Technical problem
Monolithic AI applications collapse under heavy user load or sudden traffic spikes, cannot scale, and take the rest of the system into deadlock with them.
AI-ready microservice and distributed system architecture
Architectural solution
We design AI services as independent microservices on Docker and Kubernetes. Separating inference servers, vector databases and background job queues from one another lets us define auto-scaling rules for each. Load balancers and GPU cluster management distribute resource use against demand as it happens.
Operational outcome
Industrial-standard infrastructure that stays uninterrupted and elastic at global scale, even under heavy concurrent traffic, with a 99.9% uptime guarantee.
II
Software adaptation, legacy systems and microservice architecture
Legacy system modernisation and intelligent API layersThe previous-generation ERP, CRM, mainframe or bespoke database systems a business has run for years — the ones at the centre of its workflows — cannot technically accommodate AI integration.Two-way AI integration across departments and business softwareAI stays a static chat window that only produces text, and never turns into action inside the business software already in use — HR, sales, operations, finance.
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