Folaranmi L.

Most AI systems fail quietly. Not with an error message. With a hallucination nobody caught, a retrieval layer that returns the wrong context, or an LLM wired to live data with no guardrails on what it says when that data runs out. The system demos beautifully. Three months later it's eroding trust with every wrong answer. I build AI systems that don't do that. I'm Fola. I'm an AI Systems Engineer, and the difference between what I do and what most "AI automation" freelancers do is this: I evaluate what I build. I design retrieval pipelines with real metrics, test for failure modes before deployment, and treat production reliability as a first-class requirement, not an afterthought. My MSc dissertation is a quantitative benchmark of four RAG configurations on the BEIR Natural Questions corpus, measured across Recall@5, MRR@5, NDCG@5, and RAGAS Faithfulness. I build the same rigour into client work. If a system is supposed to retrieve accurate information and stop when it doesn't know something, I can prove it does, and I design it to fail gracefully when it doesn't. That matters because the clients I work with are done experimenting. They've seen the demos. They want something that works in production, handles edge cases, and doesn't require babysitting. WHAT I BUILD Production RAG Systems: Retrieval pipelines grounded in your actual data, not LLM hallucinations. Hybrid retrieval (dense + sparse + reranking), vector databases (pgvector, AstraDB, ChromaDB, FAISS), evaluation frameworks, and hallucination guardrails built in from the start. You get a system that knows what it knows and defers when it doesn't. LLM Integration & Agent Systems: Multi-agent architectures with tool use, function calling, structured outputs, and memory management. OpenAI, Claude, Gemini. Connected to your actual systems: CRMs, databases, communication platforms, internal knowledge bases. Not bolted on as a chatbot, wired in as infrastructure. AI-Powered Internal Tools: Full-stack applications where AI does the heavy lifting. Knowledge bases your whole team can query in natural language. Operational dashboards that generate insights automatically. Document processing pipelines that extract structure from unstructured inputs. Systems your employees use daily because they actually work. Automation Architecture: When the right solution is workflow automation, I build it properly. n8n, Make..com, Zapier, Power Automate. Error handling, retry logic, failure alerting, and documentation that means someone else can maintain it. But automation is one tool, not the whole job. Full-Stack Delivery: I own features end-to-end. Python (FastAPI), Node.js, TypeScript, Next.js, React, PostgreSQL, Supabase. I can build the backend, the frontend, the database, and the API layer. This is what makes AI systems maintainable in production rather than dependent on the original developer forever. WHAT MAKES THIS DIFFERENT Most AI freelancers connect tools. I design systems. The LBS WhatsApp assistant I built for Lagos Business School handles three MBA programmes, draws from two separate knowledge bases with programme-aware retrieval, detects when a query falls outside its verified scope, and routes to the correct human contact rather than guessing. Staff query-handling workload dropped by 80% in production. That result came from architecture decisions, not just prompt tweaking. I also run evaluation on what I build. If a retrieval system is supposed to surface accurate context, I measure whether it does, before a client's users discover it doesn't. 100% Job Success. Top Rated. Maintained by being honest about what a system needs before I quote it, and building it to work six months from now, not just on delivery day. If you need an AI system that actually does what it's supposed to do in production, let's talk.