Core banking modernization, high-throughput lending automation, risk mitigation, and secure compliance engineering. For 20+ years, Azati has been the trusted engineering partner financial institutions rely on to design, scale, and maintain mission-critical systems.
Estimate your project costModern banking and fintech technology leaders are managing three conflicting priorities simultaneously, where every architectural mistake carries heavy operational and regulatory penalties.
Core systems running on COBOL, legacy .NET, or older Java versions are stable, but they reject modern business needs: open APIs, real-time settlement, and cross-border integrations. Full replacement is too risky. We specialize in phased, evidence-based modernization – mapping undocumented "as-is" processes into clean BPMN/Sequence structures and splitting monoliths into agile microservices without breaking accounting consistency.
SOC 2, PCI-DSS, DORA, GDPR, and the EU AI Act require deep systemic traceability. When a regulator asks how an automated decision or transaction occurred, the answer must be instantly inspectable. We build audit logging, secure HMAC key storage, cryptographic workflows, and human-in-the-loop checkpoints directly into the software core.
AI pilots fail after 90 days because models trained on clean data struggle with raw, messy production feeds. We bridge this gap by engineering robust orchestration layers, asynchronous background processing, and confidence-based scoring models for automated transaction categorization and risk underwriting.
Concrete engineering across the full BFSI stack – from high-volume ledgers to multi-venue exchange infrastructure, drawn from 20+ years of industry-focused delivery.
Account management, deposit operations, double-entry bookkeeping, and automated billing engines. We design and optimize distributed financial ledgers processing millions of transactions per day, and build cost-optimized event processing systems that scale via indexing and batching optimization rather than expensive database upgrades.
Loan origination, automated application ingestion, financial scoring engines, and microservice refactoring. We have decoupled complex monolithic lending systems into 35+ independent microservices, dramatically increasing application processing throughput.
End-to-end engineering of multi-bank aggregation platforms, secure API architectures, and automated payment routing. We seamlessly integrate third-party fintech ecosystems (Plaid, Stripe, Clearbank) and embed automated AML/sanctions screening workflows (Mastercard, Moody's).
High-concurrency trading platforms with real-time market data streaming, order matching engines, and multi-venue exchange aggregators. Our teams execute deep infrastructure upgrades (e.g., migrating enterprise trading cores to Java 25 and Spring Boot 4) with zero downtime and full end-to-end trace propagation via Prometheus and Micrometer.
The amount of time which Azati Software's work has saved our company, as well as me, has been fantastic. We typically look at what we need, and at what can be automated, and get Azati Software to do the work. What might take me three or four days to figure out how to do, Azati Software can do in a couple of hours because of their existing knowledge. I've recommended Azati Software on multiple occasions, to anyone looking for their types of services.
A polyglot engineering core, with the depth to modernize enterprise estates and the breadth to meet financial institutions on whatever stack they run.
The workhorse of enterprise banking. Azati's BFSI Java work spans Java 8 → 21/25 migration programs, Spring Boot 2 → 4 upgrades, microservice decomposition of monoliths, and Spring AI / LangChain4j integration for AI features inside existing enterprise systems.
For BFSI clients with Microsoft estates: .NET application development, Agentic AI on Semantic Kernel + Azure OpenAI for production AI agents, SharePoint and document-management migrations, and DevOps automation on Azure.
Risk modeling, ML pipelines, trading-system computation cores, data engineering. FastAPI, Pandas, NumPy, scikit-learn, Celery – the toolkit for the parts of BFSI where math has to meet production engineering.
| Layer | Technologies |
|---|---|
| Backend & Core Languages | Java (8 to 25), Spring Boot (2.4 to 4), .NET / C#, Go (Golang), Python, Ruby |
| Digital Banking & Frontend | React, Angular, Vue.js, SvelteKit, Native iOS & Android, Hotwire / Stimulus |
| Enterprise Ledgers & Databases | PostgreSQL, Oracle, Tibero, MSSQL, DB2, DynamoDB, Tigerbeetle, MongoDB, Redis |
| Middleware & Event Streaming | Kafka, RabbitMQ, AWS EventBridge / EventBus, Camunda BPMN, Firebase |
| FinTech APIs & Integrations | Plaid, Stripe, Mastercard API, Clearbank, Moody's Analytics, DocuSign, OpenAPI / REST |
| Cloud, DevOps & Observability | AWS, Azure, GCP, Render, Pulumi (Go/TS), Docker, GitLab CI, GitHub Actions, AWS CodePipeline, Datadog, Prometheus, Micrometer, Grafana |
| AI, ML & Automation Core | OpenAI API (ChatGPT), Claude Code, Spring AI, LangChain4j, Semantic Kernel, scikit-learn, Pandas, NumPy |
The challenge
A monolithic loan processing application limited operational scalability, causing delays in loan amendment workflows and high maintenance overhead.
The solution
Azati mapped the undocumented backend workflows into clean architectural diagrams and split the monolith into 37 distinct microservices.
The outcome
3× increase in loan-amendment throughput, a 61% reduction in case resolution time, and a highly maintainable infrastructure with independent service deployment.
Read full case studyThe challenge
A fintech mortgage platform experienced critical data synchronization errors between its scoring and calculation engines right before launch.
The solution
Our QA team isolated a 5–7% calculation drift caused by race conditions during parallel requests and unaligned caching layers under high load. We re-engineered the cache synchronization logic.
The outcome
Reduced calculation variance to 0%, lowered API error rates from 8% to <0.5%, and added full concurrent-access automated test coverage for several thousand applications daily.
The challenge
An international payment intermediary faced an urgent technical debt crisis and a critical infrastructure engineering shortage in the UK.
The solution
Azati deployed dedicated Cloud/DevOps engineers within 48 hours. We rebuilt the cloud-native infrastructure using Pulumi (Go/TypeScript), integrated Mastercard and Clearbank APIs, and deployed Tigerbeetle for high-speed ledger consistency.
The outcome
Unblocked the internal engineering leadership, automated the CI/CD pipelines via AWS CodePipeline, and established robust monitoring using Datadog and Grafana.
No security-stamp marketing, no synthetic case studies — just the reasons real banks, exchanges, and fintechs keep Azati engineering their mission-critical systems.
We fill critical engineering and DevOps talent gaps in days, not months. We adapt immediately to your internal task management (Linear, Jira) and communication tools.
Average engineer tenure at Azati is 8+ years. The team that modernizes your core banking application is the same team that supports it through future regulatory shifts.
Whether your infrastructure relies on standard open-source tools or custom, bank-proprietary internal frameworks, our polyglot engineering team learns and masters your environment quickly.
We don't patch security compliance at the end of a sprint. Data encryption, signed webhook verification, role-based access control (RBAC), and automated audit logs are baked into our daily code commits.
Unlike traditional outsourcing with its rigid contractual bottlenecks, Azati aligns with your real business risks and budget limits. Learn more about our engagement models.
Auction.com. Venstar Exchange. Algowave. MedPro Group. DOXA. Plus dozens of named and anonymized engagements with banks, exchanges, insurers, brokers, and financial-services firms.
We apply an evidence-based migration approach. Before writing code, our business analysts map the "as-is" state into strict BPMN structures. We run parallel accounting ledgers during the migration phase, utilize automated data reconciliation scripts, and execute high-concurrency regression testing to guarantee zero ledger drift and total consistency.
Yes. Our engineers have extensive experience working with non-standard, internal software ecosystems developed natively by banks. We analyze undocumented code, write comprehensive technical documentation, and build clean integration layers on top of proprietary systems.
We implement AI-assisted workflows – such as transaction categorization – strictly as asynchronous background processes with confidence-based threshold filters. Decisions below safety thresholds are automatically routed to human operators. Every model response is logged alongside its production context to ensure full inspectability for financial auditors.
Don't let legacy technical debt or talent shortages delay your product releases. Tell us about your system architecture challenges – whether it is core ledger migration, API integration, or urgent DevOps automation. A senior financial software architect will review your request and schedule a technical walkthrough within one business day.
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