Enterprise-grade observability for LLMs and predictive models. Ensure accuracy, control token costs, and maintain operational safety.
Traditional Monitoring
Primary Focus
Server Uptime & Latency
Failure Mode
Crashes & 404 Errors
Budgeting
Static Infrastructure Costs
Safety
Encryption & Access Control
AI Observability (IWConnect)
Primary Focus
Model Truthfulness & Logic Accuracy
Failure Mode
Silent “Model Drift” & Hallucinations
Budgeting
Token-Level ROI & Usage Tracking
Safety
GenAI Guardrails & Prompt Defense
Slow response times that frustrate users. Latency creeps up, but nobody notices until complaints arrive.
Accuracy dropping as real-world data shifts. Your AI was great at launch, now it’s quietly getting worse.
Token usage and compute costs spiraling without visibility. Budget surprises at the end of every month.
Early warning when model behavior changes. Catch accuracy drops before they impact users.
Token usage, compute costs, ROI dashboards. Know exactly what each AI capability costs you.
Latency, throughput, and error rate monitoring. Get notified before users complain.
Continuous output evaluation in production. Track accuracy, relevance, and user satisfaction.
1
Establish performance and quality benchmarks
2
Add monitoring hooks to all AI systems
3
Build operational visibility and alert thresholds
4
Define response procedures for every incident type
5
Trust becomes measurable
6
From reliable data to safe AI
Challenge A US-based fintech enterprise running critical B2B payment platforms faced rapidly escalating Datadog costs and declining monitoring effectiveness. Multiple engineering teams had independently adopted observability tools
Challenge One of Europe’s largest retailers faced a growing operational bottleneck: fragmented exception handling that drained engineering resources and slowed incident response. Monitoring tools were
Challenge Modern digital systems generate huge amounts of operational data, but most companies still react to problems after they happen. This organization wanted to move from firefighting incidents
Challenge A prominent Scandinavian retail company, heavily reliant on SnapLogic for its data processing, faced a critical challenge: their existing monitoring system, limited to basic
Reduction in Mean Time to Detect (MTTD)
Issue identification (down from 4 hours)
Observability assessment. We’ll identify monitoring gaps and quick wins.
By signing up for the waiting list now, you'll secure your spot for early access and claim these valuable benefits.
