We build monitoring and observability systems that give you full visibility into your AI applications, from LLM call traces to agent decision paths to model performance metrics.
Traditional application monitoring tracks uptime, latency, and error rates. AI systems require additional dimensions: output quality, model behavior, cost efficiency, and decision tracing. An LLM application can return 200 OK responses with zero errors while producing outputs that are factually wrong, off-brand, or unhelpful. Without quality-focused monitoring, these issues go undetected until users complain.
AI observability extends beyond metrics to tracing. When an AI agent produces an unexpected result, you need to trace the chain of decisions, tool calls, and model responses that led to that output. When a RAG system returns an inaccurate answer, you need to see which documents were retrieved, how they were ranked, and how the model synthesized the response. This level of visibility is essential for debugging, optimization, and building trust.
Arthiq builds AI monitoring as a core component of every system we deliver, and we also help organizations instrument existing AI applications that lack adequate observability. Our monitoring solutions cover LLM applications, agent systems, RAG pipelines, and traditional ML models.
LLM applications have unique monitoring requirements that Arthiq addresses comprehensively. We track every model call with input prompts, output responses, token counts, latency, model version, and cost. This data enables analysis of model behavior over time, identification of expensive or slow queries, and detection of quality degradation.
Quality monitoring goes beyond logging. We implement automated evaluation that scores a sample of model outputs against quality criteria using LLM-as-judge techniques or custom evaluation functions. These scores are tracked over time, and automated alerts trigger when quality drops below acceptable thresholds. This catches issues like prompt drift, model version changes, or data quality problems before they affect many users.
Cost monitoring provides real-time visibility into API spending by model, feature, user, and time period. Budget alerts prevent cost overruns. Usage dashboards identify optimization opportunities, such as queries that could be served by a cheaper model or cached from a previous identical request.
Agent systems and multi-step AI pipelines require execution tracing that shows the full sequence of operations. Arthiq implements distributed tracing for AI systems using LangSmith, custom tracing infrastructure, or open-source solutions. Every agent decision, tool call, retrieval operation, and model invocation is captured in a traceable execution graph.
Traces are searchable and filterable, enabling quick debugging of issues. When a user reports an incorrect response, support and engineering teams can look up the specific trace, see every step the system took, and identify exactly where things went wrong. This dramatically reduces debugging time compared to log-based investigation.
For RAG systems, our tracing shows the query, retrieved documents with relevance scores, the context passed to the model, and the generated response. This end-to-end visibility makes it straightforward to diagnose retrieval quality issues, context assembly problems, and generation errors.
Arthiq builds actionable monitoring dashboards that surface the metrics your team needs to operate AI systems confidently. Dashboards are organized by system component: model performance, retrieval quality, agent success rates, cost trends, and user satisfaction metrics. Real-time updates show current system status while historical views reveal trends and patterns.
Alerting is configured for the metrics that matter most to your operations. Quality score drops, cost spikes, latency increases, error rate changes, and specific failure patterns all trigger notifications through your preferred channels: email, Slack, PagerDuty, or custom webhooks. Alert thresholds are calibrated to minimize noise while catching genuine issues.
Regular reporting summarizes AI system performance for stakeholders. Weekly and monthly reports cover quality trends, cost analysis, usage patterns, and incident summaries. These reports provide the data your team needs to make informed decisions about AI system investments and improvements.
Operating AI systems without adequate monitoring is operating blind. Arthiq builds the observability infrastructure that gives you confidence in your AI systems and the data to continuously improve them.
We implement monitoring during system development so it is ready from day one, not bolted on after problems arise. For existing AI systems that lack adequate monitoring, we conduct observability audits and implement instrumentation without disrupting production operations.
Contact us at founders@arthiq.co to discuss monitoring for your AI systems. Whether you are building new AI applications or improving visibility into existing ones, we have the expertise to help.
Our team will build monitoring and observability infrastructure that gives you full visibility into your AI application quality, performance, and costs.