We build intelligent extraction systems that pull structured data from documents, emails, web pages, and any unstructured source with high accuracy and reliability.
Vast amounts of valuable business data exist in formats that machines cannot directly consume: PDF reports, email threads, web pages, support tickets, social media posts, and handwritten notes. AI data extraction transforms these unstructured sources into clean, structured data that feeds your analytics, databases, and automated workflows.
The challenge with traditional extraction approaches is that they require explicit rules for every source format and data pattern. When formats change, rules break. When new sources are added, new rules must be written. AI-powered extraction uses language understanding and pattern recognition to handle format variations automatically, making the system far more resilient and adaptable than rule-based alternatives.
Arthiq builds data extraction systems that handle the full complexity of real-world data sources. Our solutions go beyond simple text matching to understand document structure, resolve entity references, handle multiple languages, and validate extracted data against business rules and external reference data.
Different data sources require different extraction approaches. Arthiq designs unified extraction architectures that handle multiple source types through a common pipeline. Documents go through OCR and layout analysis. Emails are parsed for structure and threaded conversations. Web pages are rendered and cleaned of navigation and advertising content. API responses are normalized from varying schemas.
At the core of our extraction system is an LLM-powered extraction engine that understands the semantics of the content rather than relying on positional rules. We define extraction schemas that describe what data you need, and the engine identifies and extracts matching information regardless of how it is presented in the source. This approach handles format variations, synonyms, and structural differences that would require dozens of rules in a traditional system.
For high-volume extraction tasks, we implement batch processing pipelines that parallelize work across multiple workers and optimize model usage to keep costs manageable. Our systems process tens of thousands of documents per day while maintaining consistent extraction quality.
Extraction is only useful when the data is accurate. Arthiq implements multi-layer validation frameworks that catch errors before extracted data enters your systems. Format validation ensures dates, numbers, and identifiers conform to expected patterns. Cross-field validation checks that extracted values are internally consistent. Reference validation verifies entities against your master data.
We also implement confidence scoring for every extracted field. When the extraction model is uncertain about a value, the item is flagged for human review rather than being passed downstream with potential errors. Review interfaces show the source document with highlighted extraction regions, making verification fast and intuitive.
Our systems learn from corrections. When reviewers fix extraction errors, those corrections are captured and used to improve future extraction accuracy through prompt optimization and few-shot example libraries. This continuous improvement loop means accuracy increases over time as the system encounters more document variations.
Beyond documents, Arthiq builds AI extraction systems for web pages, email communications, and dynamic online sources. Our web extraction tools use headless browsers to render JavaScript-heavy pages, then apply AI to understand page structure and extract the specific data you need without brittle CSS selectors or XPath expressions.
Email extraction handles the complexity of threaded conversations, forwarded messages, varied signature formats, and inline content mixed with attachments. Our systems can extract structured data from order confirmations, shipping notifications, appointment reminders, and any other recurring email pattern in your inbox.
For competitive intelligence and market monitoring, we build extraction pipelines that collect data from multiple web sources on a schedule, normalize the extracted data into a consistent schema, and deliver it to your analytics platform or data warehouse. These pipelines include change detection that alerts you when monitored sources update their information.
Data extraction is a foundational capability that enables downstream automation, analytics, and decision-making. Arthiq builds extraction systems that are accurate, scalable, and maintainable, handling the full complexity of real-world data sources without requiring constant rule updates.
We start every engagement with a data source assessment where we analyze your actual data, benchmark extraction accuracy, and design an architecture that meets your throughput and quality requirements. Our iterative delivery approach means you see working extraction results within the first two weeks.
Contact us at founders@arthiq.co to discuss how AI data extraction can eliminate manual data entry and unlock the value hidden in your unstructured data sources.
Our engineering team will build a data extraction system that transforms unstructured sources into clean, structured data that powers your business processes.