- Titan was named AI Startup of the Year at the Tearsheet AI Innovation Awards 2026.
- Titan Founder and CEO Arjun Sirrah breaks down why banking-native AI is different, how it gets deployed, and what’s ahead for the market.
Over the past year, technology vendors have been using a new phrase to describe AI for financial institutions: adapted for banking. Whether the language is “trained on banking data” or “wrapped in a banking interface,” the implication is the same: a general-purpose model was retrofitted for an industry that it was never designed for.
However, this approach equates knowledge to understanding. Banking is not a general knowledge domain; it is a structured system of relationships between products, policies, regulations, risk frameworks, and supervisory expectations that bankers spend years learning to navigate. A model that has read about banking is not the same as a model designed to work within it.
Titan was built for banking. The platform combines three things. Banking-native models trained to reason through real regulatory and supervisory logic. A context layer grounded in both industry knowledge and each institution’s own policies and data. And agents that put that intelligence to work across risk, compliance, underwriting, and operations, all while keeping humans in control.
This architecture earned Titan the AI Startup of the Year Award at Tearsheet’s AI Innovation Awards 2026. Tearsheet spoke with Arjun Sirrah, Titan’s Founder and CEO, about why banking-native AI requires a different foundation, what it takes to reach production inside a regulated institution, and where the market is headed.
Q: Why did Titan choose to build banking-specific models instead of adapting general-purpose LLMs?
Arjun Sirrah, Titan: Banking doesn’t need generalist AI that knows a little about everything. It needs AI that understands the industry at a deep level, including the relationships among products, records, policies, risk tolerances, and both regulatory and supervisory expectations. A general-purpose model wasn’t built to understand why a commercial credit decision differs from a consumer lending decision, how an institution’s policy may differ or be more restrictive than the underlying regulation, or what an examiner will expect to see after that decision is made.
We understood that distinction because our team has lived inside banks. We’ve built and implemented banking products, managed operations and technology, and worked through second-line reviews, audits, and examinations. That experience showed us that banking context wasn’t something to be added at the end. Security, auditability, regulatory reasoning, and institutional accountability had to be designed into the platform.
That’s why Titan combines secure access to foundation models with banking-native models, a banking context layer, and supervised agents. The goal isn’t to make AI sound more like a banker. It’s to give it the structure and context needed to actually reason through real banking work in a way that a banker, risk officer, or examiner can follow and trust.
Q: What was the hardest part of building banking-native models that FIs could actually deploy in production?
Arjun Sirrah, Titan: The hardest part was building a platform around models that could not only produce an accurate, relevant answer, but also one that banks could safely use in a live workflow with confidence and verify.
To reach production, banks must solve several problems all at once. They have to select the appropriate model, protect sensitive information, control access, understand how an output was produced, ground that output in current policies and procedures, document what happened, and determine where human review is required. Then that technology has to fit into an actual workflow without forcing an institution to replace every existing system or redesign its operating model overnight. Assembling all of those pieces independently and correctly is a genuinely hard problem.
That’s why Titan was built around three components that work together: banking-native models, context, and agents. The models include Titan’s Banking Model, designed for the precision and regulatory reasoning banking requires, alongside secure access to frontier LLMs, routed to the right model for the task. The context layer grounds every output in a proprietary knowledge graph that encodes how banking works across products, regulations, and risk frameworks, and in some deployments, a second knowledge graph or ontology built from the institution’s own policies, procedures, and data. The agents then connect that intelligence to real banking workflows, handling the searching, retrieving, and documenting, while surfacing recommendations to human experts for review and final decision. Production readiness comes from making all three work as a single system rather than assembling them independently.
Q: What is one lesson you’ve learned about building banking-specific models that surprised you?
Arjun Sirrah, Titan: A key lesson is that governance doesn’t have to slow AI adoption. When it’s designed in from day one, governance can actually accelerate it.
Banks aren’t resistant to useful technology, but they’re risk-averse by nature, and their hesitation usually comes from unanswered questions, things like: Where is the data going? Which model is being used? What information informed the response? Can we reconstruct what happened? Who reviewed the recommendation? If those questions are addressed only after a pilot, the project will likely stall just as it begins to show value. But if these are answered right at the start as part of the design itself, the various teams can evaluate the same system together.
We’ve also learned institutions don’t have to start with the most autonomous or complex use case. They can begin by giving employees a secure, governed alternative to unapproved shadow AI tools, then ground those outputs in the institution’s approved policies and procedures, and finally introduce supervised agents into targeted workflows. This way, governance is the foundation for scaling, allowing an institution to move from experimentation to production with greater ease and confidence, rather than a gate at the end of innovation.
Q: Titan’s Banking Agents automate underwriting, risk, and compliance workflows. What makes banks comfortable trusting these models and agents with higher-stakes decisions?
Arjun Sirrah, Titan: Trust begins with defining the AI agent’s role correctly right from the start. Titan’s agents aren’t designed to replace the accountable banker or make opaque final decisions. They’re designed to do the legwork: collect the needed information, retrieve the relevant policies and procedures, analyze that data against those requirements, and finally produce a recommendation for a human to review. The banker remains responsible for the judgment and review at all the appropriate control points, and ultimately, the final decision.
Several design principles reinforce this approach. The agents are grounded in the institution’s own products, data, policies, and risk tolerances. Their work is logged, traceable, and reviewable at any time. They combine AI reasoning with defined tools and approval steps, and they surface their findings to human experts rather than hiding the process behind a black-box answer. This gives risk, compliance, and business teams the ability to inspect not only the recommendation, but the context and reasoning behind it.
Institutions become much more comfortable when AI expands the capacity of their people without removing the judgment or accountability. The machines handle the repetitive but important tasks: the searching and retrieving, the staring and comparing. This lets the people focus on the actual decision-making that requires experience, interpretation, and responsibility.
Q: Since launching in 2025, what has been the biggest change you’ve seen in how banks approach AI adoption?
Arjun Sirrah, Titan: AI is beginning to be treated less as an isolated experiment and more as a new operating layer within banking infrastructure. That is the biggest shift we’ve seen since launching in October 2025, and it has happened faster than most expected.
Early conversations were mostly centered on experimentation, model selection, and general-purpose productivity. Banks were testing tools, but many hadn’t really established the path from an individual use case to a fully deployed, controlled enterprise capability. Increasingly, the conversation is now about production: replacing ungoverned AI usage, grounding outputs in actual institutional knowledge, selecting workflows with clear operational value, and building the auditability and human oversight required to scale both responsibly and effectively.
What’s also emerging is a deeper realization about why so many early AI deployments underdelivered. It wasn’t the model. It was the absence of banking context. General-purpose models don’t understand the relationships between a bank’s products, policies, regulatory obligations, and risk frameworks. Without that structure, outputs may sound reasonable but don’t reflect how banking actually works. Institutions are starting to ask harder questions about what knowledge their AI is reasoning from, and whether that knowledge was built for banking or borrowed from somewhere else entirely.
We’re also now seeing institutions think more holistically. They’re no longer looking for a chatbot or a single-point solution. Instead, they’re examining their data, processes, and operating models as a whole, and asking how AI can improve the way the institution works across risk, compliance, underwriting, and operations if the right context is invoked at the right time. Titan’s rapid growth since emerging from stealth, and the pace at which institutions are replacing generic, ungoverned tools with governed, auditable capabilities, reflect that shift.
Facts Only
* Titan was named AI Startup of the Year at the Tearsheet AI Innovation Awards 2026.
* Banking-native AI is distinct from adapting general-purpose models by focusing on industry structure.
* Banking involves a structured system of relationships between products, policies, regulations, risk frameworks, and supervisory expectations.
* Titan’s platform combines banking-native models trained to reason through regulatory and supervisory logic.
* The system includes a context layer based on industry knowledge and institutional policies/data.
* Agents are used to apply this intelligence across risk, compliance, underwriting, and operations while keeping humans in control.
* Building deployable models required addressing model selection, data protection, access control, output understanding, grounding in policies, and workflow integration simultaneously.
* Production readiness stems from making the banking-native models, context, and agents function as a single system.
* Trust is maintained because agents are designed to perform legwork (collecting information, retrieving policies) and surface recommendations for human review and final decision.
* A lesson learned is that designing governance in from day one can accelerate AI adoption rather than slow it down.
Executive Summary
Titan’s approach centers on building banking-native AI by moving beyond retrofitting general-purpose Large Language Models (LLMs). The core argument is that banking requires specialized understanding of structured relationships between products, policies, regulations, and risk frameworks, which generalist models lack. Titan addresses this by combining banking-native models trained in regulatory logic, a context layer grounded in industry knowledge and institutional data, and agents designed to execute these insights within workflows.
The difficulty in deployment lies in creating a production system where the AI not only provides accurate answers but can operate safely within regulated environments. To achieve production readiness, the platform must manage complex challenges like model selection, data security, access control, output traceability, grounding in institutional policy, and workflow integration without requiring wholesale system overhauls.
A key lesson learned is that governance should be integrated into the design from the start rather than applied retrospectively. This approach allows for governance to accelerate adoption by answering inherent risk questions—such as data provenance, model selection, and auditability—at the foundational level. Trust in AI agents is established by ensuring they function as assistants that surface recommendations based on verifiable internal context, rather than making final decisions.
Full Take
The narrative demonstrates a structural tension between the agility of general-purpose AI and the necessity of the rigor inherent in regulated financial systems. The pattern emerging is the recognition that domain-specific knowledge is not an additive layer but a foundational prerequisite for reliable operation, suggesting that superficial adaptation leads to systemic failure when applied to high-stakes environments like banking.
The shift from viewing AI as an isolated experiment to viewing it as an operating layer within infrastructure reflects a maturation of institutional adoption. The core implication is that the barrier to entry for enterprise AI in finance is not technological capability but contextual accountability. When institutions move from experimentation to production, they are implicitly demanding traceability and accountability mechanisms baked into the architecture, forcing AI design to align with existing auditability mandates rather than merely mimicking user interfaces.
The strategy of grounding agents in proprietary knowledge graphs and policies addresses a fundamental failure mode of early deployments: the absence of verifiable context leads to plausible but irrelevant outputs. This suggests that successful adoption relies on establishing trust through transparency into the reasoning chain, where human oversight shifts from validating a single answer to overseeing a documented process for generating recommendations based on explicit institutional constraints. The pattern indicates that true innovation in regulated fields occurs when the technology is forced to solve compliance and risk challenges intrinsically, rather than layering them on top later.
Bridge Questions: If governance accelerates adoption, what specific metrics or mechanisms should financial regulators use to assess the inherent safety of banking-native reasoning versus generalist reasoning? What are the long-term consequences for institutional knowledge transfer if operational workflows become entirely dependent on proprietary context layers? How can the industry develop a standard framework for verifying that "context" is sufficiently robust and legally compliant across disparate institutional models?
Sentinel — Human
This analysis appears to be based on an interview transcript, characterized by a specific, grounded voice that weaves technical concepts with real-world institutional experience.