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Description
Dobs is a comprehensive AI solution tailored for financial due diligence, utilizing advanced AI agents to transform chaotic data rooms into polished workbooks within minutes rather than the traditional weeks. This platform enables transaction teams to effectively analyze critical financial aspects such as proof of cash, payroll, and EBITDA with an impressive accuracy rate exceeding 99%, which significantly diminishes the reliance on manual spreadsheet work and expedites the review process. Designed to manage and interpret inconsistent financial documents, Dobs extracts structured data, organizes accounts, reconciles information, and produces outputs ready for analysis that teams can leverage for due diligence, reporting, and strategic decision-making. Its AI agents cater to a variety of applications within financial due diligence, as well as sectors like construction, real estate, and property management, assisting users in reviewing T12s, benchmarking performance, uncovering hidden expenses, mitigating covenant risks, and recording change orders. In addition to these capabilities, Dobs enhances real estate investment evaluations by offering AI-driven parsing, automated account mapping, real-time benchmarking, and a dynamic chat feature for deal discussions, ensuring users have access to the most relevant insights for informed decision-making. As a result, Dobs not only streamlines the due diligence process but also empowers teams to make timely and well-informed financial decisions.
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
DOBS AI
Founded
2024
Country
United States
Website
dobs.ai/
Vendor Details
Company Name
Subquadratic
Founded
2026
Country
United States
Website
subq.ai/subq-1-1-small-technical-report
