Problem

Refactor targets the operational drag created when large organizations try to automate workflows that still depend on fragmented, legacy software. Its customers face manual, inconsistent, and hard-to-scale work across retail, healthcare, and logistics. The clearest pain point is retail catalog onboarding: merchants may manage hundreds of thousands of SKUs while vendors submit product data in spreadsheets, PDFs, and unstructured text with missing or inconsistent specifications, creating labor costs, slow launches, and poor data quality. In healthcare revenue-cycle management, staff similarly spend hours researching payer portals, ERA/835 files, EHR systems, and payer policies to resolve denials and make follow-up calls.

The strongest “killer” use cases are therefore high-volume, rules-heavy workflows where employees repeatedly move between systems or conduct long phone calls. Refactor’s public examples emphasize automating product onboarding in retail and denial management, payer calls, and related revenue-cycle work in healthcare, with the economic goal of reducing manual effort while improving speed, collections, conversion, or data quality.

Product / Service

Refactor provides tailored, enterprise AI-agent deployments rather than a generic chatbot. The company says its autonomous systems can call, click, and navigate legacy software, and that it works closely with large organizations to tackle ambitious AI use cases. In retail, an agent can extract product information from vendor sources, normalize attributes, enrich missing specifications from verified sources, and contact vendors when information is incomplete. A separate conversational shopping agent is designed to provide multilingual product discovery, comparisons, recommendations, tutorials, and advice through chat or voice.

In healthcare, Refactor describes an end-to-end AI orchestration layer for revenue-cycle management. Agents can ingest payer-portal, ERA/835, read-only FHIR/EHR, and policy data to research denials and take complex actions with an audit trail; phone agents can handle eligibility, prior authorization, claims follow-up, medical-record confirmation, and billing calls. The promised benefit is parallel, scalable execution of repetitive operational work, with measurable improvements such as faster onboarding and denial resolution, fewer manual follow-ups, better claims and product data, faster collections, and improved conversion or average order value.

Market

Refactor competes in the emerging enterprise AI-agent and intelligent workflow-automation market, with vertical applications in retail operations and healthcare revenue-cycle management. Its differentiation is the combination of autonomous interaction with legacy systems and bespoke deployment for large organizations, rather than requiring a customer to replace its existing software. The competitive set is broad and partly adjacent: UiPath and Microsoft Copilot Studio represent general enterprise automation and agent platforms, while healthcare RCM specialists such as CombineHealth, Waystar, Experian Health, MedEvolve, FinThrive, Optum, and Datavant address parts of the denial-management workflow. In retail, AI shopping-assistant alternatives include Agentforce and SAP CX AI Toolkit.

Refactor is not presented as pre-revenue in the available public material, but it also does not disclose revenue, a named customer, or a quantified deployment count. The company says it has worked with organizations serving tens of millions of customers and generating more than $50 billion in annual revenue. Y Combinator lists it as an active Summer 2024 company, founded in 2024 and based in San Francisco, so the public evidence supports early enterprise traction and validation rather than a documented revenue scale.