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What Metrics Should You Track to Measure LLM Quality?

Services5 hours ago1View 0Likes 0Comments

Most teams building on LLMs know something is wrong before they can say exactly what. The chatbot sounds confident and wrong. The RAG pipeline returns plausible answers that contradict the source documents. The agent completes tasks in testing but drifts in production. The real problem is that most teams treat LLM quality as a single score,…

The LLM Monitoring Checklist: 10 Things to Verify Before You Ship

Services3 days ago4Views 0Likes 0Comments

A financial services company shipped an AI-powered customer advisor to production on a Friday afternoon. By Monday, the support queue had 200 complaints. The model had been confidently answering questions about interest rates using figures from 18 months ago. Nobody had checked whether the retrieval layer was pulling current documents. Nobody had set up an alert for…

How to Spot Data Quality Issues in Your AI Pipeline Early

ServicesMay 25, 20264Views 0Likes 0Comments

A financial services company spent eight months building an AI assistant to help relationship managers prepare for client calls. The system pulled from CRM notes, transaction histories, and market data. In testing, it performed well, however, in production, something subtler happened. The CRM team had quietly changed how they logged meeting outcomes, a field that had…

Fixing the Data Foundation: How to Solve the #1 Bottleneck in AI DataOps

ServicesMay 21, 20266Views 0Likes 0Comments

The demo went perfectly, the AI agent pulled from the CRM, summarised customer history, flagged a churn risk, and recommended the right offer, all in under four seconds. The executives in the room were impressed. Three months later, the same system was in production and quietly making wrong recommendations. Not because the model degraded. Because a…

How to Build a Safe Agentic Development Workflow for Your Team

ServicesMay 7, 202616Views 0Likes 0Comments

Every team that deploys an autonomous AI agent eventually has the same conversation, usually triggered by something going wrong. An agent that was trusted with a routine task found a creative interpretation of its instructions. It did not crash, it did not throw an error. It just did something nobody expected, and by the time anyone…

The Biggest Risks of Agentic Development (and How to Reduce Them)

ServicesMay 4, 202620Views 0Likes 0Comments

There is a moment in almost every enterprise AI project when someone in the room says, 'Let's just give it access and see what happens.' That moment is exactly where most agentic development risks begin. Autonomous AI agents are being deployed to handle almost everything from customer onboarding to supply chain decisions, and the pace of…

How Agentic Development Accelerates the Software Development Lifecycle

ServicesApril 30, 202623Views 0Likes 0Comments

There is a familiar frustration that lives in almost every software organisation. A product idea arrives with genuine momentum, stakeholders are aligned, the roadmap looks clean, and then reality kicks in. Requirements expand mid-sprint as QA becomes a six-week exercise in archaeology. Deployment day carries the quiet dread of something unexpected going wrong at exactly the…

Implementing Agentic Development: A 5-Step Blueprint for Enterprise Teams

ServicesApril 27, 202622Views 0Likes 0Comments

Something significant has been happening inside the engineering floors of Africa's fastest-growing enterprises. The teams that were debating whether to adopt AI last a couple of years back are now debating something more specific: how do we move from a chatbot that answers questions to an AI system that actually gets things done? That move, from prompt-and-response…

Stop Asking ‘How Fast?’ Start Asking ‘How To Deliver Value That Persists’

ServicesJanuary 19, 202675Views 0Likes 0Comments

The executive asks the question that seems perfectly reasonable: "How fast can we deploy this AI system?" The team reviews the scope, considers the complexity, and provides an honest estimate: "Six months for proper implementation." The executive leans forward with the response everyone has learned to dread: "Make it three months." Three months later, the system…

The Domain Expertise Your Generic Model Will Never Learn

ServicesNovember 13, 202574Views 0Likes 0Comments

You've probably noticed something strange about your AI assistant. It answers basic questions well, but keeps missing the mark when things get specific to your business. You ask about "drawdown," and it talks about economic decline when you meant loan disbursement. You mention "code status," and it gives software development advice when you're in healthcare. This isn't your…

Why Your Competitors’ AI Outperforms Yours

ServicesNovember 10, 202578Views 0Likes 0Comments

Business leaders across Africa are noticing a troubling pattern: competitors' AI solutions consistently deliver better customer experiences than their own implementations. These business leaders feel but rarely admit: "Our AI assistant feels clunky compared to what our competitors offer." This isn't just about technology; it's about market position. When customers experience your AI, they're not comparing…

When Your Workflow Is Too Complex for Generic AI

ServicesNovember 6, 202581Views 0Likes 0Comments

You bought into the promise of AI. You tried a popular, off-the-shelf model to automate a key business process. The results were… underwhelming. The AI didn’t understand your internal rules, couldn’t connect to your legacy systems, and made decisions that would have failed an audit. This isn't a failure of AI technology. It’s a sign that your…