Astera ReportMiner Knowledge Hub
Everything about Astera ReportMiner, an AI-powered data extraction solution, from common use cases, pricing, features, integrations, to real customer results.
Product Overview
Astera ReportMiner is a no-code, AI-powered data extraction software that automates the ingestion, extraction, validation, and delivery of data from unstructured and sem-structured documents. It extracts data from PDFs, text files, scanned files, images, Excel files, Word documents, and emails in to structured format that flows directly into databases, ERP systems, CRM platforms, and BI tools. It combines template-based extraction with AI-driven extraction, so it handles both consistent, repeating layouts and documents whose structure varies from file to file.
Astera ReportMiner automates the full data extraction pipeline. It ingests documents from folders, FTP servers, or email inboxes, extracts data using AI or reusable templates, applies built-in OCR for scanned documents, validates and transforms the extracted data, and delivers it to downstream destinations such as SQL Server, Snowflake, Salesforce, or Excel. The entire workflow is built through a visual point-and-click interface without writing code. The entire sequence, from ingestion to delivery, can run on a schedule or trigger automatically when a new file arrives.
Astera ReportMiner is classified as data extraction software and unstructured data management software. It also serves as an intelligent document processing (IDP) platform because it combines OCR, AI-driven extraction, validation, and workflow orchestration in one tool.
Businesses that process high volumes of documents from external parties use ReportMiner, including accounts payable teams handling supplier invoices, manufactures extracting purchase orders, healthcare organizations processing claims and lab reports, or logistics companies processing shipping documents, bills of lading, packing slips. More broadly, any business that needs to pull information out of documents, scans, or images of varying layouts and formats, is a fit. The interface is built for business users rather than developers, so teams without a technical background can build and run extraction processes very easily.
No. ReportMiner uses a point-and-click interface for building extraction templates, setting up transformations, and mapping extracted fields to a destination. To build an extraction template, users identify data patterns visually on the document itself rather than writing extraction scripts. In recent 11.2 version, AI-driven template builder has completely eliminated the need for manual template building effort, where AI agent is generating templates in mere seconds.
Building a custom extraction system requires OCR integration, AI model selection, template management, validation logic, scheduling, error handling, and connectors to every destination system. ReportMiner ships with all of these in one platform. Organizations save the engineering investment and ongoing maintenance, and business users can configure new document types themselves without filing IT tickets.
Unstructured documents have no consistent schema. The same field, for example, "invoice total", can appear in different positions, under different labels, in different layouts depending on the sender. Scanned documents add an OCR layer where image quality and font variation create additional errors. Hierarchical relationships (line items inside an invoice, transactions inside a bank statement) need to be preserved, not flattened. ReportMiner addresses each of these challenges with a specific feature: AI templates for layout variability, OCR for scanned content, and hierarchical extraction for nested data.
A Report Model is a reusable extraction template. It tells ReportMiner where to find data on a source document and how to structure it. A Report Model is made up of Data Regions, sections identified by a repeating pattern that mark where the data to be extracted lives. Data Regions can nest inside one another to represent the document's hierarchy, like line items under an invoice. Each Data Region contains the Data Fields that capture the individual values.
Yes, Astera ReportMiner offers a free trial and does not require credit card details to start one. The free trial is activated for 7-14 days depending on the use-case and requirements. A continued use of ReportMiner requires a paid license, and volume discounts are available for multi-seat and enterprise-wide licensing.
Company and Origins
Astera Software is a privately held data management company headquartered in Westlake Village, California. It develops a portfolio of no-code data tools including ReportMiner (document data extraction), Centerprise (Agentic ETL), Data Stack (unified platform), DW Builder (data warehousing), EDI Connect (electronic data interchange), API Management, Dataprep (chat-based AI data prep), and Docspire (document management automation).
ReportMiner was created to solve a long-standing enterprise problem: business-critical data is trapped inside unstructured documents like invoices, purchase orders, and bank statements, and getting that data into databases or applications has traditionally required either manual data entry or expensive custom scripting. ReportMiner gives operations and business teams a way to process that data without code or developer time.
ReportMiner's latest version is 11.2, and it moves the product from straightforward extraction into fuller document intelligence: AI-powered Auto-Generate Layout (AGL) builds an extraction template in about five seconds instead of the ten-plus minutes it used to take manually, Text Converter now pulls searchable text from PDFs, Word files, images, and more with support for Google OCR, AWS Textract, Tesseract, and custom OCR models plus rotation and deskew correction for messy scans, and Smart Source maps varying file layouts to one consistent schema using AI-powered semantic matching. On top of that, three new Dataflow objects (SplitText, Build Embeddings, and Query Vector DB) let teams build a full retrieval-augmented generation pipeline natively, so the same platform that extracts a document can also chunk it, embed it, store it, and answer questions against it.
Astera Software was founded in 1995 with a focus on lightweight ETL and data mapping tools, and entered the broader data integration and document processing space around 2008–2009. The company's stated mission is to make enterprise data management accessible to business users through no-code, user-friendly interfaces.
Features and capabilities
ReportMiner's core features are:
AI-powered Auto-Generate Layout (AGL) for template creation in ~5 seconds
Template-less LLM Generate for documents with variable layouts
Built-in OCR with five engine choices
Visual point-and-click template builder
Real-time data preview during template design
Hierarchical extraction for parent-child document structures
Data validation rules with error, null, and warning reporting, error logs
Built-in transformations (200+ functions: type conversion, calculations, restructuring, merging, splitting, normalizing)
Workflow orchestration with scheduling and triggers
Parallel processing engine for high-volume workloads
Centralized job monitoring dashboard
Integration with excel, csv, json, xml, databases, ERP, CRMs, and 300+ connectors via native connectors, or API connections.
Extraction errors are cheaper to catch before data reaches downstream systems than after. Real-time preview shows the extracted output immediately as fields are defined, so users see exactly what will land in the destination. This reduces rework, improves data quality, and shortens the configuration cycle.
There are two paths:
Automatic:
upload a sample document, run Auto-Generate Layout, and ReportMiner produces a complete report model with regions and fields in about 5 seconds. Adjust as needed.
Manual:
open File > New > Report Model, select a sample file, and define data regions by specifying repeating patterns. Add fields by clicking on the document content, set their start position, length, and type, then preview the output before saving.
Either way, the saved .rmd file becomes a reusable template applied to all matching documents.
Validation runs as part of the extraction pipeline, not as a separate step. Users define rules, for example, required fields, valid ranges, type checks, or cross-field comparisons. As each document is processed, ReportMiner reports the number of errors, null values, and warnings per job. Built-in transformations clean the data (removing whitespace, unwanted characters, and noise) before it reaches the destination.
Extraction Templates and AI-Powered Extraction
You load a source document into the Report Model designer and mark the repeating pattern that identifies a Data Region, for example a label like "ACCOUNT:" that appears in every occurrence of it. ReportMiner highlights every matching occurrence in the document. From there, you add Data Fields inside the region, identifying each field's position, either by a fixed position or by a pattern such as a label it follows. Once configured, the template is ready to reuse on any file with the same layout.
Building a template manually typically takes 10 minutes or more, depending on document complexity. Using Auto-Generate Layout (AGL), Astera's AI-driven template builder, the same template can be generated in about 5 seconds.
AGL is a feature that uses AI to create an extraction template automatically with the click of a button. It gives users a head start, so instead of creating an extraction template from scratch, they can review and make changes using all the functionality available in the Report Model interface. It can generate a template on any document within seconds.
Yes. Astera ReportMiner supports hierarchical, parent-child structures by letting Data Regions nest inside one another. Repeating groups of records nested inside a parent, such as multiple line items under a single purchase order or multiple order items under one customer account, are captured with Collection Data Regions. ReportMiner preserves this parent-child structure and can export it to JSON, XML, or as separate related tables in a database.
Each parent record starts where its identifying pattern appears in the document, for example, every time "INVOICE #" shows up. Any line items found after that point, up until the next "INVOICE #," are treated as belonging to that invoice. This is how a repeating child, like line items, stays tied to the correct parent instead of spilling into the next one.
Yes, ReportMiner offers a few ways to handle this. Regex can be used in a Data Region's pattern, or in a Data Field's position when set to "Follows String" on the current or previous line, to match based on shape rather than a fixed keyword or column. A Floating Pattern lets a region's fields shift position on the line instead of staying at fixed offsets. And a single region can carry up to five pattern slots, so it can still match the same data when it appears in a few different formats within one document.
Yes, using an Append Region in report models. It captures data that lies outside the main data region, like a grand total line, and concatenates that data with the main region's output. It can sit either before or after the main region in the document.
Yes, Astera ReportMiner can calculate values during extraction through formula fields. A formula field uses an expression builder with over 200 built-in functions to calculate or derive a new value from extracted fields, for example computing a discounted price from an extracted list price and a discount percentage.
Yes, ReportMiner allows users to edit raw document text before building the extraction template using Edit Mode. Edit Mode provides find-and-replace, cut, copy, paste, undo, redo, and a revert-to-original option, letting you edit the source text before building the template. It's not limited to OCR output, correcting OCR noise or misread characters is just one common use case. The edited text is saved as a .txt file, and once you close Edit Mode, that .txt file replaces the original as the Report Model's source, so the template gets built against the edited text.
Yes, ReportMiner uses the PDF Form Source object to extract data from PDF forms with checkboxes. It's built for fillable PDF forms, PDFs with digital fields such as text boxes, checkboxes, and radio buttons. The PDF Form Source object detects these fillable fields directly and extracts their values, generating a corresponding field for each one that's ready to map to a destination.
Templateless extraction uses AI to pull data directly from a document without a pre-built Report Model, which makes it suited for documents where layouts vary a lot or unseen layouts are common, such as invoices from a new vendor whose format hasn't been seen before. Template-based extraction is the better fit for repeatable layouts, such as a handful of known vendors who each use their own consistent format.
ReportMiner’s templateless data extraction pipeline works in three simple steps. Text Converter, a dataflow object that extracts searchable, editable text from a document (using OCR if it's image or scanned), pulls the text out of the source file. LLM Generate, a transformation that connects the pipeline to a large language model, then applies a prompt to that text, using placeholders like {Input.field} to reference the input, to pull out the fields you want. The result can be parsed into structured fields via a parser or a smart source and can be written to excel, csv, database or any external system.
Can Astera ReportMiner extract data from documents that span multiple pages?
Yes, ReportMiner can process documents of any length with both template-based and templateless extraction. A Report Model matches patterns across the document's full text, so a region can occur as many times as needed across as many pages as the document has, and Page Header and Footer regions capture values that repeat on every page, like a running page number. In a templateless pipeline, Text Converter may be configured to split the document into one text record per page, LLM Generate then runs the prompt on each page, and its Union JSON Results setting merges the per-page results back into one coherent, parse-able output record.
OCR and Scanned Document Processing
Yes. When ReportMiner detects that a PDF is image-based or scanned rather than text-based, it offers to run OCR (Optical Character Recognition) before extraction, converting the scanned image into machine-readable text automatically.
ReportMiner supports five OCR engines: native heavy-duty and light OCR, Google Cloud OCR, Amazon Textract, and a custom option where you build your own flow that uses any OCR through API calls, such as an LLM model prompted to act as OCR. Users choose the engine that fits their accuracy, cost, or data residency requirements.
OCR accuracy varies significantly by document type, language, and layout. A single engine that works perfectly for English printed invoices may struggle with multilingual scanned forms, handwritten notes, or non-Latin scripts. Offering five engines means customers can route different document types to the best-fit engine instead of accepting whatever a single-engine vendor provides.
ReportMiner applies rotation correction and deskew as preprocessing steps, which straighten and correctly orient scanned pages before OCR runs, reducing misreads caused by skewed or rotated scans. Users can also set OCR Resolution to balance processing speed against accuracy.
Yes, ReportMiner can open and extract data from password-protected documents while preserving data integrity, provided the password is supplied in the Report Options panel or Text Convertor object by the users.
Text Converter is a dataflow component that extracts searchable, editable text from PDFs, Word documents, images, HTML, Markdown, TXT, Excel, and CSV files, using any of the five supported OCR engines when the source is image-based or scanned. The recognized text can then feed into either a templateless, AI-driven pipeline or a Report Model.
Yes. ReportMiner offers custom LLM-based OCR engine that processes handwritten content by interpreting the full page as visual context. Unlike traditional ICR software that isolates individual characters, the LLM-based path reads handwriting alongside surrounding printed text, field labels, and document structure, which produces significantly more accurate results on mixed printed-and-handwritten documents.
Supported File Formats and Data Sources
ReportMiner processes a wide range of formats:
Structured: XLS, XLSX, CSV
Semi-structured: XML, JSON, PARQUET
Unstructured text-based: PDF, PDF forms, TXT, RTF, DOC, DOCX, RPT, PRN, COBOL, EML, LIS
Image-based (via OCR): JPG, JPEG, PNG
Web formats: HTML, HTM, XHTML, Markdown
EDI: X12, EDIFACT, HL7
Scanned and image-based versions of these documents are handled through the built-in OCR engines.
Yes, ReportMiner uses the Email Source object in the dataflows to fetch data from emails. Email Source watches a specified email folder for incoming messages, downloads attachments automatically, applies filters to process only relevant emails, and can pass those attachments into an extraction pipeline.
Yes. ReportMiner connects to Microsoft Azure Blob Storage, Microsoft SharePoint, and Amazon S3 Bucket Storage as both sources and destinations for extraction jobs.
Yes. ReportMiner reads EDI Source Files and includes dedicated EDI Message Parser and EDI Message Serializer objects, along with EDI Acknowledgment handling in workflows, supporting formats like X12, EDIFACT, and HL7 for healthcare and supply chain transactions.[MA2]
Yes. A File System Items Source object provides metadata about files in a folder, and combined with the Context Info object and a workflow, ReportMiner can watch a folder and automatically run an extraction dataflow on every new file that arrives.
Mapping Inconsistent Layouts
For documents with inconsistent layouts, ReportMiner uses LLM Generate and Smart Source. Instead of building a separate template for each layout variation, the user writes a natural language prompt describing the fields to extract. For example, "extract the invoice number, vendor name, line items, and total amount." The LLM applies that prompt to each document regardless of where the fields appear visually. This approach scales to long tails of vendor-specific formats without manual template work. Then ReportMiner uses a Smart Source object to parse and map the extracted data into a consistent output schema.
Smart Source (the Smart Document Source object) parses and maps documents or serialized strings with different formats and field names into the same output structure. It's built for situations where multiple senders, such as different suppliers, each use their own field names and structure for conceptually the same data.
Smart Source uses four matching strategies, applied individually or in combination: Exact Match for identical field names, a Synonym Dictionary for custom mappings, Semantic Match using AI to align fields with similar meaning (for example, matching "Vendor ID" to "Supplier Code"), and dynamic mapping keyed by a document identifier such as a supplier ID.
A Synonym Dictionary File is a text file (with a .Syn extension) that stores alternate names for the same field, separated by pipes, for example mapping "ShippingID," "Shipment_ID," and "Shipment Number" all to a single "Shipping Number" field. Astera checks this file at runtime and matches whichever variant appears in the incoming source.
Smart Source adapts at runtime, using whichever of the three matching strategies you've enabled: Exact Match, Synonym Dictionary, and Semantic Match, applied in that order of precedence. If a layout change isn't caught by one enabled strategy, it falls through to the next one in that order, so with Semantic Match enabled, an unmapped or unseen field name can still resolve without needing a redefined mapping. This means organizations onboarding new data sources on a recurring basis rarely need to manually rebuild the mapping.
In a documented retail use case involving supplier invoices across a large vendor network, Smart Source reduced manual invoice field mapping by 80% to 90% and cut new supplier onboarding time from days to hours.
No, if AI Semantic Match is enabled in the Smart Source object, it sends only field names and structure to the language model doing the matching, not the underlying document content or data values. You can review the LLM call in the job trace to confirm no data was sent.
AI Features
LLM Generate is a drag-and-drop dataflow transformation that connects an extraction or transformation pipeline directly to a large language model, including OpenAI, Anthropic, Together AI, or a custom self-hosted model. A single configurable prompt, written once with placeholders for mapped input fields, can classify data, extract information, generate SQL from a natural-language request, summarize content, or enrich a dataset in the same extraction pipeline.
Yes. Astera offers three objects in the datafow toolbox to build a RAG pipeline: SplitText, which chunks incoming text; Build Embeddings, which generates vector embeddings for storage in a vector database; and Query Vector DB, which embeds a user's question and searches it against stored embeddings, with support for dense, sparse, and keyword vector search plus reranking for accuracy. This means the same platform that extracts a document can also chunk it, embed it, store it, and answer questions against it, without a separate RAG tool.
Yes, users can connect their own OpenAI or Anthropic key for AI-powered features, such as Smart Source and Auto-Generate-Layout, instead of relying solely on Astera's default key.
Data Quality, Transformation, and Export
Yes, ReportMiner supports automated data quality checks through Data Quality Rules, a dataflow object that applies custom business rules to incoming records and flags any record that fails a rule as an error, along with a descriptive error message. Quick Profile and Data Profile tools also show statistical summaries of extracted fields, including error counts, null counts, and warning counts.
ReportMiner and the broader Astera platform provide roughly 26 built-in dataflow transformations, ranging from basic operations like Aggregate, Sort, and Filter to advanced ones like Tree Join, Switch, and Normalize, for reshaping extracted data before it reaches its destination.
Yes. From a single Report Model, data can be exported to an Excel sheet, a delimited file, or a database table, and a Report Model can also feed into a Dataflow as a Report Source object to reach additional destinations including JSON, XML, and API endpoints.
Yes, through rule-based filtered export. An expression is written against the extracted fields (for example, exporting only records where a field contains a specific value), and only records matching that rule are sent to the destination.
Yes. The Data Preview window shows the entire extracted structure with actual field values before export, and a Preview Selected Export option shows what the output file will look like before the export job runs.
Integrations and Connectors
ReportMiner connects to SQL Server, Oracle, MySQL, PostgreSQL, MariaDB, SAP HANA, SAP SQL Anywhere, Snowflake, Amazon Redshift, Amazon Aurora (including the Postgres variant), Amazon RDS, Google BigQuery, Google Cloud SQL, Microsoft Azure SQL Server, IBM DB2, Netezza, Sybase, Teradata, Vertica, SQLite, and MongoDB as a source, along with any ODBC-compliant database.
Yes, ReportMiner can connect with Salesforce, Microsoft Dynamics CRM, Hubspot, Zoho, Sage, SAP, Zendesk, and more either via native connector or API connector.
Astera ReportMiner integrates with Oracle, NetSuite, Microsoft Dynamics 365, Sage, QuickBooks, Xero, CMIC, Airtable and 300+ additional systems through database connectors, API connectors, and webhooks.
Yes, Astera ReportMiner has native connectors for local folders, FTP/SFTP, Azure Blob, AWS S3, Sharepoint, MFT, Dropbox.
Yes. ReportMiner supports REST and SOAP web services for both consuming external APIs and publishing APIs of its own in a no-code visual canvas, with delivering extracted data through an API being one use case.
Yes, users can build a custom connector for any external application using the API Client object in Astera ReportMiner. It lets you connect to a source or destination that isn't covered by an existing connector by calling its API directly.
Process Automation and Scheduling Features
ReportMiner Enterprise Edition includes a built-in scheduler. Jobs can be triggered on a fixed frequency (hourly, daily, weekly, monthly), on file arrival in a monitored folder, on email receipt with an attached document, or manually on demand.
The workflow designer is a visual tool for chaining tasks together. Built-in tasks include FTP upload and download, file system actions, email send, SQL script execution, program execution, and the ability to run extraction, cleansing, and transformation jobs in sequence with branching and dependencies.
Real document workflows do not stop at extraction. A typical AP automation workflow has eight or nine stages: receive email, save attachment, run OCR, extract data, validate against PO, route exceptions to a human, transform for ERP format, load to ERP, send confirmation. Without orchestration, customers would need a separate scheduling tool to glue these stages together. Including orchestration in ReportMiner removes that integration tax.
Yes, ReportMiner can run extraction jobs automatically using the built-in Job Scheduler. Jobs can run on a recurring frequency (hourly, daily, weekly, or monthly) or in real time, triggered the moment a file arrives in a watched folder, an email inbox, or another monitored location. A job is equivalent to an extraction workflow for any document type.
Watch Email Inbox is a scheduling trigger, that starts a workflow automatically the moment a relevant email with an attachment arrives, so vendor or partner files get processed without anyone checking the inbox manually. Combined with the Context Info object, a workflow can also branch its logic based on the email's sender, recipient, subject, or body.
Yes. A centralized job monitoring dashboard shows all deployed jobs, users can generate server diagnostics and log files for troubleshooting with the click of a button. Alert emails can be configured to notify a team about abnormal terminations, task completions, or errors.
Yes, using the Send Mail workflow object, which can be placed at any point in a workflow, including conditionally after a branching object like an If/Else, so notifications only go out when a specific condition is met, such as a job failing or completing successfully. It supports up to five attachments per message.
Yes. Dataflows, workflows, and batch jobs can run on a single server or be distributed across multiple servers for load balancing, and multiple servers can be configured to share the same repository.
The processing engine uses parallel processing to handle multiple documents simultaneously. Workflow components can be executed concurrently, and performance scales roughly in proportion to available processing power. Native bulk-load support is available for popular databases. For very high volumes, the Enterprise edition supports unlimited parallel jobs, and multiple servers.
Deployment, Architecture, and Licensing
Both. ReportMiner can be installed on-premise with a client and server architecture, or deployed on Astera Cloud with a lightweight local client connecting to managed cloud infrastructure for execution, scheduling, and processing.ReportMiner can also be easily deployed on a private cloud for enterprises with data residency requirements.
Yes, ReportMiner supports Role-Based Access Control, which governs what different users can view, edit, or deploy within the platform, alongside options for Windows Authentication and Azure Authentication.
Yes. Astera ReportMiner supports Git-based version control for projects, including managing Git repositories, moving a repository to a remote server, and resolving Git conflicts between collaborators.
Projects deploy as a single version-consistent unit rather than as individual artifacts, which keeps every flow in a deployment aligned to the same version and reduces the risk of mismatches between environments. The catalog is enabled by default for every deployed artifact.
Support is delivered through email “support@astera.com”, via chatbot, knowledge base articles, FAQs, and teams channel. Documentation is available at documentation.astera.com. Customers consistently cite support quality as a strong point in third-party reviews on G2, Capterra, and Software Advice.
ReportMiner offers a transparent subscription-based licensing. It has multiple tiers, starting from an express version for power users that can build unlimited extraction templates and export structure data to excel. Followed by a standard tier with 4-core production server, 5 concurrent jobs, and 3 client licenses for automated document processing workflow. ReportMiner further offers Premium and Enterprise versions for heavy processing loads, and large team sizes of 30-100 client users.
Migrating From Monarch
Yes. ReportMiner includes a built-in converter that opens a Monarch .xmod file directly and converts it into a usable Report Model (.rmd file), so existing extraction logic does not need to be rebuilt from scratch.
Users can easily migrate dmods, xmods, JSON, dpwx, dprj to ReportMiner models, and flows via a conversion logic or an AI Migration Agent.
Monarch models store extracted data as appended, flat records. ReportMiner's Report Models use a parent-child hierarchy, which represents nested relationships, like multiple order items under one order, more directly than Monarch's appended structure.
A: To switch from Altair Monarch, our specialists run a guided migration with AI Agents that moves your extraction logic across seamlessly. Astera's AI-driven converter migrates your .dmod, .xmod, .dprj, and .dpwx models as ReportMiner report models automatically, usually in hours, so your reporting keeps running.
A: ReportMiner covers report mining, hierarchical report extraction, built-OCR, and parallel processing in a modern no-code platform. Along with intelligent and AI-driven features, Astera offers an award-winning support and a consistent and transparent licensing structure. This ensures your team processes thousands of documents with no downtime and a predictable pricing. So you gain capability rather than trade it away when you switch.
A: Datawatch Monarch Pro (now part of Siemens) focuses on template-driven extraction and export to BI tools. ReportMiner covers that ground and extends it with AI extraction, OCR, data quality, and integration across ERP, CRM, cloud, and API, usually at a lower total cost.
A: The cost of Datawatch Monarch is quote-only and has seen frequent increases causing frustration amongst its many former users who have not switched to ReportMiner. Astera ReportMiner uses transparent, tiered plans and supports multiple clients per license. Most teams find that the switch lowers total cost while adding OCR, data quality, and automation, along with excellent support in the package
Industry Use Cases
Common document types include invoices, purchase orders, bank statements, insurance claims forms, mortgage documents (payslips, credit reports, tax returns), bills of lading, packing slips, medical records, lab reports, utility bills, contracts, identity documents, resumes, student transcripts, financial statements, and customer feedback forms.
ReportMiner is used across financial services, insurance, healthcare, mortgage and lending, logistics and supply chain, government and public sector, education, and HR/payroll. Any organization that processes high volumes of documents and needs to load extracted data into downstream systems is a candidate.
Use case | What gets extracted |
Invoice processing (AP automation) | Line items, totals, vendor details, tax data |
Purchase order processing | Item descriptions, quantities, unit prices, PO numbers |
Mortgage and lending | Borrower information, income, financial commitments from payslips, bank statements, credit reports |
Insurance claims | Policyholder details, claim numbers, damage assessments, settlement data |
Healthcare records | Patient demographics, diagnosis codes, billing information, clinical data |
Logistics | Shipment IDs, carrier details, delivery addresses, tracking data |
Utility bill processing | Meter readings, billing data, consumption patterns |
Customer feedback analysis | Survey responses, free-text comments, ratings |
Healthcare organizations use ReportMiner to extract patient demographics, medical history, treatment details, billing codes, and diagnosis information from electronic health records, lab reports, and medical forms, then automate that data into hospital management systems for claims processing and compliance reporting.
Banks and lenders use ReportMiner to extract applicant information, loan amounts, interest rates, and payment schedules from mortgage applications, loan agreements, and credit reports, reducing manual processing time in underwriting and risk assessment. ReportMiner also performs a cross document validation to detect any fraudulent and forgery attempts. It can do smart reconciliation between data values on several documents with 99.5% accuracy. Furthermore, ReportMiner can keep a track of loan application progress and identify which documents are missing, invalid, and illegible and can send automated requests for document submissions.
Insurance companies extract policyholder details, claim numbers, damage assessments, and settlement information from claims forms and adjuster reports, which supports faster claims validation, fraud detection, and policy compliance checks.
Retailers use ReportMiner to standardize supplier invoice data across vendors that each use different field names and formats, automating accounts payable matching for line items like tax, item totals, and vendor codes. The same approach extends to shipping documents, product catalogs, and customer intake forms across a retailer's supply chain.
Accounts payable workflows have three properties that match ReportMiner's design: high document volume, varied vendor formats, and the need to validate extracted data against purchase orders before payment. ReportMiner extracts line items, totals, vendor details, and tax data from invoices in any format, validates them against POs in the same pipeline, and loads the result into ERP or accounting systems. Customers report cutting invoice processing from minutes to seconds per document.
ReportMiner extracts structured data from scanned manifests, delivery notes, and bills of lading, including handwritten or inconsistently formatted ones, pulling out fields like Shipment ID, Carrier, and Delivery Date. It uses OCR for scanned documents and Smart Source to handle the fact that different carriers and logistics partners format their paperwork differently, mapping all of it to one consistent schema. This gets that data into warehouse or logistics systems automatically instead of someone keying it in by hand, which improves tracking accuracy and cuts the manual work involved in reconciling shipments
A typical invoice automation workflow looks like this:
ReportMiner monitors an email inbox or shared folder where vendors send invoices.
When a new invoice arrives, OCR runs (if scanned), then the appropriate report model or LLM prompt extracts vendor name, invoice number, line items, totals, and tax.
Validation rules verify required fields are present, totals add up, and tax calculations match expected formats.
Extracted data is matched against the corresponding purchase order.
Clean data is loaded into the ERP or accounting system, triggering the payment workflow.
For purchase orders that arrive from many different vendors with varying layouts, customers typically use the LLM Generate feature. A single natural language prompt, describing the fields to extract from any PO, replaces the need to build dozens of vendor-specific templates. ReportMiner processes each PO as it arrives, extracts the requested fields regardless of layout, and loads the structured data into the procurement system or database.
Comparisons
In a documented case, a purchase order that took up to 30 minutes to update manually took under 2 minutes with ReportMiner, regardless of how many line items the PO contained. A separate customer account, processing 10,000 to 15,000 invoices per month across more than 3,000 vendors, reported invoice automation time dropping from about 40 minutes to about 2 minutes per invoice.
ReportMiner supports the same core report mining use cases as Monarch, plus AI-powered template generation (AGL), five OCR engine options, hierarchical (parent-child) data structures instead of Monarch's appended structure, and integration into a broader dataflow and workflow orchestration platform. Existing Monarch models can be imported directly using Astera's built-in converter, so the migration path doesn't require rebuilding templates from scratch.
Use template-based extraction, with report models, when layouts repeat, like a handful of known systems or vendors, even if that means a separate template for each one. Use AI-driven templateless extraction when layouts vary a lot, or unseen layouts are common, like invoices from a new vendor whose format hasn't been seen before.
A standalone OCR tool converts images to text. ReportMiner does that, then handles everything around it: AI-driven field extraction, validation, transformation, workflow orchestration, scheduling, and direct loading into databases and business applications. Customers using ReportMiner do not need to glue together OCR, extraction logic, validation, and integration tools separately.
Common alternatives evaluated alongside ReportMiner include Datawatch Monarch, Azure Form Recognizer, ABBYY FineReader, Nanonets, Rossum, PDF Focus, and various intelligent document processing (IDP) platforms. Customers report choosing ReportMiner for its handling of complex PDF layouts, end-to-end pipeline coverage, award-winning support, and no-code interface.
Customer reviews on G2 and Capterra consistently cite three reasons: ReportMiner handles more complex PDF layouts where simpler tools fail, it includes the full pipeline (validation, transformation, scheduling, and destination loading) rather than just extraction, and it offers a no-code interface accessible to operations staff. Several reviews specifically mention that others could not handle the same documents with high accuracy that ReportMiner processed successfully.
Customers and outcomes
Astera serves Fortune 500 enterprises, government departments, mid-size businesses, and nonprofits. Notable customers include Wells Fargo, USDA, U.S. Department of Veterans Affairs, Xerox, HP, Novartis, Raymond James, Ciena Corporation, Vanderbilt University, Cherry Health, Children's Community Health Plan, CoWorx Staffing, Advancial, Wescon Credit Union, Beal Services, Carrington Mortgages, TMP The Mortgage People, Gap Solutions, Sycomp, Coronet, Aclaimant and more.
Large enterprises process document volumes that make manual handling impractical and where accuracy is critical for compliance, auditing, or financial reporting. ReportMiner's combination of AI-driven extraction, validation rules, parallel processing, and direct integration with enterprise systems addresses both the scale and the accuracy requirements. Its no-code interface also reduces dependency on scarce developer resources for ongoing template maintenance.