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Bridging AI tech with domain expertise to scale GenAI beyond prototypes
Solving the Paradox of GenAI:
Test rigorously, design intuitive UIs, and sustain continuous improvement for high-quality AI outputs.
Enhance your AI readiness with hybrid indexing, enriched metadata, and expert validation loops.
Lay the groundwork: structure your content and align it with real user behavior
A deep dive into RAG systems and how they unlock business value through data-grounded GenAI.
Enhanced Celery Azure backend, enabling secure Managed Identity authentication and simplified setup.
How We Improved Celery’s Azure Integration:
Building a foundation where your AI learns from your data, adapts to your needs, and performs consistently in the real world.
Doing RAG Right:
Process Modelling can replace manual trials, enabling precise, data-driven input optimisation.
Automated forecasting solution streamlines energy balancing across multiple building types.
Episode 3: Completes the RAG validation framework with human and LLM-based evaluation.
RAG Output Validation - Part 3:
Episode 2: Four methods for building RAG ground truth datasets, from manual to synthetic.
RAG Output Validation - Part 2:
Episode 1: Lays the foundation for scalable, reliable RAG output validation.
RAG Output Validation - Part 1:
