ObserveAIModels

Current solutions may help to improve models issue but stakeholders remain blindsided. I bring ObserveAI to turns AI observability into an incident-style reliability workflow for silent model failures to improve healing before impacting users for C-suite. I am building ObserveAI to solve a problem that becomes critical when SLMs and LLMs move from demos into real products: AI failures often stay hidden until users notice them. By then, trust is already damaged, feedback cycles become slower, and the cost of fixing issues rises. My goal is to make every AI call traceable, scored, diagnosed, and improved before users become the monitoring system. ObserveAI brings this into one single-pane experience after login. It can ingest signals through REST, Syslog, OpenTelemetry, CSV, and future Python or JavaScript SDK wrappers. Once connected, each request becomes observable, including the input, output, system prompt intent, model, provider, latency, retries, token pressure, context usage, metadata, cost, and feedback. The platform evaluates each call for the risks that matter in production: hallucination, prompt drift, prompt injection, PII leakage, toxicity, off-topic behavior, factual inconsistency, schema failures, retry health, eval regression, and context-window pressure. These signals roll into a live Reliability Score from 0 to 100, so teams can measure AI behavior instead of guessing. The dashboard acts as a command center for local AI, cloud AI, SLMs, and LLMs. Teams can monitor reliability, cost spikes, guardrail failures, and trace-level root cause. Prompt and response diffs show what went wrong, fix suggestions show what to change, and AI Search helps operators ask what is failing and why. I built this demo with only a 50 AI unit to show that trustworthy AI does not start with massive infrastructure. It starts with the right visibility loop: detect, diagnose, fix, and measure.