Insights on AI, self-improvement, and knowledge extraction
Learn how to create an AI agent that evaluates its own outputs and refines them autonomously. We'll walk through a real estate valuation example where the agent improves accuracy over iterations.
Read Article →Why extracting structured knowledge is more powerful than relying on LLM context windows. We compare traditional prompt engineering with our graph-based approach and show 40% accuracy improvements.
Read Article →At a leading wealth management firm, AILoop automatically generates comprehensive financial plans. Each client gets a unique plan refined through 8-10 iterations, with 95% advisor approval rate on first pass.
Read Article →Deep dive into our confidence scoring algorithm. Learn how we calculate per-fact confidence based on evidence aggregation, cross-iteration consistency, and semantic similarity to ground truth.
Read Article →We benchmarked AILoop against standard single-pass agents across 5 domains: legal analysis, medical research, financial recommendations, technical documentation, and business strategy.
Read Article →We're excited to launch AILoop publicly with a generous free tier. Anyone can run 5 queries per month for free with full access to knowledge graphs, confidence scoring, and provenance tracking.
Read Article →Start building self-improving agents today.