AI & Machine Learning
LLM Deployment Services: A Buyer's Guide for CTOs
What LLM deployment services should cover: serving, autoscaling, gateways, evaluation, security and cost control, plus the deliverables to insist on.
15 October 20268 min read
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All articlesCategory
AI & Machine Learning
Practical articles on building, deploying and running machine learning and LLM systems in production: RAG, fine-tuning, MLOps, GPUs and evaluation.
These articles are about the engineering that turns a model into a dependable production system. They cover retrieval-augmented generation, fine-tuning, evaluation frameworks, MLOps pipelines, Kubernetes and GPU serving, and the data engineering that every AI project depends on.
Each piece is written from delivery experience rather than vendor marketing, so expect trade-offs, failure modes and the checks we run before anything reaches customers. If you are moving an AI prototype towards production, start with the RAG architecture and LLM evaluation guides, then read about serving costs and infrastructure.
Software Development
Deterministic AI Workflows vs Autonomous Agents in 2026
Autonomous agents were the 2025 story. In 2026 enterprise teams are rebuilding them as deterministic workflows with LLMs as components. Here is our pattern.
1 September 20269 min read
Cloud & DevOps
How to Cut an Enterprise LLM Inference Bill in 90 Days
A worked example of cutting enterprise LLM inference spend in 90 days with caching, routing and serving-tier changes, without downgrading the model.
25 August 20269 min read
Cloud & DevOps
GPU Inference Autoscaling: Where Kubernetes HPA Fails
The default Kubernetes autoscaler was built for stateless web apps. Here's the GPU-aware inference autoscaling stack we deploy for enterprise LLM workloads.
18 August 202610 min read
Software Development
Building an Internal LLM Gateway for Enterprise AI
Every enterprise AI programme reaches a point where direct model calls stop scaling. Here's the LLM gateway pattern we deploy, and why.
4 August 202610 min read
Cloud & DevOps
AWS vs Azure vs GCP for AI Workloads: 2026 Verdict
AWS, Azure or Google Cloud for enterprise AI? Compare GPU access, managed ML platforms, model choice, real costs and lock-in, with a clear verdict per cloud.
29 July 202614 min read
Software Development
The Silent Test Debt Building in AI-Assisted Codebases
Why AI-assisted teams ship more tests but more production bugs — and how to measure mutation scores and requirement-test coverage to fix your safety net.
21 July 20269 min read
AI & Machine Learning
Cut LLM Inference Latency Without a Model Upgrade
Where the time really goes in a slow enterprise support copilot, and the architectural changes that fix it without a frontier model upgrade.
23 June 20269 min read
Cloud & DevOps
GPU FinOps: Cutting Enterprise AI Infrastructure Spend
A practitioner's playbook for controlling GPU and AI infrastructure costs at scale — the wasteful patterns we see most and the levers that move spend.
16 June 202610 min read
Software Development
Production LLM Eval Framework: Catch Regressions
How to build an LLM evaluation framework for production: golden datasets, LLM-as-judge scoring, CI gates and live sampling that catch regressions early.
13 May 202610 min read
Software Development
AI Coding Agents for Enterprise Engineering
A practitioner's guide to deploying AI coding agents in enterprise engineering: governance, review patterns, ROI math, and the failure modes nobody talks about.
28 April 20269 min read
Software Development
MLOps CI/CD Consulting: How We Build ML Pipelines
Looking to hire MLOps or CI/CD help? Scalexa, behind 500+ projects, builds ML pipelines, validation gates, and automated delivery for AI systems.
18 March 202610 min read
Software Development
Running ML Workloads on Kubernetes: A Practical Guide
How to configure Kubernetes for machine learning: GPU scheduling, resource management, model serving, and common pitfalls from real production deployments.
14 March 202612 min read
Software Development
RAG Architecture in Production: Enterprise Lessons
Why most RAG systems fail in production — and how to fix them. Chunking strategies, retrieval tuning, and hard lessons from real enterprise deployments.
10 March 202611 min read
AI & Machine Learning
Enterprise LLM Fine-Tuning: When It Beats RAG
Should your enterprise fine-tune an LLM or use RAG? A practical decision framework, the hidden costs and five production patterns from real deployments.
3 March 202611 min read
AI & Machine Learning
How to Deliver a Production ML System in 6 Weeks
A week-by-week plan for taking a machine learning system from concept to production in six weeks, with an illustrative logistics example.
12 February 20268 min read
Software Development
Data Engineering for AI: The Foundation Teams Skip
Data engineering is 80% of AI success. Discover how modern lakehouse architectures and feature stores build the foundation for production machine learning.
5 February 20267 min read
Software Development
The Hidden Costs of Technical Debt in AI Systems
Uncover the hidden costs of AI technical debt. Learn how to manage data dependencies, configuration debt, and model drift in enterprise ML systems.
22 January 20267 min read
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