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Description
Capitol serves as an advanced AI platform specifically crafted for high-stakes enterprises that are governed by regulations, enabling them to transform expert insights and confidential information into reliable solutions for crucial decisions. It ensures that all data remains secure within the organization's infrastructure while delivering results that are both traceable and auditable, ready for implementation in just moments rather than taking weeks. By leveraging sovereign agentic search and automated intelligence synthesis, Capitol effectively translates enterprise knowledge into actionable outcomes, offering an extensive range of customizable artifacts. Its research engine is meticulously designed to operate exclusively with the organization’s proprietary data, while the synthesis layer automatically generates comprehensive reports, briefs, and customized documents from both structured and unstructured information. Furthermore, it adeptly produces and condenses content in various formats, integrating internal and external resources to convert proprietary intelligence into valuable tools such as presentations, spreadsheets, software code, and audio files, thereby enhancing the organization's decision-making capabilities. Such features empower organizations to make informed choices rapidly and confidently, reinforcing their strategic advantage in the market.
Description
SubQ 1.1 Small is the second iteration of Subquadratic’s long-context AI model, built to help enterprises solve problems that require reasoning across entire artifacts rather than isolated chunks. The model is designed for use cases involving large code repositories, document libraries, legal agreements, financial reports, contracts, and other complex information sets. Its Subquadratic Sparse Attention architecture reduces the compute burden of traditional dense attention, making it more practical to process multi-million-token contexts. SubQ 1.1 Small achieves near-perfect performance on needle-in-a-haystack retrieval tests up to 12M tokens, despite being trained primarily at 1M tokens. It also performs strongly on RULER, GPQA Diamond, LiveCodeBench, and AutomationBench Finance, showing a balance between long-context retrieval and general reasoning ability. At 1M tokens, the model uses 64.5x less compute than dense attention and runs 56x faster than FlashAttention-2 on a single attention layer. This efficiency makes long-context training and inference more scalable for enterprise AI applications. SubQ 1.1 Small is especially valuable for teams that need to analyze relationships across full documents, trace logic across codebases, or connect information across extensive collections. The model is intended to help organizations reduce dependence on complex retrieval workarounds and reason more directly over large-scale data.
API Access
Has API
API Access
Has API
Integrations
Claude Code
OpenAI
OpenAI Codex
SubQ
Pricing Details
No price information available.
Free Trial
Free Version
Pricing Details
No price information available.
Free Trial
Free Version
Deployment
Web-Based
On-Premises
iPhone App
iPad App
Android App
Windows
Mac
Linux
Chromebook
Deployment
Web-Based
On-Premises
iPhone App
iPad App
Android App
Windows
Mac
Linux
Chromebook
Customer Support
Business Hours
Live Rep (24/7)
Online Support
Customer Support
Business Hours
Live Rep (24/7)
Online Support
Types of Training
Training Docs
Webinars
Live Training (Online)
In Person
Types of Training
Training Docs
Webinars
Live Training (Online)
In Person
Vendor Details
Company Name
Capitol
Founded
2021
Country
United States
Website
www.capitol.ai/
Vendor Details
Company Name
Subquadratic
Founded
2026
Country
United States
Website
subq.ai/subq-1-1-small-technical-report
Product Features
Artificial Intelligence
Chatbot
For Healthcare
For Sales
For eCommerce
Image Recognition
Machine Learning
Multi-Language
Natural Language Processing
Predictive Analytics
Process/Workflow Automation
Rules-Based Automation
Virtual Personal Assistant (VPA)