Digital Trading Platform Case Study

The challenge

A global insurer's digital trading and product onboarding process was fundamentally broken - not because of performance, but because of architectural rigidity. Every business decision required engineering intervention, turning the technology team into a bottleneck for commercial operations.

⚠️ Manual code change required for every new product. Each new insurance product launch - a new pricing tier, coverage variation, geographic rollout, or distribution channel - required a developer to write or modify code. Business teams were entirely dependent on engineering calendar availability, and the development cycle for a single product change could span 3–4 weeks.

⚠️ Long onboarding cycles delaying time-to-market. By the time a new product was ready to trade, the market window had often shifted. Competitors using more agile platforms were launching comparable products faster, putting the insurer at a structural commercial disadvantage that couldn't be solved by hiring more engineers.

⚠️ High operational cost of product maintenance. Engineers were spending a significant proportion of sprint capacity on configuration-style tasks - updating product rules, adjusting pricing matrices, modifying eligibility criteria - work that in a well-designed system would be done entirely through configuration tools with zero engineering involvement.

⚠️ No intelligent automation for complex product variations. Insurance products have complex combinatorial logic - hundreds of variations across coverage levels, excess amounts, add-ons, and customer segments. Without AI-driven automation, this logic required extensive manual specification, testing, and validation effort for every change.

Before & after

Before OKRUTI

⏱ 3–4 weeks per product change

👨‍💻 Engineering required for every launch

🔗 Monolithic - one change risks everything

🤷 No AI - all logic hand-coded

📈 High dev cost for config tasks

After OKRUTI

⚡ New product live in hours, not weeks

🖱️ Business teams configure without engineering

🔀 Microservices - isolated, independently scalable

🤖 AI handles complex variation logic automatically

💰 Engineering focused on innovation, not config

Our solution

OKRUTI engineered a microservices-based digital trading platform with AI capabilities for intelligent automation - and crucially, a configuration-driven product management layer that allowed business teams to launch and modify products with zero engineering dependency.

→ Configuration-Driven Product Engine: Built a product definition framework where all product attributes - pricing rules, eligibility criteria, coverage structures, add-on configurations, distribution channel rules - are stored as structured configuration data, not code. Business teams use an admin interface to create, modify, and publish products with no engineering involvement required.

→ AI-Driven Automation: Integrated AI capabilities for intelligent handling of complex product variations. The system learns from existing product configurations to suggest pricing bands, flag configuration conflicts, and auto-validate new product setups against regulatory requirements - eliminating the manual specification effort that previously slowed each launch.

→ Event-Driven Microservices Architecture: Decomposed the monolith into a set of domain services - Product Catalogue, Pricing Engine, Eligibility Checker, Quote Generator, Policy Issuer, Document Producer - communicating via events. A change to the pricing rules for one product does not require redeployment of any other service.

→ Smart Validation Engine: Built an AI-assisted rules engine that processes onboarding logic dynamically. When a business user configures a new product, the validation engine checks for pricing logic contradictions, regulatory compliance gaps, and configuration completeness - providing instant feedback before the product goes live.

→ Fault Isolation Between Products: Each product's services run in isolated contexts. A misconfigured product can be rolled back without affecting the 50+ other active products on the platform. Canary deployments allow new products to be tested under live traffic conditions before full rollout.

→ Azure Cloud Infrastructure: Deployed on Microsoft Azure with auto-scaling groups, managed Kubernetes orchestration, and blue/green deployment pipelines. The platform can scale horizontally during open-window trading periods and scale down during off-hours, significantly reducing cloud infrastructure costs versus the previous always-on monolith.

Results achieved

5×

Faster product launch - from 3–4 weeks per product to hours using the new config-driven engine

Zero

Engineering changes per new product - business teams fully self-serve on the product management layer

Intelligent onboarding automation - AI validates new product configurations and handles complex variation logic

↓

Operational cost reduction - engineering capacity redirected from configuration tasks to platform innovation

We used to need two weeks and a sprint allocation to launch a new insurance product. Now our commercial team does it themselves in a morning. The AI validation catches configuration issues we would have only found in UAT. The ROI on this engagement was faster than any technology project in recent memory.

- VP of Digital Trading, Global Insurance Group

Modernising a trading or product platform?

Whether it's insurance, financial products, or any complex configurable platform - we can help you move from code-per-product to configuration-driven, AI-augmented delivery.

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Speeding Up Digital Trading
with a Microservices & AI Platform

Modernising a trading or product platform?

Let's talk about your platform.

Speeding Up Digital Tradingwith a Microservices & AI Platform

Modernising a trading or product platform?

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Speeding Up Digital Trading
with a Microservices & AI Platform