7/14/2026, 1:02:44 PM · sales-crm

KredosAI Raises $7 Million Series A to Expand AI-Powered Enterprise Collections Platform

The oversubscribed round, led by BMW i Ventures, backs a behavioral-intelligence platform that targets the critical window between a missed payment and a write-off across telecom, auto lending, and financial services.

KredosAI, an Issaquah, Washington-based artificial intelligence (AI) company, announced on July 2, 2026 the close of a $7 million Series A funding round to accelerate growth of its AI-powered collections and revenue-recovery platform. The round was oversubscribed, with BMW i Ventures—the independent venture capital arm of BMW Group—serving as lead investor. New backers Motley Fool Ventures and Walter Ventures joined existing investors Okapi Venture Capital, StartFast Ventures, SaaS Ventures, and Stout Street Capital. Total funding for the company now stands at just over $10 million.

Company Background

KredosAI was founded in 2021 by Balaji Sridharan, chief executive officer (CEO), and Dave Thoms, both former executives at T-Mobile. The company employs approximately 25 people and holds SOC 2 Type II certification. It serves Fortune 50 enterprises across North America, with publicly named clients including Anderson Brothers Bank. The platform is also integrated with the FICO Platform, the analytics software widely used by banks and large enterprises for credit decisions and collections.

Platform and Technology

The platform is designed to operate in the interval between a payment becoming overdue and an account being written off or transferred to third-party collections. Rather than deploying uniform outreach campaigns, the system applies behavioral science and machine learning to individualize the message, communication channel, and timing for each customer. The software weighs account-level signals—such as payment history, average balance, and tenure—while excluding legally restricted variables like age. The company describes this approach as replacing static campaigns with dynamic, adaptive engagement.

KredosAI's primary verticals are telecom operators, auto lenders, and financial institutions. The platform has processed more than 200 million customer interactions over two years, during which revenue grew more than sixfold.

Reported Outcomes

Across its enterprise portfolio, KredosAI reports an 11.5% reduction in write-off rates and a 13.6% increase in customer lifetime value (CLV). The company states these gains translate to more than $50 million in annual bottom-line benefit for some clients by preventing accounts from reaching the write-off stage, where conventional third-party collections activity can erode customer relationships. The platform claims a return on investment (ROI) of 20x or greater.

The broader market context underpins investor interest: the company notes that millions of accounts are past due at any given time at large enterprises, tying up 10–15% of revenue in delinquent receivables, with annual bad debt expense at some organizations exceeding $1 billion. Subprime auto-loan delinquencies have separately climbed to their highest levels since the 1990s, according to GeekWire.

Use of Proceeds and Investor Rationale

The Series A proceeds are allocated to three priorities: expanding go-to-market activity in financial services and auto lending; accelerating product development, including a multi-agent artificial intelligence framework with voice-agent support; and growing the team by 2× to 2.5× over the next 12 months.

BMW i Ventures Partner Baris Guzel cited "the clear data network effects behind the product" as a key attraction, alongside the founders' domain expertise and the platform's combination of behavioral economics and AI. The automaker's venture arm manages approximately $1.1 billion in assets and focuses on physical AI, agentic AI, and AI-native enterprise software—an investment thesis that aligns with KredosAI's planned roadmap expansion into agentic and voice capabilities.

The funding round illustrates a broader pattern in enterprise AI adoption, where vertical-specific applications with auditable, domain-relevant ROI metrics—rather than general-purpose large language model (LLM) deployments—are attracting institutional capital at the Series A stage.

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