Data Quality Platform

VeriStruct

Production-Grade Structured Data from Complex Documents

Set-and-forget data quality layer that delivers highly accurate, structured data from financial filings, research reports, and disclosures. AI-driven extraction with human verification ensures correctness before data reaches production systems.

99.9%
Data Accuracy
10x
Faster Processing
24/7
API Availability

API-First Processing

Submit extraction requests via simple API. Async processing with secure webhooks.

AI-Driven Extraction

Advanced models trained on financial and research documents for accurate field extraction.

Human Verification

Expert reviewers validate AI extractions, ensuring production-grade accuracy.

The Data Quality Problem

Data-driven teams face a critical challenge: unreliable or incomplete data from source documents

Current Pain Points

  • Fragile extraction pipelines break on document format changes

  • Manual QA processes slow down time-to-insight

  • Missing or incorrect fields cause downstream pipeline failures

  • Teams lose time debugging broken data feeds instead of innovating

VeriStruct Solution

  • Set-and-forget data quality layer with guaranteed correctness

  • AI-powered extraction handles complex, inconsistent documents

  • Human verification ensures production-grade data quality

  • Scalable automation replaces fragile, ad hoc QA processes

Trusted Data Quality Layer

Acts as a reliable bridge between raw documents and production systems, ensuring teams can trust their data without slowing down innovation.

Scalable Without Headcount

Replace manual validation processes with automated, expert-reviewed workflows that scale with your data volume without proportional headcount growth.

Core Platform Features

Production-ready data extraction with AI automation and human oversight

API-First Data Processing

  • Simple API for submitting extraction requests (fields, tables, metrics)

  • Asynchronous processing with results delivered via secure callbacks

  • Designed for batch and recurring jobs (quarter-end updates, ongoing monitoring)

  • RESTful endpoints with comprehensive documentation and SDKs

AI-Driven Document Sourcing & Extraction

  • Automatically locates relevant source documents across repositories

  • Advanced AI models trained for financial and research documents

  • Extracts fields with high accuracy from dense, inconsistent formats

  • Outputs consistently structured, properly formatted data

Human-in-the-Loop Verification

  • Expert reviewers validate AI-extracted data before delivery

  • Optimized review workflows focus human effort where AI confidence is low

  • Ensures production-grade data quality, not 'best-effort' extraction

  • Continuous learning improves AI accuracy over time

Secure Integration & Delivery

  • HTTPS webhooks with signature verification for secure callbacks

  • Support for multiple data formats (JSON, CSV, Excel)

  • Configurable retry policies and delivery guarantees

  • Real-time status updates and processing notifications

Data Quality & Validation Approach

Multi-layer verification ensures production-grade accuracy for mission-critical workflows

Consensus Validation

Multiple reviewers independently verify critical fields with consensus logic to reduce single-person errors.

Error-Resilient Outputs

Emphasis on correctness, completeness, and formatting to prevent downstream pipeline failures.

Quality Guarantees

Designed to meet reliability expectations of production trading, research, and reporting systems.

Technical & Modeling Foundation

Purpose-built for financial and research documents, not generic PDFs

State-of-the-Art AI Models

Built on advanced document understanding and extraction models specifically optimized for dense, complex financial and research documents.

Proprietary Training Data

Enhanced with real-world training data derived from financial filings, analyst reports, and regulatory disclosures for higher accuracy.

Purpose-Built Workflows

Specialized workflows for handling inconsistent formats, dense tables, and poorly structured documents rather than generic PDFs.

Example Use Cases

Production-ready applications across investment, research, and compliance workflows

Financial Filing Data Repair

Repair missing or incorrect fields in financial filing data feeds used by quantitative research teams.

Research Report Extraction

Extract clean, standardized metrics from analyst and research reports for investment workflows.

ESG Data Collection

Collect, validate, and structure ESG data from regulatory and corporate disclosures.

Data Quality Gate

Serve as final data quality checkpoint before data is pushed into analytics, models, or production systems.

Vision & Target Markets

Trusted data quality layer between raw documents and production systems

Target Users

  • Quantitative Investment Teams - Analysts needing reliable financial data feeds for models and research

  • Research Analysts - Teams extracting insights from dense reports and disclosures

  • Data Engineers - Teams building production data pipelines requiring high-quality inputs

  • Compliance-Focused Investors - Organizations tracking ESG, regulatory, and disclosure data

Pain Points Solved

  • Teams losing time debugging broken data feeds or manually validating extractions

  • Organizations relying on fragile, ad hoc QA processes that don't scale

  • Production systems vulnerable to data quality issues from upstream sources

  • Innovation bottlenecked by unreliable data extraction infrastructure

Set-and-Forget Reliability

Teams can rely on their data without manual validation, enabling faster innovation and confident decision-making based on production-grade structured data.

Scalable Without Headcount

Replace fragile pipelines and manual QA with automated, scalable data quality solutions that grow with data volume without proportional increases in team size.