AI strategy, architecture and implementation
The difficult part isn't getting an AI demo to work. It's making the system reliable, grounded, complete, auditable and deployable enough for real financial workflows.
SmartApp combines 25 years of financial-services engineering with hands-on experience building and hardening AI systems for complex regulated workflows.
Strategy through implementation
We help financial institutions decide where AI belongs, design the controls it needs and build the resulting system.
Make sound decisions before technology choices become expensive. We identify where probabilistic AI creates value, where deterministic systems should remain in control and how people fit into the workflow.
Build AI that can be inspected, tested and operated. We engineer the controls around probabilistic models so outputs are grounded in source evidence and failures are visible and reviewable.
Choose a deployment model that fits the information being processed and the controls your organisation must satisfy. Private deployment is an architectural decision, not a claim that every model or region is interchangeable.
Financial-services engineering first
SmartApp Solutions Ltd is an established financial technology and engineering business. We bring the discipline of enterprise financial systems to AI: explicit controls, testable behaviour, operational visibility and clear accountability.
Our founder, Ihab Salem, has worked with HSBC, Credit Suisse, RBS, Schroders, Mizuho and RBC. Today, SmartApp helps financial institutions move from AI strategy to working systems for controlled, high-stakes workflows.
Architecture recommendations are grounded in what can actually be built, integrated and operated.
Probabilistic models are bounded by evidence, deterministic logic and meaningful human-review states.
Burhan.finance is a separate product built by SmartApp for complex Shariah review and Islamic-finance workflows. Building and hardening it exposed failure classes that polished AI demos often hide: unstable conclusions, incomplete coverage, unsupported claims, lost transaction context and unclear human-review states.
We know these problems because we have had to engineer controls for them.
Visit Burhan.financeInsights
Practical notes on what it takes to build reliable AI for financial services: model instability, evidence grounding, coverage, document intelligence, human review and deployment.
A practical first conversation
Talk to us if you're moving a use case from prototype to production, dealing with inconsistent model behaviour, designing controlled document AI or evaluating private deployment and data-residency constraints.
Leadership
Founder, AI Strategy & Implementation Lead
Ihab has 25 years of experience building enterprise systems for financial institutions, including Royal Bank of Scotland, Credit Suisse, Mizuho Bank, Schroders Asset Management, HSBC Asset Management and RBC BlueBay Asset Management.
He now combines that financial-systems background with hands-on AI architecture and engineering, including the design and build of Burhan.finance.
Let's talk
Whether you're shaping an AI use case or trying to make an existing system dependable, send us a note and we'll get back to you.