Problem

Velos addresses the manual back-office work that companies have historically handled with internal staff or BPO and offshore teams because the workflows involve messy documents, exceptions, judgment calls, and legacy systems. This work is slow and error-prone, while its economics scale poorly: offshore teams can take months to ramp, errors create downstream costs, and every volume spike increases outsourcing or hiring spend. The central use case is commercial insurance operations, where processes such as bordereaux reporting, policy servicing, reconciliation, and claims work require high accuracy and frequent handling of varied data formats.

A representative customer process involved monthly reporting from more than 200 trucking dealerships, with inputs arriving in many formats and manual completion taking up to a week. Velos reports completing the process in a day with 98% accuracy, six days faster reporting, and no added headcount.

Product / Service

Velos replaces outsourced manual work with managed AI automations: software-driven workflows that the company designs, deploys, and manages end to end. Its approach combines AI and code, trains automation on the customer’s real data and edge cases, and encodes the customer’s rules and standard operating procedures into auditable workflows. The platform is positioned as custom AI workers for complex operations rather than a generic chatbot or document processor, with human-in-the-loop steps and versioning where needed.

The service automates work including data entry, bordereaux, policy servicing, billing and invoicing, loss reserving, and month-end close. The intended benefits are faster processing, lower costs than labor-based outsourcing, real-time visibility into outcomes and exceptions, consistent execution, and the ability to absorb higher volumes without adding headcount or expanding a BPO team.

Market

Velos operates in AI automation for back-office operations, also described as AI outsourcing automation, with an initial emphasis on commercial insurance carriers, wholesalers, and MGAs. It also presents use cases across finance and professional services, healthcare, HR, local government, and food and beverage. Its practical alternatives are traditional BPO and offshore outsourcing or hiring internal operations staff; the available research does not name specific direct software competitors.

The company appears early but has customer evidence rather than being purely pre-launch. Velos says it was founded in 2023, is based in San Francisco, is backed by Y Combinator, and has three employees. Its published customer examples include MRI recovering 50 hours per month, State Garden cutting gross-margin reporting time by 90%, Kamylon saving hundreds of hours on due diligence, and Orchid’s users saving 25,000 hours through patient-data workflows. The available material does not disclose revenue or funding amounts, so revenue stage cannot be determined from the research.