Why 30% of Gen AIProjects Get Abandoned
Your team shipped a brilliant Gen AI prototype.Then production happened.
The Hallucination Incident
Model confidently produces false information
Customer trust destroyed • Legal liability incurred
The Cost Explosion
Inference costs balloon from $50K to $500K/month
CFO demands explanation • Budget allocation frozen
The Compliance Audit
Regulator asks "show me your AI decision logs"
No audit trail exists • Fines + reputation damage
The Performance Decay
Model quality silently degrades over weeks
Users complain • No diagnostic data available
The Invisible Problem
You wouldn't run a bank without transaction logs.You wouldn't run a hospital without patient records.Why would you run Gen AI without observability?
LLMs need something fundamentally different
Seven Layers ofObservability Sovereignty
This is engineering, not just monitoring. A complete platform for production-grade LLMOps.
Select a layer to explore
Click any layer on the left to see detailed features and technical implementation
Private AI vs.Cloud-First Monitoring
Clear, honest comparison. No marketing spin. See where RisiCare wins and why it matters.
| Feature | RisiCare (This Platform) | Arize AI | Fiddler AI | Datadog LLM Obs | Langfuse (OSS) |
|---|---|---|---|---|---|
Deployment | |||||
Data Sovereignty 100% on-premise / air-gap capable | 100% (zero external calls) | Cloud only | Cloud only | Cloud only | ✓ (if self-hosted) |
Deployment Options On-prem, air-gap, private cloud, SaaS | On-prem/Air-gap/Cloud | Cloud only | Cloud only | Cloud only | Self-host (DIY) |
Quality & Detection | |||||
Hallucination Detection Multi-method academic research implementation | Multi-method (Nature 2024 + HSAD) | Semantic entropy | Basic LLM-as-judge | Generic quality metrics | DIY implementation |
Security Guardrails Prompt injection, PII, toxicity detection | Multi-layer (char + AML + semantic) | Basic filters | Prompt shield integration | Sensitive data scanner | DIY |
Compliance | |||||
EU AI Act Ready Article 12/19 automatic logging | EU AI Act + NIST + ISO 42001 | Generic logging | Strong governance | APM logs extended | Manual setup |
Cost & Performance | |||||
Cost Optimization Predictive cost modeling & anomaly detection | Predictive + anomaly detection | Dashboards | Cost tracking | Basic usage reports | Manual analysis |
Multi-Model Routing Observability-driven adaptive routing | Observability-driven adaptive | Static rules | Not included | Not included | Not included |
Implementation | |||||
Setup Time Time to production-ready observability | <1 week (managed service) | 2-4 weeks | 3-6 weeks | Depends on existing DD | Weeks-months (DIY) |
Deployment
Data Sovereignty
100% on-premise / air-gap capable
Deployment Options
On-prem, air-gap, private cloud, SaaS
Quality & Detection
Hallucination Detection
Multi-method academic research implementation
Security Guardrails
Prompt injection, PII, toxicity detection
Compliance
EU AI Act Ready
Article 12/19 automatic logging
Cost & Performance
Cost Optimization
Predictive cost modeling & anomaly detection
Multi-Model Routing
Observability-driven adaptive routing
Implementation
Setup Time
Time to production-ready observability
They extended APM for LLMs. We built for LLMs from the ground up.
They require cloud access. We guarantee sovereignty.
They give you tools. We give you a platform.
They optimize for dev speed. We optimize for production confidence.
Observability ThatWatches, Learns, Improves
This isn't monitoring. It's meta-intelligence—intelligence that observes intelligence and makes it better.
Observation Changes Behavior
In quantum mechanics, observing a particle changes its state. In AI systems, comprehensive observability doesn't just measure performance—it fundamentally improves it. When every token is traced, every decision logged, every output evaluated, the entire system becomes more reliable.
Your LLM knows it's being watched. That changes everything.
Feedback Loops Create Intelligence
Traditional monitoring generates static logs. Agentic observability creates dynamic feedback loops—user signals inform model routing, hallucination patterns trigger prompt refinement, cost anomalies automatically adjust inference strategies. The system doesn't just observe. It learns and adapts.
Observability that makes your AI smarter with every request.
Explainability Builds Trust
Enterprises don't fear AI capability—they fear AI unpredictability. Every "why did the model say that?" question without an answer erodes confidence. Deep observability transforms mysterious outputs into traceable decisions: which context influenced the response, which reasoning path was taken, what confidence scores existed.
Trust isn't built on performance. It's built on understanding.
The Metamorphosis of Observability
From passive logging to active intelligence—the evolution of how we understand AI systems
Passive Logs
StaticTraditional monitoring: events recorded, reports generated, humans analyze
Active Metrics
ReactivePerformance tracking: latency measured, costs calculated, dashboards updated
Intelligent Evaluation
DiagnosticQuality scoring: hallucinations detected, bias measured, safety enforced
Agentic Optimization
AutonomousSelf-improving system: routing adapts, prompts refine, models learn from observation
We're building the autonomous stage. Are you ready?
From Research PapersTo Production Reality
We read Nature 2024, arXiv 2025, and the latest research. Then we shipped it.
The Hallucination Problem
Hallucination is inevitable (formally proven in arXiv:2401.11817). LLMs will generate plausible-sounding falsehoods. The question isn't IF your model will hallucinate—it's HOW FAST you can detect and prevent it.
Our Multi-Method Detection Arsenal
Semantic Entropy
Measures uncertainty about meanings, not just text variations
How It Works
LLM generates multiple possible answers to the same question
System clusters semantically similar answers (different words, same meaning)
Calculates entropy across meaning clusters, not token probabilities
High semantic entropy = model is uncertain about the correct answer
Strengths
- •Detects factual uncertainty
- •Works across paraphrases
- •No external data needed
Limitations
- •Requires multiple forward passes
- •Computationally expensive for real-time
Real-World Detection: A Case Study
“What was our company's Q4 2024 revenue?”
$127M, up 23% YoY from $103M in Q4 2023
Model generated 5 different revenue figures across samples: $127M, $125M, $130M, "data not available", $122M. High semantic entropy (2.1) indicates uncertainty.
Frequency analysis of hidden layers shows activation pattern consistent with "creative generation" rather than "factual recall".
Faithfulness score: 0.33. The specific figure "$127M" cannot be inferred from retrieved context. Context states figures are "pending final audit".
GPT-4: "$127M", Claude: "Revenue data not yet available", Llama: "$125M estimated". Consensus failed.
Response blocked. Fallback message: "Q4 2024 revenue data is pending final audit. Please check back after [date]."
Prevented potential SEC violation (material misstatement in financial data)
Built for HowEnterprises Actually Buy
Lower friction to engagement. Meet buyers where they are.
Technical Deep Dive
For Engineering Leaders
Business Case Workshop
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Hands-On Sandbox
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The $749B AI Infrastructure Opportunity Demands Real Observability
By 2028, $749 billion will flow through AI infrastructure. The winners won't be those with the fanciest models — they'll be those who can see, understand, and trust their AI.
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