Mission Lane’s Source of Truth for Credit Decisioning with Chalk

Mission Lane is a fintech helping millions of Americans left behind by traditional financial service companies access fair, transparent credit. Founded by industry veterans, the company has built a customer base of over 2.5 million by combining traditional credit data with machine learning.

As modeling and decisioning systems became more complex—and more central to daily operations—Mission Lane needed a modern feature platform to unify real-time and batch infrastructure. They chose Chalk to serve as a centralized, production-grade feature store to power underwriting, fraud detection, and customer-facing product experiences.

We're a fintech, so we are opportunistic in where we build our infrastructure, and we needed a partner who could solve this cleanly.

Mike Kuhlen ML Solutions & Strategy

As Mission Lane’s modeling workflows matured, the team reached a familiar breaking point for many data-driven fintechs: feature engineering had become a bottleneck. The problem wasn’t model quality—it was the growing complexity of the systems around them.

Across the company, different roles relied on different tools:

Each team operated with its own requirements, but without a shared foundation, teams rebuilt the same features multiple times across environments, leading to duplication, silent inconsistencies, and the potential for drift between training and production.

This friction was amplified by the nature of the data itself: over 6,000 features spanning customer provided data, credit bureau pulls, Plaid transaction data, and multi-year customer behavior histories. These weren’t trivial aggregates—they required nested joins, temporal logic, and consistent semantics across systems.

At the center of the problem was Mission Lane’s hybrid architecture. The company relied on both:

• Batch workflows, like the Credit Line Increase Program (CLIP), which score hundreds of thousands of customers nightly
• Real-time application decisioning, where features are computed in real time as a user submits an application

In practice, this meant implementing the same feature logic in multiple places, across batch scoring, real-time inference, and model training pipelines, often with subtle differences that introduced inconsistencies.

It wasn’t just about tech debt. You could train a model in Python, but when it went into production, it could get slightly different inputs. That’s a dangerous problem when you’re dealing with credit risk.

Mike Kuhlen ML Solutions & Strategy

To move faster and more safely, the Mission Lane team needed:

• One place to define and calculate features and serve them across both batch and real-time systems
• Support for Python and SQL, without requiring teams to choose between them
• Reliable lineage and observability to satisfy engineering, risk, and compliance needs

Mission Lane chose Chalk as its next-generation feature platform to unify batch and online decisioning and eliminate drift across workflows.

Part of what made Chalk the right fit was its ability to integrate cleanly into Mission Lane’s hybrid architecture and existing tools—Python, SQL, DBT, and Snowflake. Teams didn’t have to change how they worked; they simply plugged into a shared platform that standardized feature logic where it mattered. Chalk’s Kubernetes-native design and support for hybrid-cloud deployments also made it easy to run securely within Mission Lane’s own GCP environment, without introducing new infrastructure complexity.

Chalk now powers both mission-critical model evaluations and emerging use cases beyond machine learning. Every feature defined in Chalk is automatically available across batch jobs, real-time inference, and reverse ETLs, with no need for duplicate engineering work.

Chalk lets us define features once and use them everywhere—whether we’re evaluating two million customers overnight or scoring someone in real time as they apply.

Mike Kuhlen ML Solutions & Strategy

What began as a solution for ML pipelines has become foundational to operations, support, and product.

Mission Lane’s stack resembles many modern fintechs, but their hybrid workload model introduces real complexity. They needed infrastructure that could scale up for batch processing, scale down for low-latency online scoring, and integrate cleanly with their existing platform.

Mission Lane’s modern data stack includes:

• Data warehouse: Snowflake
• Orchestration: DBT, Airflow
• Model development: Python, Jupyter notebooks, DVC pipelines
• Infrastructure: GCP + Kubernetes

Chalk sits across both batch and online environments, supporting:

• Unified feature definitions in Python and SQL
• Consistent execution in batch and real-time environments
• Reverse ETL APIs for production-grade CX and ops-facing applications
• Auto-scaling compute for high-volume batch evaluations, like CLIP

This hybrid architecture—where the same feature logic must support both a single API call and a batch job over millions of rows—is exactly where most tools break down. Chalk made the abstraction seamless.

For us, real-time means computing features while the customer is waiting. Batch means aggregating behavior over time frames from days to years. We use Chalk for both, and it’s the same feature logic either way.

Mike Kuhlen ML Solutions & Strategy

Chalk helps Mission Lane move faster, improve reliability, and extend value beyond its ML team.

Chalk enables the next-generation rollout of CLIP—a core initiative affecting millions of customers.

Mission Lane set out to find a feature store to unify feature logic across batch and real-time ML. What they found in Chalk was a data platform with a feature engine and store built in, powering decisioning across risk, operations, and customer experience.

Adoption continues to expand:

• Faster integration of new bureaus and alternative data sources
• Broader support for explainable credit models through transparent lineage
• New customer features like credit score tracking over time
• Wider use across fraud, CX, and product workflows

Chalk solved the problems we brought it in to solve—and ended up helping us productionize far more of our data than we expected.

Mike Kuhlen ML Solutions & Strategy