Building Production-Ready RAG Pipelines
A comprehensive guide to designing and deploying Retrieval-Augmented Generation systems that scale.
Building reliable cloud & AI systems
for real-world impact
CoreSyntax Technology designs and builds cloud-native, AI-driven, and platform-grade software systems focused on trust, performance, and long-term maintainability.
What We Deliver
From proof-of-concept to production, we engineer systems that teams can trust, operate, and scale — without the typical pitfalls of runaway complexity.
AI & GenAI Platforms
Production-grade LLM systems, RAG pipelines, inference optimisation, and cost-aware AI architectures built to handle real enterprise load.
Cloud & Platform Engineering
AWS-native systems, Kubernetes platforms, distributed services, and scalable backends designed for operational clarity from day one.
System Design & Architecture
Greenfield builds and legacy modernisation with clear trade-off documentation and architectures that evolve without chaos.
MLOps & Production ML
End-to-end ML pipelines with full observability, zero-downtime model updates, and automated retraining workflows at scale.
Our Work
A selection of engineering outcomes delivered across enterprise environments — cloud modernisation, AI platforms, and systems built for scale.
AI & GenAI
Enterprise RAG Platform
Designed and deployed a Retrieval-Augmented Generation platform for an enterprise client — reducing query latency by 50% while serving production traffic at scale. Full observability, cost guardrails, and zero-downtime model rollouts included.
Cloud Engineering
Monolith to Microservices
Modernised a high-traffic monolithic system into an event-driven, cloud-native architecture — doubling deployment velocity and cutting infrastructure costs by 30% through right-sizing and autoscaling.
MLOps
Production ML Pipeline
Built a production ML pipeline with automated retraining, model versioning, and A/B rollout — handling 10M+ daily requests with full distributed tracing and alerting from feature ingestion through inference.
Platform Engineering
Shared Kubernetes Platform
Architected an internal developer platform serving 200+ microservices — GitOps workflows, RBAC policies, automated scaling, and a self-service model that reduced onboarding from weeks to hours.
Expertise & Track Record
Over 15 years delivering production systems across global technology companies — from telecoms and fintech to cloud-native SaaS. Deep specialisation in AI/ML engineering, distributed systems, and cloud platform architecture.
Research to Production
Practical application of large language models in production environments — building generative AI systems that reduce operational overhead while improving accuracy and reliability.
Cloud-Native Architecture
Distributed systems design with clear trade-off awareness. Modernising legacy monoliths into event-driven, cloud-native architectures that scale under real-world load without breaking.
Engineering at Scale
Consistently recognised across global technology leaders for architectures that move business metrics — from Kubernetes migrations to zero-downtime GenAI deployments at enterprise scale.
15+
Years enterprise delivery
4
Core service areas
Global
Remote-first
Sectors Served
Telecoms
Fintech
Cloud SaaS
Enterprise Software
Media & Broadcast
Delivery Scale
- 10M+ daily requests in production
- 200+ microservices on a single platform
- 50%+ latency reductions delivered
How We Work
Great systems are built by combining deep technical expertise with pragmatic decision-making — no hype, no shortcuts.
Design with Intent
Clear trade-offs, documented decisions, and architectures that evolve without chaos.
Build for Production
Every system is designed to be observable, secure, and maintainable from day one.
Optimise for Reality
Performance, cost, and reliability matter more than hype. We measure what matters.
Technical Blog
Deep dives into cloud architecture, AI systems, and engineering best practices.
AWS Cost Optimisation Strategies
Proven techniques to reduce cloud costs without sacrificing performance or reliability.
Designing Observable Distributed Systems
How to build systems that engineers can understand, debug, and trust in production environments.
Get in Touch
Interested in working together or discussing your next project? Let's talk.