Agentic System & Rapid Content Updates
Selective training: We provide curated knowledge from our internal repository of best practices, enabling quick updates and ensuring domain-specific responses remain fresh.
Retrieval-Augmented Generation (RAG)
Context-Enriched Queries: Each user prompt is supplemented with relevant documentation or case studies. This approach boosts coherence and relevance by grounding responses in real-world data.
Role-Based LLM Division
Multiple AI “Roles”: One LLM proposes a recommendation, another checks alignment with security, compliance, and the user’s stated goals - preventing off-track or generic outputs.
Rigid Templates for Consistency
Framework-Driven Output: By mapping suggestions to known structures (e.g., DORA or SAFe), we ensure consistent and recognizable guidance that leadership teams can immediately apply.
Configurable Approach
Instead of a one-size-fits-all model, we designed a flexible configuration layer that lets us adapt system prompts, context sources, and conversation flows. This ensures rapid iteration without the overhead of coding entire pipelines from scratch.
