Build real-time dashboards and monitoring systems that track AI performance, measure business impact, and give you visibility into what's working—and what's not.
Organizations that have deployed AI systems but lack visibility into performance, or operations teams that need real-time monitoring of production processes, quality metrics, and system health. Essential for manufacturing, retail, finance, and any organization running AI in production.
50% fewer defects
Early detection and real-time alerts prevent quality issues from reaching customers
100% transparency
Real-time dashboards show AI accuracy, drift, and business impact metrics
80% faster
Automated anomaly detection alerts teams to problems in minutes, not hours
Quantified impact
Track cost savings, throughput improvements, and revenue impact from AI initiatives
Custom dashboards tracking KPIs like OEE, defect rates, throughput, AI accuracy, CSAT, and business metrics with drill-down capabilities
Automated detection of performance degradation, quality issues, and AI model drift with configurable alerts (email, Slack, PagerDuty)
Connectors to your systems (ERP, MES, CRM, databases, APIs) to collect metrics in real-time and historical data for trend analysis
Monitor model accuracy, latency, cost per inference, and business impact metrics. Track drift and trigger retraining when needed
Quantify cost savings, revenue impact, efficiency gains, and other business outcomes from AI and process improvements
We identify the critical metrics to track (OEE, defect rates, AI accuracy, throughput, etc.), define success thresholds, and map data sources. We interview stakeholders to understand monitoring priorities and alert requirements.
We build data pipelines to collect metrics from your systems, configure dashboards with real-time visualizations, set up anomaly detection algorithms, and implement alerting workflows. We test with historical data to validate accuracy.
We deploy the monitoring platform, train users on dashboards and alerts, establish governance for metric definitions, and set up ongoing maintenance. We refine thresholds and alerts based on real-world performance.
6-10 weeks
From requirements through deployment and initial training
3-5 hours/week
Stakeholder interviews, metric definition, data access setup, dashboard review, and user acceptance testing
Timeline factors:
$25,000 - $75,000 + maintenance
Project-based pricing for platform build-out, with optional monthly maintenance for ongoing support, updates, and optimization. Higher-end pricing for complex manufacturing OEE systems or vision-based QC platforms.
Our monitoring platforms track and improve operational, quality, and AI performance metrics across your organization.
Overall Equipment Effectiveness (OEE %)
Defect rate (PPM or %)
First-pass yield (%)
Throughput rate (units/hour)
Cycle time (hours/units)
AI model accuracy (%)
Mean time to detect (MTTD)
Mean time to resolve (MTTR)
Customer satisfaction (CSAT)
First-contact resolution rate (%)
Forecast accuracy (%)
Schedule attainment (%)
On-time delivery rate (%)
System uptime (%)
Cost per unit ($)
We use modern monitoring and visualization tools that integrate with your existing systems. Our approach is flexible—we select the best-fit platform for your environment and requirements.
Risk: Monitoring dashboards show incorrect metrics due to bad data, missing data, or calculation errors, leading to poor decisions
Safeguard: We implement data validation checks, handle missing data gracefully, and validate calculations against known baselines. We provide data quality scores and alerts when data sources are unavailable or producing anomalies. We also document all metric definitions and calculation formulas for transparency.
Risk: Too many alerts or false positives cause teams to ignore notifications, missing real issues
Safeguard: We use intelligent thresholding, anomaly detection algorithms that reduce false positives, and configurable alert rules. We implement alert escalation paths and allow users to tune sensitivity. We also provide alert analytics to identify and eliminate noisy alerts.
Risk: Monitoring platform becomes slow or unreliable under high data volumes, impacting trust and usability
Safeguard: We design for scalability from day one, using efficient data storage (time-series databases), optimized queries, and caching strategies. We load test with expected data volumes and provide performance SLAs. We also offer managed hosting options for reliability.
Challenge: Production managers had no real-time visibility into OEE, constraint utilization, or quality metrics. Defects were discovered late in the process, causing rework and delays. Schedule attainment was only 65% due to poor visibility into bottlenecks.
Solution: Built real-time production dashboards tracking OEE by machine, constraint utilization, throughput, and defect rates. Integrated with MES and quality systems. Implemented anomaly detection that alerted when defect rates spiked or machines underperformed.
Impact: Improved schedule attainment from 65% to 87% through better bottleneck visibility. Reduced defects by 45% via early detection and alerts. Increased OEE by 12% through proactive maintenance triggered by performance trends. ROI: $1.8M annual value.
Challenge: Deployed AI chatbots and recommendation engines but couldn't measure ROI or detect when models degraded. Customer support metrics (CSAT, resolution time) were tracked manually in spreadsheets, providing no real-time visibility.
Solution: Built monitoring platform tracking AI model accuracy, latency, cost per inference, and business metrics (conversion rate, revenue lift). Created real-time dashboards for CSAT, first-contact resolution, and ticket volume. Set up alerts for model drift and performance degradation.
Impact: Detected model degradation 3 weeks earlier than before, preventing revenue loss. Improved CSAT by 15% through real-time visibility into support performance. Quantified $2.4M annual ROI from AI initiatives. Reduced time spent on manual reporting by 80%.
Traditional BI tools are great for historical analysis and reporting, but they're not designed for real-time monitoring, anomaly detection, or alerting. Our platforms provide real-time dashboards (sub-second updates), automated anomaly detection, and proactive alerting when metrics cross thresholds. We also specialize in operational metrics (OEE, throughput, quality) and AI performance tracking that many BI tools don't handle well.
That's exactly what we solve. We build data pipelines that pull metrics from all your systems—ERP, MES, SCADA, databases, APIs, and more. We normalize the data, calculate derived metrics (like OEE), and present it in unified dashboards. You get a single source of truth for operational visibility, even when data lives in multiple places.
We support both. For real-time monitoring (production lines, quality systems), we use streaming data pipelines (Kafka, Kinesis) that update dashboards in seconds. For less time-sensitive metrics (financial KPIs, monthly reports), we use batch processing. We'll design the architecture based on your requirements—some metrics need real-time, others are fine with hourly or daily updates.
Absolutely. We monitor AI model accuracy, latency, cost per inference, and business impact metrics (conversion rates, revenue lift). We implement drift detection algorithms that alert when model performance degrades or data distributions shift. This allows you to retrain models proactively before they impact business outcomes. We can also track A/B test results and model version performance.
We use intelligent thresholding and anomaly detection algorithms that reduce false positives. We implement alert escalation paths (e.g., only alert managers for critical issues, not every minor fluctuation). We also provide alert analytics so you can identify and tune noisy alerts. During setup, we work with you to define alert rules that match your operational priorities—not every metric needs an alert.
We track both operational metrics (defect reduction, throughput improvement, cost savings) and business outcomes (revenue impact, customer satisfaction). We help you establish baselines before AI deployment, then measure improvements. For example, we can quantify cost savings from reduced defects, revenue lift from improved recommendations, or efficiency gains from automated processes. We provide ROI dashboards that show cumulative impact over time.
Yes, we offer maintenance packages ($3K-$8K/month) for ongoing support, metric updates, dashboard enhancements, and system optimization. Many clients start with project-only, then add maintenance once they see the value. We can also provide managed hosting for the monitoring platform if you prefer not to run it internally.
Let's discuss your monitoring needs and explore how real-time dashboards can improve decision-making and reduce defects by 50%.
For ongoing AI evaluation and testing protocols. We create evaluation harnesses and benchmark testing for your AI systems.
Build the AI workflows and processes that our monitoring platform will track. Perfect pairing for end-to-end AI implementation.
Last updated: November 2025