Meibel is an AI platform that turns your most complex document work into structured, reliable data, then puts that data to work through AI agents that automate, answer, and prove their results. Deploy Meibel in your own AWS account, with confidence scores and source traceability on every output.
Documents Become Data. Data Becomes Agents.Meibel is an AI platform that turns your most complex document work into structured, reliable data, then puts that data to work through AI agents that automate, answer, and prove their results.
What Meibel Does
Your most valuable business data lives in documents that were never designed for automation: PDFs, scanned files, contracts, specifications, carrier statements, and legacy archives. Meibel processes them completely. Tables stay tables. Charts become data. Handwriting is recognized alongside typed text. Document Intelligence, the platform's data foundation, ingests 25+ file formats and produces structured, searchable, queryable knowledge, complete with confidence scores and source traceability on every output.
On that foundation, you build agents: AI units of work that extract, validate, analyze, answer, and route. Agents connect to three input layers: your documents, your structured data (databases, spreadsheets, CSVs), and your external tools and systems. Every agent execution is reproducible, every output is scored, and every answer traces back to its source. The same agent that processes one document interactively can process millions in batch, with no code changes.
Built to Run on AWS
Meibel deploys as fully managed SaaS or as Bring-Your-Own-Cloud inside your own AWS account. In the BYOC model, a control plane and data plane architecture keeps your data inside your AWS environment. It never leaves. On-premises deployment is also available for maximum data sovereignty. Meibel connects to the AWS data sources your business already runs on, including S3 and the databases and enterprise systems behind your workflows, and every capability is available through an API-first architecture.
Who Benefits
Engineering, platform, and AI/ML teams building AI features that need to work in production, not just in demos
Product leaders who need AI output quality they can measure and manage
Operations leaders and business owners in document-heavy industries such as insurance, construction, manufacturing, financial services, and legal, where manual document work drains time and budget
Compliance and risk stakeholders who need every AI decision to be traceable, reviewable, and auditable
System integrators and services firms delivering AI solutions across many clients without rebuilding infrastructure each time
The Problems Meibel Solves
Data locked in documents: Terabytes of knowledge sitting static in PDFs and archives that search tools and generic AI cannot reliably read
Extraction that fails on real documents: Most out-of-the-box tools flatten tables, miss handwriting, and lose the structure that makes data meaningful
No way to verify AI output: Without a quality score on every result, teams either review everything manually or trust everything blindly. Neither scales.
Fragile, expensive pipelines: Teams stitch together a document parser, a vector database, a workflow engine, a tool router, an observability system, and an evaluation framework, then struggle to maintain consistency.
Why Meibel
One platform replaces six stitched-together tools. Confidence scoring is built into execution, so high-confidence results flow to automation and low-confidence results route to human review. Deployment options include SaaS, Bring-Your-Own-Cloud on AWS, and on-premises. Customers have cut document processing from minutes per document to seconds, and moved from proof of concept to production in weeks, not quarters.
Highlights
Complete document processing, not just text extraction. Meibel turns complex PDFs, scanned files, contracts, and specifications into structured, queryable data. Tables stay tables, charts become data, and handwriting is read alongside typed text across 25+ file formats.
AI agents that automate, answer, and prove their results. Build AI agents on your documents, structured data, and existing tools. Every execution is reproducible, every output carries a confidence score, and every answer traces back to its source, so high-confidence results flow to automation and low-confidence results route to human review.
One platform, deployed in your own AWS account. Meibel replaces six stitched-together tools (document parser, vector database, workflow engine, tool router, observability, and evaluation) with a single AI platform. Deploy as managed SaaS or Bring-Your-Own-Cloud on AWS, where your data never leaves your environment.
AWS Marketplace now accepts line of credit payments through the PNC Vendor Finance program. This program is available to select AWS customers in the US, excluding NV, NC, ND, TN, & VT.
Pricing is based on the duration and terms of your contract with the vendor, and additional usage. You pay upfront or in installments according to your contract terms with the vendor. This entitles you to a specified quantity of use for the contract duration. Usage-based pricing is in effect for overages or additional usage not covered in the contract. These charges are applied on top of the contract price. If you choose not to renew or replace your contract before the contract end date, access to your entitlements will expire.
Additional AWS infrastructure costs may apply. Use the AWS Pricing Calculator to estimate your infrastructure costs.
This private offer bills through two dimensions, both measured in US dollars. The Usage dimension is metered: you pay for the work your agents actually run, such as agent actions, document pages processed, fields extracted, stored memory, data ingestion, and query tokens. Usage accrues based on what you consume, with no fixed quantity. The Platform Fee is a separate charge that covers access to the platform itself. Together, the two dimensions split your cost into ongoing metered consumption and a base platform charge, so spending scales with how much you process.
Top-of-mind questions for buyers
What counts as one agent action for the metered usage charge?
Each discrete step an agent takes counts as one action. That includes a decision, a tool call, or a confidence score. Simple tasks use a few actions; complex ones use more. You pay only for the steps that actually run.
How do the different usage metrics combine to form my total bill?
The Usage dimension bundles several metered items that bill independently and add together: agent actions, document pages, extracted fields, stored memory, data ingestion, and query tokens. The mix that dominates depends on your workload. Document-heavy work leans on page and field charges; query-heavy work leans on token charges.
Are outside AI model costs included, or billed separately?
When your agents call an outside model, that provider cost passes straight through with no markup. Meibel's own query token rates cover only the models it runs for you. These pass-through provider charges fall within your metered Usage dimension.
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