Engineering deep-dives, strategy playbooks, and industry analysis from the Susea.ai team.
As LLMs transition from experimental playgrounds to the backbone of enterprise infrastructure, the economic reality of token generation has become the primary bottleneck for scalability.
Dr. Elara Vance
Head of Engineering
Most companies using AI are unknowingly ceding their most valuable asset — their institutional knowledge — to third-party platforms. Here's how to take it back.
Dec 1, 2024Why most generative AI prototypes fail at scale and the structural changes needed to bridge the gap between demo and production.
Nov 15, 2024The CAIO role is no longer optional for enterprises serious about AI. Here's what the mandate actually entails — and why most companies are getting it wrong.
Sep 10, 2024As AI takes over procurement, routing, and demand forecasting decisions, supply chain leaders face a new obligation: being able to explain what their algorithms decided and why.
Aug 22, 2024Low-code ML platforms are maturing fast. Here's an honest assessment of where they deliver genuine value — and where they create hidden technical debt.
Jul 15, 2024Enterprise-scale neural network deployment was once the exclusive domain of companies with nine-figure AI budgets. The infrastructure economics have shifted dramatically — here's how SMBs can compete.
Jun 28, 2024As generative AI moves into customer-facing and internal enterprise workflows, the attack surface expands dramatically. These are the guardrails every production deployment needs.
Jun 5, 2024Agentic AI — systems that plan, act, and learn over extended time horizons — is moving from research labs to production. What changes when AI stops answering questions and starts taking actions?
May 20, 2024Measuring the ROI of AI investments is harder than most frameworks admit. Here is a rigorous model that accounts for both direct returns and the hidden costs that rarely appear in the business case.
May 1, 2024Traditional CI/CD pipelines assume deterministic tests. LLMs are probabilistic. Here's how to build a deployment pipeline that gives you confidence without waiting for 100% test coverage that will never arrive.
Apr 10, 2024Retrieval Augmented Generation works beautifully in notebooks. Production RAG at scale is an entirely different engineering problem. This is the architecture we use across our enterprise deployments.
Mar 25, 2024AI hiring tools promise faster, more consistent screening. The evidence on bias is more complicated. Here is what the research shows, and what responsible deployment actually requires.
Mar 5, 2024Agentic AI systems access far more data than traditional applications — and retain it in ways that are difficult to audit. Here's how to build agents that are powerful without being a privacy liability.
Feb 18, 2024Most AI governance frameworks live in policy documents. The organisations that actually control their AI systems have turned those policies into code — automated checks that run continuously in production.
Jan 30, 2024Full fine-tuning of large language models is prohibitively expensive for most organisations. LoRA (Low-Rank Adaptation) makes domain-specific customisation practical at a fraction of the cost — here's the complete technical guide.
Jan 15, 2024Serverless GPU inference has matured significantly. It is now a viable option for many production workloads — but the trade-offs are real. Here is an honest assessment.
Dec 20, 2023Training GPT-4 consumed an estimated 50 GWh of electricity. As AI scales, the environmental cost is no longer ignorable. Here's how organisations can make more responsible infrastructure choices.
Dec 1, 2023Running AI inference at the network edge — on devices, in factories, at the point of data generation — eliminates latency, reduces bandwidth costs, and enables use cases that cloud-first architectures simply cannot support.
Nov 15, 2023The supply of qualified AI engineers is not keeping pace with demand. Organisations that figure out how to attract, retain, and develop AI talent will have a decisive advantage over those fighting the same battles for the same candidates.
Nov 1, 2023Financial services regulators are increasingly requiring that AI decisions affecting customers can be explained. Here's what explainability means in practice — and how to build it without sacrificing model performance.
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