as you process more documents. Open in DB Browser, Excel, or connect directly to Power BI.
Spreadsheets are a great start. But what about real-time integrations?
You exported 500 leases to Excel. Now your accounting team needs that data in QuickBooks. Your ops team needs it in Salesforce. You need it to flow, not just sit in a file.
Our AI doesn't just extract — it acts.
MCP tool calling lets the AI push extracted data directly into your databases, CRMs, and business systems. Zero manual entry.
Extract
AI identifies invoice amounts, contract terms, patient data, or any fields your schema defines — structured and validated.
Connect
Register your own MCP servers. Our AI discovers your tools dynamically — no hardcoded integrations, no restarts. PostgreSQL, QuickBooks, Salesforce, your custom APIs.
Automate
Upload an invoice PDF. AI extracts vendor, amount, line items. Data lands in your accounting system automatically. Done.
Need invoice processing specifically?
We built a specialized invoice processor on top of PaperIQ.ai — multi-modal AI that sees scanned invoices, extracts all line items, and sends data to QuickBooks, Xero, and ERPs.
“This sounds great — but can I trust it with sensitive documents?”
Contracts with client names. Medical records. Financial data. If you're putting documents through AI, you need to know exactly who can see them.
Your data. Your models. Your control.
Multi-tenant isolation ensures your documents and MCP servers are completely invisible to other tenants
Complete Tenant Isolation
Every document, job, MCP server, and token is scoped to your tenant. Database-level filtering — zero cross-tenant access.
OAuth 2.1 + Zero Trust
Server-specific tokens, URL-based audience validation, PKCE, client pre-registration. Every request authenticated independently.
Run Your Own Models
Process locally with Ollama, or use your own API keys for OpenAI, Anthropic, Bedrock, Gemini. Your data never has to leave your infrastructure.
Documents in. Structured data out. Automated.
From trapped content to database-ready intelligence, with zero manual entry and enterprise-grade security.
Multi-Modal Vision
Schema Validation
MCP Automation
Your Data, Your Control
Free to get started · No credit card required · Connect your own databases via MCP
Guides and use cases
Pillar articles on schema-validated extraction, MCP, and RAG—plus vertical workflows for invoices, leases, contracts, and call archives.
JSON Schema for Real-World Documents
Why schema-at-generation beats post-hoc cleanup for invoices, leases, and regulated forms.
MCP for Business Data Automation
Connect schema-validated JSON to downstream systems with tenant-scoped MCP servers.
Invoice Extraction to Validated JSON
Commercial Lease Abstraction to Portfolio JSON
PaperIQ.ai
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Pomsoft LLC
AI-powered document and voice recording intelligence platform.
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