At Hyperbots, our AI stack is now powered by AI. Deep AI research at a break-neck pace is critical. We now have the new age AI-stack. This not only gave us a 10-15x throttle, but much more. Our ML research stack is powered by an autonomous multi-agent system that runs the entire LLM/VLM lifecycle end-to-end: → Literature review (deep domain research) → Dataset analysis & quality checks (data, data, data) → Environment infra setup (ai - ml ops) → Distributed training & monitoring (ml infra) → Evaluation & benchmarking (evals) → Failure analysis & debugging (more evals) → Reporting & experiment memory (analytics..) The architecture at a high level: one orchestrator, seven specialized research-engineering subagents. Each of them triggering continuous experiment loops. Minimal but critical human intervention. The result: ⚡ Faster iteration cycles 📈 Better benchmark performance 🧠 Persistent experiment memory across runs 🔁 Self-improving research workflows Try this out right here: apis.hyperbots.com This is what happens when AI agents move beyond copilots and become full-stack research engineers. The future of model development is autonomous and we are definitely seeing those gains in our Finance AI journey. #AIAgents #MachineLearning #LLM #VLM #MLOps #Hyperbots #HyperAPI #anthropic #AWS #FinanceAI #FinanceDocAI #EnterpriseDocAI