Overview
Stay ahead of the drift
From first deployment to advanced RAG setups, QKTech enables AI Engineers, Data Scientists, and Engineering Managers to build reliable, production-ready GenAI systems.
With our Insight Engine, Nimbus AI, teams debug faster, explain outputs with context-rich visibility, manage drift by spotting shifts early, and cut costs through optimized monitoring.
Seamless OpenTelemetry integration makes every layer of your LLM stack observable, aligning AI agent response tracking with data quality signals to ensure reliability, performance, and trust in production
Thought Leadership
See Results in What Matters
Focus areas
How we drive AI reliability from prompt to production
Model Performance Monitoring
Our observability platform continuously tracks accuracy, latency, precision, and other key metrics to stay aligned with evolving business goals.
With real-time feedback loops and Qualitykiosk’s evaluation templates, teams benchmark performance against baselines and industry standards , tune, and optimize models for faster, more reliable AI-driven decisions.
Monitor Data Drift and Quality
Early warnings alert when live data diverges from training data, ensuring issues are caught before they impact outcomes.
Automated alerts with intuitive visual diagnostics help isolate root causes, reduce disruption by addressing drift early, and maintain prediction accuracy and model trust in production environments.
System Health Monitoring
Prevent silent failures with full visibility into GenAI systems’ infrastructure performance. By monitoring compute, memory, storage, and network utilization alongside model traces, our solution uncovers latency bottlenecks, detect scaling inefficiencies before they disrupt workloads, and correlate metrics with model behavior for faster root cause analysis.
Model Explainability (SHAP, LIME, Interpretable AI)
We use domain-aware explainability methods to reveal how specific inputs influence decisions, especially in retrieval-augmented and multi-turn dialogue systems.
This transparency supports regulatory compliance with auditable decision paths, builds stakeholder confidence through interpretable outcomes, and accelerates AI adoption in sensitive, high-stakes domains such as finance, healthcare, and public services.
Integrated Observability in ML Pipelines
A built-for-GenAI observability dashboard helps teams deliver higher-quality releases with shorter resolution cycles, tighter feedback loops.
Correlating infrastructure, model, and user-level signals enables proactive detection of bottlenecks, or hallucinations to ensure reliable AI deployments.
Features
Start small or scale with the full suite
AI agent-level observability with response tracing
Integrated model and retrieval evaluation
Experiment tracking and versioning
Custom dashboard and alerting setup
OpenTelemetry & OpenInference integration
Complaince & audit trail reporting
Customer Benefits
Tracking observability's value
Enhanced Gen AI application reliability
Transparent model decision explanations
Seamless Integration Across the AI Stack
Early anomaly and bias detection
Aligned models with business KPIs
Faster incident resolution (MTTR reduction)
Improved compliance & audit readiness
Continuous feedback loop for improvement
Optimized cost-to-performance ratio
SUCCESS STORIES
Challenges we’ve solved for our clients
SUCCESS STORIES
Challenges we’ve met
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