Continuous tuning, tracking, and updates to ensure your AI solution performs today—and tomorrow
AI is Never a One-Time Job
Unlike traditional software, AI solutions are dynamic. Over time, user behavior shifts, data patterns evolve, and external factors change. If left unattended, even the best models degrade.
That’s why Monitoring & Optimization is essential. At AK Technolabs, we don’t just build AI—we keep it performing at its best through proactive monitoring, performance tuning, and ongoing improvements.
What We Offer in AI Monitoring & Optimization
Our services ensure your AI systems remain high-performing, explainable, and aligned with your evolving business goals.
- Model Drift Detection
Automatically track when model predictions start to deviate due to changes in data patterns or user behavior. - Performance Monitoring Dashboards
Real-time dashboards showing model accuracy, latency, prediction volumes, and feedback loops. - Continuous Learning Pipelines
Re-train and fine-tune models as new labeled data becomes available, without full re-deployment. - Error Analysis & Root Cause Detection
Identify mispredictions, feature issues, and unexpected edge cases before they affect users. - Business Metrics Alignment
Map AI metrics (e.g., accuracy, F1 score) to business KPIs (e.g., conversion, churn, engagement). - A/B Testing & Shadow Deployment
Deploy new models in parallel (shadow mode) or run experiments to test different versions safely.
Case Study: Improving AI Accuracy for a Fitness App’s Meal Recommendation Engine
Client: Mobile fitness app with over 300K users
Problem: AI meal planner saw reduced engagement over time; user complaints increased about irrelevant suggestions
Goal: Restore trust and improve personalization accuracy
Timeline: 6 weeks
Stack: Python, TensorFlow, Google Cloud Monitoring, MLflow, Looker
Optimization Process
- Baseline Performance Audit
Analyzed historical accuracy, recommendation click-through rate (CTR), and diet compliance feedback. - Monitoring Setup
Deployed a real-time dashboard using MLflow and Looker to monitor meal recommendation acceptance rates and errors. - Drift Detection
Identified concept drift: users’ preferences were shifting seasonally and post-holiday. - Feedback Loop Integration
Added thumbs up/down feedback in-app, connected to data pipeline for automatic retraining. - Model Tuning
Retrained the meal recommendation engine using the latest 3-months user data with updated dietary trends and constraints. - Shadow Deployment & A/B Test
Tested new model in shadow mode and then ran A/B testing across 20% of users.
Results & Metrics
- 37% improvement in recommendation acceptance rate
- 24% reduction in user-reported dissatisfaction
- Feedback collection rate increased 4X
- Achieved 97% model uptime with real-time drift alerts
Don’t Let Your AI Go Stale
AI performance declines without care—and that affects user trust and business outcomes. At AK Technolabs, we provide end-to-end AI lifecycle support to ensure your solution evolves with your users, your data, and your goals.
Explore more of our AI App Development Services or contact our AI team for a performance audit of your current AI systems.
