AI Optimisation & Support
Maximise AI performance and ROI with continuous optimisation and expert support
Executive Summary Executive Overview
AI systems require ongoing optimisation to maintain peak performance and deliver maximum value. Our Optimisation & Support services ensure your AI investments continue to improve over time, adapting to changing business needs, new data patterns, and evolving technology capabilities. We provide proactive monitoring, continuous improvement, and responsive support to keep your AI systems running at their best.
The Challenge Executive Overview
Common Post-Deployment Issues
- • Performance Degradation: Model accuracy declines as data patterns shift over time
- • Scaling Challenges: Systems optimised for pilot scale struggle with production load
- • Cost Creep: AI infrastructure costs increase without proportional value gains
- • Knowledge Gaps: Internal teams lack expertise to troubleshoot and optimise AI systems
- • Missed Opportunities: New AI capabilities emerge but aren't incorporated
Impact of Neglecting Optimisation
AI systems left unoptimised experience:
- • 15-30% annual performance decline as data patterns drift
- • Infrastructure costs 2-3x higher than optimised systems
- • User frustration from slow responses and inaccurate results
- • Security vulnerabilities as threats evolve
- • Inability to capitalize on new AI breakthroughs
Comprehensive Optimisation Approach Executive Overview
Our optimisation framework combines proactive monitoring, continuous improvement, and responsive support to ensure your AI systems deliver increasing value over time rather than degrading.
Monitoring & Alerting
- • Real-Time Dashboards: Business and technical metrics in one view
- • Proactive Alerts: Detect issues before users notice
- • Trend Analysis: Identify degradation patterns early
- • Cost Tracking: Monitor AI spend vs business value
Performance Tuning
- • Model Retraining: Update models with fresh data quarterly
- • Prompt Optimisation: Refine prompts for better results
- • Infrastructure Scaling: Right-size resources for load
- • Caching Strategy: Reduce API costs by 40-60%
Security & Compliance
- • Vulnerability Scanning: Regular security audits
- • Compliance Monitoring: GDPR, HIPAA, SOC 2 adherence
- • Access Reviews: Quarterly permission audits
- • Incident Response: 24/7 security incident handling
Feature Enhancement
- • New Capabilities: Incorporate latest AI advances
- • User Feedback: Prioritize improvements based on usage
- • A/B Testing: Validate changes before full rollout
- • Integration Expansion: Connect additional systems
Support Tiers
- • Business hours coverage
- • 4-hour response time
- • Monthly optimisation reviews
- • Quarterly model retraining
- • 24/7 coverage
- • 1-hour response time
- • Weekly optimisation reviews
- • Monthly model retraining
- • 24/7 dedicated team
- • 15-minute response time
- • Continuous optimisation
- • Real-time model updates
Technical Optimisation Techniques
Technical Details
Model Performance Optimisation
Continuous Training Pipeline
Automated retraining prevents model drift and incorporates new patterns
- • Data drift detection using statistical tests (Kolmogorov-Smirnov, Chi-squared)
- • Automated data labeling for active learning
- • Model versioning and A/B comparison
- • Automated rollback if performance degrades
Prompt Engineering Optimisation
Systematic refinement of prompts for better accuracy and cost efficiency
- • Automated prompt testing across model versions
- • Few-shot example optimisation
- • Chain-of-thought reasoning when beneficial
- • Output format optimisation for downstream parsing
Cost Optimisation
Reduce AI infrastructure costs without sacrificing performance
- • Semantic caching with vector similarity (40-60% cost reduction)
- • Request batching for GPU efficiency
- • Model compression and quantization
- • Intelligent routing to smaller models for simple queries
Monitoring & Observability
Key Metrics Tracked
- • Task completion rates
- • User satisfaction scores
- • Time savings vs manual processes
- • Error rates and escalations
- • Response times (p50, p95, p99)
- • Model accuracy and confidence
- • API costs per request
- • Infrastructure utilization
Security Hardening
- • Prompt Injection Defense: Input sanitization and validation
- • Output Filtering: Block sensitive data leakage
- • Rate Limiting: Prevent abuse and DDoS attacks
- • Audit Logging: Full request/response trails for compliance
- • Anomaly Detection: ML-based detection of unusual patterns
Continuous Improvement Process
- 1. Monitoring: Automated dashboards track performance 24/7
- 2. Analysis: Weekly review of trends and anomalies
- 3. Hypothesis: Identify optimisation opportunities
- 4. Testing: A/B test improvements in controlled environment
- 5. Deployment: Gradual rollout with monitoring
- 6. Validation: Confirm improvements in production
Optimisation Results Executive Overview
Continuous Improvement Impact
How We Help Executive Overview
Healthcare - Patient Triage System
AI-powered patient triage system showing declining accuracy, increasing costs, and user complaints about slow responses
Comprehensive optimisation: model retraining with recent data, prompt refinement, semantic caching implementation, infrastructure right-sizing, and continuous monitoring dashboard
Ready to Get Started?
Schedule a free consultation to discuss how we can help achieve your goals.