What KPIs Should I Track After Implementing AI?Track
IRPR builds custom dashboards that connect directly to your AI models. We ship in 12 weeks with fixed pricing and SOC 2 compliance.
IRPR builds KPI tracking systems that plug directly into AI models like GPT-4, computer vision, and NLP pipelines. We capture model accuracy, precision, recall, user adoption, and business impact in custom dashboards using Grafana, Metabase, or your BI tool.
What ships: a complete analytics stack with real-time dashboards, alerting, and data pipelines. Timeline: 12-14 weeks. Price range: $80K-$250K, fixed after a 2-week Roadmap phase. All systems comply with GDPR, HIPAA, and SOC 2.
Who hires us: CTOs of SaaS companies needing AI feature adoption KPIs, product managers requiring ROI justification, operations directors monitoring automation efficiency, and data science teams that want to track model drift and retraining cycles.
Types of software we ship.
Real-time AI Performance Dashboard
A dashboard that tracks model accuracy, latency, and throughput using Prometheus and Grafana. Updates every 30 seconds.
Model Drift Detection System
Monitors feature and prediction drift with statistical tests. Alerts via Slack or PagerDuty when drift exceeds thresholds.
AI ROI Calculator
Connects to your CRM and financial systems to compute cost savings, revenue uplift, and payback period from AI deployments.
Chatbot Conversation Analytics
Tracks deflection rate, CSAT, and fallback frequency for Dialogflow or Rasa bots. Integrates with Zendesk and Intercom.
AI Adoption Analytics Platform
Measures daily active users, feature usage, and user behavior patterns on AI features. Built with Segment and Amplitude.
Automated KPI Alerting Engine
Sends real-time alerts when AI KPIs breach thresholds. Uses Kafka and AWS Lambda to trigger notifications in Slack or Teams.
A/B Test Reporting for AI Models
Compares model versions side-by-side with statistical significance tests. Generates automated reports in Tableau or Looker.
Data Pipeline Health Monitor
Watches ETL pipelines for data freshness, completeness, and schema changes. Built on Airflow and dbt with Snowflake.
We build for every business type.
SaaS
Fintech
Healthcare
Ecommerce
Manufacturing
AI KPIs that drive decisions
Most teams track the wrong metrics. IRPR builds dashboards tied to business outcomes.
We start by mapping your business goals to specific AI KPIs. A recommendation engine needs to track click-through rate and revenue lift, not just model accuracy. Your fraud detection system should measure false positive costs, not just F1 score.
Every dashboard we build includes real-time data streaming from your AI pipelines. You get alerts when metrics move outside thresholds, and automated weekly reports to stakeholders.
Generic vs. engineered KPI tracking
Off-the-shelf analytics fall short. IRPR builds custom KPI pipelines that connect to your AI stack.
Default analytics tools like Google Analytics cannot track model-specific metrics. They don't measure drift, they can't capture inference latency, and they don't integrate with MLflow or Weights & Biases.
IRPR engineers build data pipelines that stream raw model events into your warehouse. We then create dashboards that show exactly what you need to know, from precision-recall curves to daily ROI.
- Uses Google Analytics for everything
- No model-specific metrics
- No real-time alerts
- No integration with MLflow
- No data privacy controls
- No custom dashboards
- Metrics tied to model accuracy, precision, recall
- Real-time drift detection
- Custom dashboards with Grafana
- MLflow and Weights & Biases integration
- GDPR and HIPAA compliant data pipelines
- Automated alerting via Slack/PagerDuty
From AI launch to measurable impact
Our 4-phase process ensures KPIs are tracked from day one.
We don't bolt on analytics after launch. During the Roadmap phase, we define the KPIs that matter to your business. Then we instrument your AI models to emit those events from the start.
After deployment, you get a live dashboard the same week. We then iterate on the metrics based on real user behavior, ensuring every KPI answers a business question.
What you get with every AI KPI project
A complete analytics stack shipped with your AI module.
Every engagement includes a production-ready analytics environment. Your team gets full ownership of the code, documentation, and data pipelines.
- Custom dashboard in Grafana or Metabase
- Real-time event streaming via Kafka
- Model performance monitoring (accuracy, drift)
- Automated A/B test reports
- User behavior tracking for AI features
- Data warehouse connector (Snowflake, BigQuery)
- Slack/email alerting for KPI thresholds
- Code repository with pipeline scripts
- Documentation for all metrics and alerts
- Sentry error tracking for pipelines
What we ship in this practice.
AI churn prediction KPI dashboard
A SaaS client reduced churn by 15% in 3 months. We built a dashboard tracking churn probability scores, retention rates, and model accuracy. Tech: Python, Airflow, Tableau.
Chatbot deflection rate tracker
A fintech company improved deflection by 22% with a real-time CSAT and topic clustering dashboard. Tech: Dialogflow, BigQuery, Looker.
Defect detection KPI system
A manufacturer reduced false positives by 30% using a custom dashboard that tracked precision and recall per production line. Tech: TensorFlow, Kafka, Grafana.
AI recommendation engine analytics
An ecommerce brand saw a 12% increase in AOV after we built a dashboard that correlated click-through rate with revenue. Tech: PyTorch, Snowflake, Metabase.
Healthcare diagnostic AI monitor
A clinic reduced diagnostic turnaround by 40% with a dashboard tracking model sensitivity, specificity, and usage. Tech: PyTorch, AWS Kinesis, Grafana.
AI-powered underwriting KPI tracker
An insurtech company reduced manual review time by 8 hours per week by tracking automated approval rates and accuracy. Tech: XGBoost, Airflow, Tableau.
Why teams hire us.
Fixed price, not hourly
Every project gets a fixed quote in the Roadmap phase (week 2). No hourly billing, no surprise invoices, no scope creep charges.
Senior engineers only
All AI work is done by senior engineers with 8+ years of experience. They have built production systems at companies like Google, Stripe, and Palantir.
We ship in 8-12 weeks
An MVP with full KPI tracking is delivered in 12 weeks on average. We start with a 2-week Roadmap, then build, test, and deploy in 10 weeks.
GDPR, HIPAA, SOC 2 ready
All data pipelines are built with encryption, access controls, and audit logging. We have delivered compliant systems for healthcare and fintech clients.
Full ownership of code
You own the source code, data pipelines, and dashboards. No vendor lock-in. We push everything to your Git repository.
CI/CD from day one
We deploy using GitHub Actions, Docker, and Kubernetes. Every dashboard update goes through automated testing before hitting production.
Idea → Roadmap → Product → Release.
Every engagement runs through the same four-stage pipeline. Predictable by design.
Questions clients ask first.
Related services and industries.
Ready to build? Let's talk.
30-minute discovery call. No deck. We'll tell you honestly what it takes, how long, and how much.
─── share this page ───
