SellWizr provides a unified data foundation and AI agents that consolidate fragmented sales data across financial institutions, turning messy, duplicated information into a reliable, actionable context graph. By delivering a ready-to-use layer for engineers, it eliminates the need to rebuild data pipelines, enabling sales teams to focus on selling rather than reconciling data.
Funding
Funding not disclosed
Founders
Product
Problem
Financial institution sales teams rely on client and product data that is spread across multiple, inconsistent systems, resulting in duplicated, incomplete, and drifting records. This fragmented data erodes trust, forces salespeople to spend time reconciling information, and leads to reactive, inconsistent coverage.
Solution
SellWizr provides a unified data foundation—a context graph that consolidates client and product information from disparate sources into a single, reconciled entity model. The platform continuously resolves and updates entities in real time, delivering a reliable, action‑ready data layer for both human users and AI agents. Built as a ready‑to‑use integration layer, it eliminates the need for engineering teams to rebuild data pipelines or entity resolution logic. AI agents can then operate on consistent, trustworthy data to generate proactive deal insights and automated next‑best‑action recommendations. By flattening data‑infrastructure costs and preventing quality drift, SellWizr enables sales organizations to move from data reconciliation to data‑driven execution.
Target Audience
Primary customers are sales and revenue operations teams within banks, asset managers, and other financial institutions that need reliable client‑product intelligence, as well as the technology leaders responsible for their sales‑tech stack.
Features
- Context graph that ingests and normalizes client and product data from multiple legacy systems
- Automated entity resolution and historical reconciliation to maintain a single source of truth
- Real‑time data updates ensuring that sales signals are current when decisions are made
- AI‑driven agents that consume the unified data layer to produce actionable deal insights and next‑best‑action recommendations
- Plug‑and‑play integration layer that removes the need for custom data‑pipeline development by engineering teams
- Scalable architecture with flat cost model, avoiding per‑token or per‑record pricing escalation