Operate & Scale
Keep AI systems performing optimally as usage grows, requirements evolve, and the technology landscape changes.
Continuous monitoring, optimization, and evolution as a managed service or advisory retainer
The Expensive Part of AI Isn't Deployment. It's Everything After.
You deployed AI. It worked great. For about three months. Now the quality is slowly degrading—users are noticing hallucinations more often, the system is slower, and your API costs are 2x what they were when you launched. Your original team that built it is distracted by the next project. And when OpenAI releases a new model, you're not sure if you should upgrade or if it'll break everything.
This is the pattern: Teams get excited about AI, build something impressive, deploy it, and then realize they don't have the expertise—or the bandwidth—to keep it optimized and current. The system doesn't fail. It just slowly stops being as good as it was. And the costs don't stop, they compound.
Most organizations don't hire for "AI operations" because it's unclear what that role even is. We've built the expertise internally and now we're offering it as a service. We monitor your systems 24/7, catch degradation before your users do, run optimization sprints to reduce costs and improve quality, and evaluate new models to keep you current without the risk of breaking what's working.
Business Impact
Cost Optimization
AI costs spiral without active management. We implement caching, model routing, prompt optimization, and usage monitoring to reduce API costs while maintaining or improving output quality. Typical optimizations reduce costs by 30-50% within the first quarter.
Performance Reliability
Proactive monitoring catches issues before they impact users. Automated alerts, drift detection, and regression testing ensure consistent output quality. Organizations with active monitoring report higher user satisfaction and fewer escalations.
Continuous Improvement
Regular optimization sprints improve accuracy, speed, and cost efficiency over time. Prompt engineering, A/B testing, and fine-tuning based on production data ensure systems get better, not stale. The AI landscape evolves rapidly—we help you take advantage of new capabilities.
Strategic AI Evolution
New models, features, and techniques emerge constantly. We evaluate GPT-5, Claude Opus upgrades, new voice APIs, and other innovations for applicability to your use cases. Stay current without dedicating internal resources to tracking the AI landscape.
How It Works
Our 4-phase ongoing cycle ensures your AI systems remain performant, cost-effective, and aligned with business objectives as usage scales and requirements evolve.
Baseline & Infrastructure Setup
1-2 weeksEstablish monitoring infrastructure, define KPIs, capture baseline performance metrics, and set up alerting. We instrument your AI systems for comprehensive observability.
- • Deploy monitoring stack (Langfuse, Prometheus, etc.)
- • Configure performance & cost dashboards
- • Define SLAs and alert thresholds
- • Document current architecture & dependencies
- • Live Monitoring Dashboards
- • Baseline Performance Report
- • Alert Configuration & Runbooks
- • Operations Documentation
Continuous Monitoring & Alerting
OngoingTrack performance, quality, costs, and usage in real-time. Proactive monitoring catches degradation, anomalies, and cost spikes before they become problems. 24/7 oversight with human-in-the-loop for critical issues.
- • Latency, throughput, error rates
- • Output quality & hallucination detection
- • Token usage & API costs
- • User feedback & satisfaction scores
- • Weekly performance summaries
- • Incident reports & root cause analysis
- • Cost trend analysis
- • Quality metrics & drift detection
Optimization Sprints
Monthly or QuarterlyRegular improvement cycles focused on cost reduction, performance enhancement, or quality improvements. Prompt engineering, A/B testing, caching strategies, and model upgrades keep systems optimized.
- • Prompt-ops & template refinement
- • Caching & response reuse strategies
- • Model routing (GPT-4 → GPT-3.5 where viable)
- • Fine-tuning with production data
- • Optimization Recommendations Report
- • A/B Test Results & Analysis
- • Updated Prompts & Configurations
- • Cost Savings Summary
Strategic Evolution
QuarterlyEvaluate new AI capabilities (GPT-5, Claude Opus, new voice models) for applicability to your use cases. Plan and execute model upgrades, feature additions, and architectural improvements.
- • New model evaluation & benchmarking
- • Feature enhancement planning
- • Competitive landscape monitoring
- • Roadmap alignment with business goals
- • Quarterly Strategic Roadmap
- • Technology Evaluation Reports
- • Upgrade Plans & Migration Strategies
- • Executive Business Reviews
What You Receive
Comprehensive operations management with full transparency into system health, costs, and performance trends.
Monitoring & Dashboards
- • Real-time performance & cost dashboards (Grafana, custom)
- • Weekly performance summary emails
- • Alert notifications for anomalies or SLA breaches
- • Monthly trend reports with insights
- • User feedback & satisfaction analytics
Optimization Services
- • Prompt engineering & A/B testing
- • Cost optimization implementations
- • Performance tuning & latency reduction
- • Model evaluation & upgrade execution
- • Regression testing for all changes
Incident Response
- • 24/7 alert monitoring & triage
- • Incident response & root cause analysis
- • Emergency hotfixes & rollbacks
- • Post-mortems with prevention recommendations
- • Runbook updates & process improvements
Strategic Guidance
- • Quarterly business reviews with executives
- • Technology roadmap planning
- • New model & capability evaluation
- • Use case expansion recommendations
- • Industry best practices & benchmarking
Engagement Model
Ongoing monthly or quarterly retainers. Typical engagements last 12+ months as AI systems require continuous evolution.
AI operations engineer, prompt engineer for optimization, DevOps for infrastructure. Strategic advisor for quarterly planning.
Product owner (5 hrs/week) for prioritization and feedback. Engineering liaison for deployment coordination. Stakeholders for quarterly reviews.
How We Measure Success
Key Metrics We Track
Comprehensive visibility across performance, quality, cost, and business impact.
Performance
Latency (p50, p95, p99), throughput, availability, error rates
Quality
Output accuracy, relevance scores, user feedback, hallucination rates
Cost
Token usage, API costs, infrastructure spend, cost per transaction
Usage
Active users, session patterns, feature adoption, engagement metrics
Business Impact
Time saved, revenue impact, customer satisfaction, process efficiency
Compliance
Policy violations, audit trail completeness, data governance adherence
Service Tiers
Flexible engagement models based on your AI operations maturity and support needs.
Monitoring & Alerting
Essential observability for teams that can handle their own optimizations but need expert monitoring and incident response.
- • Real-time monitoring dashboards
- • 24/7 alert management
- • Incident triage & escalation
- • Weekly performance reports
- • Best for: Internal AI teams needing expert oversight
Full Operations Management
Complete AI operations including monitoring, optimization, and continuous improvement. Ideal for production systems without dedicated AI operations teams.
- • Everything in Monitoring tier
- • Prompt-ops & optimization sprints
- • Cost reduction implementations
- • Model evaluation & upgrades
- • Best for: Most production AI deployments
Strategic + Ops
Full operations plus strategic guidance for AI roadmap, new use cases, and technology evolution. For organizations scaling multiple AI initiatives.
- • Everything in Full Operations
- • Quarterly strategic planning
- • New use case identification
- • Executive business reviews
- • Best for: Scaling AI across organization
Why Choose RPT.ai for AI Operations
Production Operations Experience
We operate AI systems handling real production workloads, not just monitor dashboards. Our team has debugged prompt failures at 3am, optimized away runaway API costs, and upgraded models without downtime. We know what actually breaks in production.
Multi-Model Expertise
Platform-agnostic operations across OpenAI, Anthropic, open-source models, and voice AI platforms. We optimize costs by routing to the right model for each task, not locking you into one vendor.
Prompt Engineering Depth
Dedicated prompt engineers focused on optimization. We treat prompts as code—versioned, tested, and continuously improved. Typical prompt optimizations improve quality by 15-25% while reducing tokens by 20-40%.
Build + Operate Synergy
Unlike pure MLOps vendors, we build AI systems AND operate them. This means operations recommendations are informed by implementation experience, and we can execute optimizations ourselves without handoffs.
Frequently Asked Questions
Do we need this if we have internal DevOps teams?
DevOps teams excel at infrastructure and deployments but typically lack AI-specific expertise—prompt engineering, model evaluation, LLM cost optimization, and quality monitoring. Think of us as an AI operations layer that complements your DevOps, not replaces it.
What happens if you find an issue after hours?
We monitor alerts 24/7. For critical production issues (system down, major quality degradation), we follow your escalation procedures and can implement emergency fixes. For non-critical issues, we triage and address during business hours. You define what constitutes "critical" during onboarding.
How much can you typically reduce costs?
Varies by system complexity and current optimization level. Newly deployed systems often see 30-50% cost reductions in the first quarter through caching, prompt optimization, and model routing. Mature systems see smaller but ongoing improvements (5-15% quarterly) as we find edge cases and efficiency gains.
Can you operate systems you didn't build?
Yes. We onboard to existing AI systems regularly. We conduct a 1-2 week discovery phase to understand architecture, review code, document dependencies, and establish baselines. If the system lacks proper instrumentation, we add monitoring as part of onboarding.
What access do you need to our systems?
Read access to monitoring dashboards, logs, and metrics. Write access to prompt configurations and model parameters for optimization. We don't need access to customer data—monitoring operates on metadata and aggregates. All access follows your security policies and can be revoked anytime.
Can we start with a trial period?
Yes. We offer 3-month pilot engagements for organizations unsure about ongoing operations needs. This includes full monitoring setup, baseline optimization, and incident response. After 3 months, decide whether to continue, scale up, or transition operations in-house with our documentation.
Your AI System Is Getting Slower and More Expensive. We Can Fix That.
Let's look at what you deployed, what's degraded, what your costs actually are, and what optimizations will help. We'll be honest about whether you need ongoing operations or if your system just needs a tune-up.
Usually takes 2-3 weeks of monitoring before we can recommend next steps.