Enterprise Infrastructure for Unstructured Data
1. Introduction to Orbit AI Studio
Orbit AI Studio is an enterprise-grade platform designed to solve the fundamental challenge of managing and extracting intelligence from unstructured financial documents at scale. While modern financial institutions have mature infrastructure for structured data (Snowflake, Databricks), unstructured documents—which contain 80%+ of critical financial intelligence—remain fragmented across systems with inconsistent processing and no unified architecture.AI Studio provides the complete technology stack for acquiring, processing, storing, and analyzing unstructured financial data. From raw PDF documents to AI-powered insights, the platform handles the entire lifecycle with enterprise-grade reliability, security, and performance.
Orbit AI Studio is to unstructured data what Snowflake is to structured data — a comprehensive platform that enables organizations to focus on building differentiated applications and generating alpha, rather than building commodity data infrastructure.
Core Platform Modules
Module 1: Knowledge Base Building Pipeline
Complete infrastructure for transforming raw documents into AI-ready structured data
Data Ingestion (web scraping, APIs, uploads)
Entity Master & Ontology Management
Metadata Management & Classification
Advanced PDF Parsing
Storage (flat files, databases, search engines)
Module 2: Pre-Built Knowledge Bases
Production-ready datasets of processed financial documents
Global Filings (50K+ companies)
Financial Reports
Earnings Transcripts
ESG & Sustainability Reports
Corporate Action Announcements
Available as data feed or via MCP/API
Module 3: Extraction & Calculation Services
AI-powered data extraction and analytical computation at scale
Bespoke Data Point Extraction
Document Summarization
Complex Analytical Workflows
Extraction Logic Optimization
Model Fine-tuning Services
Platform Architecture Overview
Raw Documents → Ingestion & Parsing → Knowledge Base → Extraction & Calculation
(PDFs) Pipeline (Structured) (AI-Powered) 2. Knowledge Base Building Pipeline
The Knowledge Base Building Pipeline is the foundation of AI Studio, responsible for transforming raw financial documents into clean, structured, AI-ready data. This pipeline handles millions of documents with enterprise reliability, processing complex financial filings that generic document processing tools cannot handle effectively.
2.1 Data Ingestion
The ingestion layer supports multiple acquisition methods to bring documents into the platform from any source, handling both public regulatory filings and proprietary internal documents.
Supported Ingestion Methods
Web Scraping Framework:
Configurable crawlers with rate limiting, respectful crawling patterns, and automatic retry logic
API Integration:
RESTful and GraphQL connectors for third-party data providers
Direct Upload:
Bulk upload via UI, API, or S3-compatible object storage sync
Email Integration:
Automated document intake from email attachments
Enterprise Connectors:
SharePoint, Box, Google Drive, internal document management systems
Technical Capabilities
Scheduled polling for batch ingestion
Webhook support for push-based ingestion
Deduplication logic to prevent reprocessing
Document versioning and change tracking
Error handling with automatic retry and alerting
Note: Custom connectors can be developed for proprietary sources. Development timeline typically 2-4 weeks depending on source complexity.
2.2 Entity Master & Ontology Management
The Entity Master provides unified entity resolution and relationship management across all documents, ensuring consistent identification of companies, people, and concepts regardless of naming variations or data source.
Key Features
Entities covered:
Unified company profiles with multiple identifier support (ticker, ISIN, LEI, CIK, etc)
Name Variation Management:
Handles alternate names, legacy names, DBA names, international variations
Automatic Entity Resolution:
ML-based matching to resolve entities across documents
Change History:
Track name changes, mergers, acquisitions, spin-offs
Custom Ontologies:
Support for client-specific taxonomies and classification schemes
2.3 Metadata Management & Document Classification
Comprehensive metadata extraction and enrichment enables efficient document discovery, filtering, and compliance tracking.
Automatic Metadata Extraction
Document Type Classification:
Annual reports, quarterly reports, earnings transcripts, presentations, sustainability reports (300+ document types)
Temporal Information:
Filing dates, report dates, event dates
Regulatory Metadata:
Exchange identifiers, filing authority
Language Detection:
Automatic language identification with translation markers
Metadata Storage & Indexing
Elasticsearch-based full-text search index
PostgreSQL/MongoDB for structured metadata queries
Support for custom metadata fields and tags
Version control for metadata updates
API access for metadata queries and bulk export
2.4 Advanced PDF Parsing Engine
The parsing engine is specifically optimized for complex financial documents, achieving 99%+ accuracy on tables and multi-column layouts.
Core Parsing Capabilities
Financial Table Extraction:
Intelligent detection and extraction of financial statements, preserving row/column headers, cell merging, and numeric formatting
Multi-Page Table Reconstruction:
Automatic stitching of tables split across pages with header repetition detection
Multi-Column Layout Processing:
Correct reading order preservation in complex 2-3 column layouts
Exhibit & Section Extraction:
Intelligent segmentation of documents into logical sections
Chart & Image Extraction:
Identification and extraction of embedded graphics with OCR for chart text
Output Formats
Structured JSON: Complete document hierarchy with text, tables, metadata
Performance Specifications
Processing speed: 100-page document in under 60 seconds
Accuracy: 99%+ on financial tables and structured content
Maximum document size: 1,000 pages
Supported formats: PDF, scanned PDF (OCR)
Auto-scaling based on queue depth
2.5 Storage Architecture
A multi-tier storage system optimized for different access patterns and data types, from raw document preservation to real-time search and analytics.
Storage Layers
Raw Document Store (S3/Blob): Original PDF files preserved for audit and reprocessing
Versioned storage with lifecycle policies
Hot/warm/cold tiering for cost optimization
Immutable storage options for compliance
Structured Data Store (PostgreSQL): Extracted text, tables, and metadata
JSONB for flexible schema
Full ACID compliance
Optimized indexes for common query patterns
Partitioning by company and date for performance
Search Engine (Elasticsearch):Document embeddings for semantic search
Multiple embedding model support
Sub-second similarity search
Full-text search and faceted filtering
Real-time indexing pipeline
Advanced query DSL support
Aggregations for analytics
Flat File Export: CSV, JSON Lines for bulk export
Partitioned by company/date for selective loading
S3-compatible bulk export
Data Management Features
Data Lineage: Complete tracking from raw document through all processing stages
Version Control: Document and data versioning with rollback capability
Reprocessing: Ability to reprocess documents with updated parsing logic
Backup & Recovery: Automated backups with point-in-time recovery
Data Retention Policies: Configurable retention with automated archival
Access Logging: Complete audit trail of data access for compliance
Knowledge Base Building Pipeline Flow
Ingestion → PDF Parsing → Entity Resolution → Metadata Enrichment → Multi-Tier Storage
Layer End-to-end processing time: 2-5 minutes per document (depending on size and complexity)
3. Pre-Built Knowledge Bases
Orbit maintains production-ready knowledge bases of processed financial documents, eliminating the need to build data collection and processing infrastructure from scratch. These knowledge bases are continuously updated as new documents are filed and are available through multiple consumption models.
3.1 Global Filings Knowledge Base
Our flagship knowledge base covering regulatory filings from public companies globally. All documents are pre-processed through the complete pipeline described in Chapter 2, delivered in AI-ready structured format.
Coverage Summary
Companies: 50,000+ globally listed companies
Documents: 15M+ historical documents
Markets: United States, United Kingdom, Europe, China, Japan, Australia, Canada, Singapore, India, and 50+ additional countries
Document Types: Annual reports, quarterly filings, current reports, proxy statements, prospectuses, earnings transcripts, presentations
Historical Depth: 10+ years
Update Frequency: Daily
3.2 Access Methods
Data Feed License
Bulk Data DeliveryDescription: Complete knowledge base delivered to your infrastructure for local hosting and integration.Delivery Options:
Initial bulk transfer (S3)
Continuous incremental updates (daily)
Best For: Organizations wanting full data ownership, no API dependencies, or integration with existing data lakes.
API Access
On-Demand QueryDescription: Query knowledge base via RESTful API or GraphQL without hosting data locally.API Capabilities:
Document retrieval by company, date, type
Full-text and semantic search
Table and section extraction
Metadata queries and filtering
Bulk export endpoints
Best For: Rapid prototyping, variable usage patterns, or supplementing existing data sources.
Model Context Protocol (MCP)
LLM-Native IntegrationDescription: Direct integration with Large Language Models (ChatGPT, Claude, etc.) via MCP standard.MCP Features:
Native tool definitions for LLMs
Semantic search over documents
Context-aware document retrieval
Automatic citation and sourcing
Optimized for RAG patterns
Best For: AI chatbots, research assistants, or conversational interfaces requiring financial document access.
4. Extraction & Calculation Services
Beyond storing and serving documents, AI Studio provides powerful computation capabilities to extract structured data and perform complex analytical workflows. These services leverage Large Language Models and custom-trained extraction models to transform unstructured text into actionable intelligence.
4.1 Bespoke Data Point Extraction
Extract specific metrics, facts, or data points systematically across thousands of documents. Unlike generic prompting, our extraction service uses optimized prompts, validation logic, and quality assurance to ensure consistent, reliable output.
Extraction Capabilities
Financial Metrics:
Revenue segments, margins by division, guidance ranges, capital expenditure plans
Qualitative Factors:
Management sentiment, risk factor changes, competitive positioning statements
Structured Events:
M&A announcements, product launches, regulatory actions
Custom Taxonomies:
Client-specific data points and classification schemes
Multi-Source Validation:
Cross-reference data points across multiple documents for consistency
Historical Tracking:
Time-series construction for trend analysis
Technical Approach
Iterative prompt engineering with validation on sample sets
Confidence scoring for each extracted data point
Source citation with page/section references
Batch processing for large-scale extraction (thousands of companies)
Real-time extraction API for ad-hoc queries
Typical Use Cases: Systematic extraction of 50+ ESG metrics from sustainability reports; revenue breakdown by product line across 1,000 companies; quarterly tracking of supply chain mentions.
4.2 Document Summarization
Generate consistent, high-quality summaries of financial documents optimized for investment analysis workflows.
Summarization Types
Executive Summaries: High-level overview for quick review (200-500 words)
Section Summaries: Condensed version of specific sections (Risk Factors, MD&A, etc.)
Change Summaries: What's different vs. prior period
Comparative Summaries: Company vs. peers on specific topics
Thematic Summaries: Extraction of all content related to specific themes (AI, ESG, supply chain)
All summaries include source citations and can be customized for length, focus areas, and output format.
4.3 Complex Analytical Workflows
Multi-step analysis pipelines that combine extraction, calculation, and reasoning to produce sophisticated analytical outputs.
Example Workflows
Competitive Positioning Analysis: Extract competitive mentions → Identify key competitors → Compare product/service positioning → Assess relative strengths → Generate summary reportRisk Factor Analysis: Extract all risks → Categorize by type → Track changes vs. prior period → Score severity → Identify emerging risks → Peer comparisonManagement Quality Assessment: Extract capital allocation decisions → Track promises vs. execution → Analyze compensation structure → Evaluate governance → Generate quality scoreThematic Exposure Quantification: Identify theme mentions → Extract revenue/margin impact → Classify as headwind/tailwind → Quantify exposure → Track over timeWorkflow Orchestration: Visual workflow builder allows business users to define multi-step analysis logic without coding. Workflows can be scheduled to run automatically on new documents or triggered via API.
4.4 Extraction Optimization Services
For complex or high-volume extraction tasks, our optimization services ensure maximum accuracy and cost-efficiency through custom prompt engineering or model fine-tuning.
Extraction Logic Optimization
Prompt EngineeringService Description: Expert prompt engineering to maximize extraction accuracy and minimize token usage.Deliverables:
Optimized prompts with validation rules
Sample output validation (100+ documents)
Accuracy benchmarking report
Documentation and usage guidelines
Timeline: 2-4 weeks
Model Fine-Tuning
Custom ML ModelsService Description: Fine-tune extraction models on your specific use case for superior accuracy and lower cost.Deliverables:
Custom fine-tuned model weights
Training and validation datasets
Model deployment (API or on-premise)
Performance benchmarking
Ongoing retraining options
Timeline: 8-12 weeks
When to Optimize: Consider optimization services when: (1) Running extraction on >10,000 documents, (2) Accuracy requirements >95%, (3) Cost per extraction needs to be minimized, or (4) Proprietary terminology requires domain adaptation.
5. Deployment Options & Commercial Models
AI Studio offers flexible deployment options to match your security requirements, technical infrastructure, and commercial preferences. Whether you prefer cloud SaaS, private cloud, or on-premise deployment, the platform capabilities remain consistent.
5.1 Platform Deployment Options
Orbit SaaS on AWS
Fully ManagedDescription: Complete AI Studio platform hosted and managed by Orbit on AWS infrastructure.Characteristics:
Zero infrastructure management required
Automatic scaling and updates
99.9% uptime SLA
Multi-tenant architecture with data isolation
Global availability (US, EU, Asia regions)
Best For: Fastest time-to-value, no infrastructure team, prefer OPEX over CAPEX.
Private Cloud (AWS/Azure)
Dedicated InfrastructureDescription: Dedicated AI Studio instance in your AWS or Azure account, managed by Orbit.Characteristics:
Data resides in your cloud account
VPC isolation and custom networking
Your encryption keys (BYOK)
Orbit manages infrastructure and updates
Custom SLA agreements available
Hybrid connectivity to on-premise systems
Best For: Data residency requirements, need dedicated resources, want managed service without multi-tenancy.
On-Premise Installation
Self-HostedDescription: Complete AI Studio stack deployed in your data center or private cloud, managed by your team.Characteristics:
Full control over infrastructure
Docker/Kubernetes deployment
Your team manages operations
Annual license + maintenance model
Orbit provides updates and support
Best For: Strict data governance, regulatory requirements, no cloud usage allowed, very high document volumes.
5.2 Module-Specific Commercial Models
PDF Parsing Services Pricing
Offering
Description
Pricing Model
Typical Use Case
SaaS Pay-As-You-Go
API access with usage-based billing
0.4 credits per 10 pages
Variable volumes, development/testing, sporadic usage
SaaS Fixed Project
Pre-purchased parsing credits for defined project
Fixed fee for X documents 50% discount vs. pay-as-you-go
One-time historical document processing, migration projects
On-Premise License
Software license for unlimited self-hosted parsing
annual license Based on volume tier + one-time setup
Very high volumes (100K+ docs/year), data security requirements, predictable costs
Break-Even Analysis: On-premise license becomes cost-effective at approximately 100K-150K documents per year compared to SaaS pay-as-you-go pricing.
Knowledge Base Access Pricing
Offering
Description
Pricing Model
What's Included
Data Feed License
Bulk data delivery for local hosting
Contact sales team
Complete historical dataset, continuous updates, unlimited internal usage, technical support
API Access - Consumption
Query knowledge base via API, pay per use
On-demand document retrieval, search, metadata queries. See detailed API pricing in Chapter 3
MCP Access - Consumption
LLM-native access via Model Context Protocol
Consumption based
Semantic search, context-aware retrieval, optimized for RAG patterns, LLM tool definitions
Extraction & Calculation Services Pricing
Offering
Description
Pricing Model
Considerations
Consumption-Based (Orbit SaaS)
Pay per extraction/calculation run on Orbit infrastructure
Consumption based
Ideal for variable workloads, testing different extraction approaches, no infrastructure management
Private Deployment License
Run extraction services on your infrastructure
Annual License + Maintenance
Predictable costs for high volumes, full control over compute, data never leaves your environment
Extraction Optimization
Professional services to optimize accuracy and efficiency
Prompt Engineering Model Fine-Tuning
Recommended for >10K documents, accuracy >95% required, or proprietary terminology. See Chapter 4 for details
5.3 Platform Licensing (Full Stack)
For organizations deploying the complete AI Studio platform (pipeline, knowledge bases, and extraction services), comprehensive licensing packages are available.
SaaS Platform
What's Included:
Complete pipeline access
Knowledge base licenses
Extraction services (consumption-based)
Managed infrastructure
Standard support
Pricing Structure:
Annual base platform fee
Knowledge base licenses
Extraction consumption
Private Cloud
What's Included:
Dedicated infrastructure in your cloud
All platform capabilities
Knowledge base licenses
Managed by Orbit
Premium support
Pricing Structure:
Setup cost
Platform License
Knowledge base licenses
Monthly management fee
On-Premise
What's Included:
Complete software stack
Unlimited processing
Knowledge base licenses
Your team operates
Orbit provides updates & support
Pricing Structure:
Setup & Implementation
Annual Platform License
Knowledge base licenses
Monthly management fee
Custom Packages: Actual packages are customized based on: document volumes, number of companies/industries covered, required knowledge bases, extraction complexity, deployment requirements, and support SLAs. Contact us for detailed proposal based on your specific requirements.
5.4 Decision Framework: Which Deployment Option?
Your Requirements
Recommended Approach
Rationale
Building RAG system, need parsing only
PDF Parsing SaaS (pay-as-you-go)
Minimal commitment, test quality, scale as needed
Need historical data for platform development
Knowledge Base Data Feed License
Complete data ownership, no API dependencies, unlimited queries
Prototyping AI applications
API/MCP Access (consumption)
Fast start, flexible, validate use case before larger investment
Processing 200K+ documents/year
On-Premise Parsing License
Cost-effective at scale, predictable budgeting
Strict data residency requirements
Private Cloud or On-Premise
Data never leaves your infrastructure
Building enterprise AI platform
Full Platform License (SaaS or Private)
Complete stack, managed service, focus on applications not infrastructure
Regulated environment, air-gapped network
On-Premise Platform License
Only option for environments without internet connectivity
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