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App Corp
Full-service software engineering
Engineering your experience…
App Corp
Full-service software engineering
Engineering your experience…
Engineering Manager, AI/ML
Sofia leads App Corp's AI practice, applying production-grade machine learning to real business problems. She holds an MSc in Computer Science from Carnegie Mellon and has published three peer-reviewed papers on applied NLP.
Gartner estimates 87% of AI projects never reach production. We've identified the five failure modes that doom enterprise AI — and how to beat them.
Traditional software is deterministic; AI systems are probabilistic. This single difference changes everything about how you architect, test, and operate them.
Building an AI application that actually works in production requires a systematic approach. Here's the playbook we use for every engagement.
Most AI POCs are built to demo, not to deploy. Here's how to design POCs that can actually become production systems.
An AI agent is an LLM-based system that can plan, reason, use tools, and take actions autonomously. Here's what that actually means architecturally.
A chatbot answers questions. An agent takes actions. The architectural difference is tool access, planning, and the ability to close the loop.
Multi-agent systems decompose complex workflows into specialised agents coordinated by an orchestrator. Here's when and how to build them.
Evaluating AI agents requires more than accuracy metrics. Here's the comprehensive evaluation framework for production agents.
The best AI agents don't eliminate humans — they know when to bring humans in. Here's the architecture for effective human-agent collaboration.
Agents fail differently than traditional software. Here are the seven failure modes unique to AI agents and the defences that work.
RAG and fine-tuning solve different problems. Here's the decision framework that saves months of wasted effort and thousands in unnecessary cost.
Evaluating RAG requires measuring both retrieval quality and generation quality. Here's the complete evaluation framework.
Educational RAG requires curriculum-aware retrieval and student personalisation. Here's the architecture for education AI products.
Model routing is the highest-leverage cost optimisation for AI SaaS. Here's the architecture for intelligent request routing.
Bigger models are not always better. Here's the task-specific framework for choosing between small and large models.
Model routing is the practice of directing each AI request to the optimal model. Here's the architecture and implementation guide.
Healthcare AI agents handle patient intake, clinical documentation, appointment scheduling, and insurance verification — reducing administrative burden by 60-80%.
Healthcare RAG retrieves clinical guidelines, drug interactions, and treatment protocols with source attribution — grounded in evidence, not hallucinated.
Medical knowledge assistants help clinicians access the right information at the point of care — grounded in evidence, not hallucinated.
AI school agents handle attendance tracking, admissions processing, fee collection, and parent communication — reducing teacher admin by 60-80%.
AI tutor architecture combines RAG for curriculum content, adaptive difficulty, and learning pattern analysis to personalise education at scale.
We built 6 AI agents for a school operating system, each handling a specific workflow. Here's why specialization beat a single general-purpose agent and how we engineered each one.
Manual attendance tracking wastes teacher time and produces unreliable data. Here's how we built an AI-powered system that automates attendance with 99.2% accuracy.