AI that empowers all your bankers
Complex decisions for complex entities carry real consequences. The bankers who make them carry hard-won expertise. We built RDC.AI around one conviction: the right AI unlocks that expertise and puts it to work at a scale no team can reach alone.
Context
Commercial and Business Banking is hard to get right. Most AI makes it harder
Most banking AI was designed for consumer credit: millions of records, standardized data, decisions that tolerate a margin of error. The Commercial and Business portfolio is different in every dimension that matters. Four realities define the challenge.
Not Enough Data
Commercial portfolios contain hundreds of records, not millions, and those records are further fragmented by industry and region. The statistical foundations behind Consumer AI simply don't exist here.
Many Data Sources
Understanding a business means connecting transaction accounts, lending accounts, financial statements, and bureau data. That data is scattered across systems, and it's inconsistent and messy. Getting it right is table stakes before any AI can add value.
Every Day Decisions Must be Explainable
Regulators require it. Risk teams demand it. Bankers need it to act with confidence. That is why we built Glass Box AI and its core capability, Self-Describing Decisions.
The Best Knowledge Lives in People
Your best bankers carry insights no dataset captures. The question is not how to replace that knowledge - it is how to scale it.
Philosophy
Teachable Intelligence
We are not here to replace your bankers. We are here to make them extraordinary. Teachable Intelligence is the design philosophy behind every choice in the RDC.AI platform, the relationship between human expertise and AI, applied consistently across every customer, every decision, every day. The more your team uses it, the sharper it gets.
Our research shows that combining machine learning with knowledge management produces more accurate predictions in commercial banking than either approach alone - especially given the small-dataset reality of the domain. The platform makes this combination practical and repeatable.
The design choices that set us apart
We made design choices that most vendors avoid.
Start with Data Problem, Not the Model
Most AI projects in commercial banking stall because the data isn't ready. We treat data infrastructure as a core product capability: a purpose-built pipeline for business and commercial banking signals. That means your AI has something real to work with from day one.
Rules and AI belong together
Rules encode your policy, your regulation, your red lines. AI surfaces the patterns rules cannot anticipate. Every decision strategy can be purely rule-based, purely model-driven, or a calibrated blend of both. You decide.
Transparency is not a Feature. It is the Foundation.
Explainability added after the fact does not work. Glass Box AI is our explainability architecture, and Self-Describing Decisions are the capability at its core: every prediction, every decision, and every recommended action generates a traceable explanation at the moment it happens. It's baked in, always on, always available.
Every Decision should make the next one better
Static strategies and models become stale. We track every decision against its intended result. Did the early warning flag the right customer? Did the opportunity signal convert? That feedback sharpens strategies. Our platform makes it easy to understand, and easy to adopt these improvements.
Methodology
The Decision Intelligence Cycle
The Decision Intelligence Cycle (DIC) is the repeatable framework that turns Teachable Intelligence into deployed capability. Each deployment contributes to a growing library of reusable templates - data sensors, feature logic, model architectures, decision strategies - validated in production. What took months on the first deployment takes weeks on the tenth.
Design
From raw data to validated decision strategy — rule-based, model-driven, or a hybrid depending on what the decision requires.
Execution
Run strategies in production with Glass Box AI Self-Describing Decisions attached to every outcome, giving your risk team evidence they can stand behind.
Monitoring
Measure what matters: did the decision achieve its purpose? What matters isn't model accuracy alone, it's real business outcomes.
Refinement
When the evidence says sharpen the strategy, we do. Each cycle enriches the templates that accelerate the next deployment.
Responsible AI
Six Principles for AI
Every AI system we build is designed and operated against six principles that apply uniformly across predictive AI, agentic AI, and any use of foundation models.
Human Centered Values
AI augments human capability, not replace human judgment. Every system has a documented purpose defined with the customer, with mechanisms to detect value drift.
Reliability and Safety
Accuracy thresholds are set at design time, validated before go-live, and monitored continuously. Limitations are disclosed and escalation pathways defined in advance.
Transparency and Explainability
Glass Box AI delivers explainability through Self-Describing Decisions: a controlled-language explanation generated for every prediction, decision, and recommended action. Each explanation is traceable from input data through to outcome and is designed to satisfy regulatory scrutiny, not just internal review.
Privacy and Security
Customer data is never shared with foundation models without explicit, scoped consent.
Infrastructure is certified to ISO/IEC
27001:2022 and attested to SOC 1 and SOC 2 (Type 2). Governance logs record every element accessed for every autonomous decision.
Fairness
Training data is assessed for bias and
underrepresentation before model
development. Fairness criteria are tailored to each customer’s regulatory context and monitored for drift.
Accountability
Responsibility is shared and clearly defined. RDC.AI provides the platform, governance framework, and monitoring. Customers retain control of configuration, use case design, and operational decisions.
Know more. Do more. Grow more. Safely.
Most vendors sell predictions. We build AI that gets better at understanding your portfolio, your customers, and your risk appetite with every decision it makes - one that becomes specific to your institution, your market, and your risk culture. When you know more about your customers, you can do more for them.
“When you know more about your customers, you can do more for them. When you do more, you grow more. Safely and reliably. That is why we built RDC.AI.”