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AI production readiness is the process of turning an AI prototype into a secure, reliable production system. I deliver HA/DR architecture, monitoring, cost controls, compliance alignment, and clear timelines so you can launch with confidence.
From AI Prototype to Production Reality
Your AI works in demos but fails in production. I bridge that critical gap with proven infrastructure, security, scaling, and compliance frameworks. Transform your prototype into a production-ready system.
Results Methodology: Performance metrics represent reported client outcomes based on post-deployment surveys and engagement retrospectives. "Significantly Faster" compares guided deployment timelines vs. client-estimated DIY timelines. "Substantial Cost" reflects infrastructure optimization opportunities identified during assessments. "High Uptime" represents production system reliability targets achievable with proper HA/DR implementation. Actual results vary significantly based on starting infrastructure maturity, current technical debt, team responsiveness, and specific implementation requirements. These outcomes are not typical or guaranteed.
Your AI Production Journey
From prototype to production-ready system
Why Do AI Prototypes Fail in Production?
The gap between working demos and production systems is where most AI projects fail. Here's why, and how to bridge it.
Infrastructure Scaling
Demo environments handle toy datasets. Production needs auto-scaling infrastructure, load balancing, and performance optimization for real-world usage patterns.
Security & Compliance
Prototypes ignore security. Production requires encryption, access controls, audit logging, and compliance frameworks (SOC 2, HIPAA, etc.).
Cost Management
Unoptimized AI infrastructure costs significantly more than necessary. Production needs cost monitoring, rightsizing, and optimization strategies.
Your AI Production Readiness Process
A systematic approach to transforming prototypes into production-ready systems
Discovery & Assessment
Comprehensive evaluation of your AI prototype, infrastructure, and production requirements.
Infrastructure Architecture
Design scalable, secure cloud infrastructure with auto-scaling, monitoring, and CI/CD pipelines.
Security Implementation
Implement security controls, compliance frameworks, and access management for production AI.
Optimization & Scaling
Performance tuning, cost optimization, and enterprise scaling for production workloads.
Common Pitfalls in DIY AI Production
Understanding typical challenges helps you make informed decisions about your deployment approach
Infrastructure Sizing
AI workloads have different resource profiles than typical web applications. Teams often over-provision (wasting budget) or under-provision (causing latency issues) without load testing specific to AI inference patterns.
Monitoring Gaps
Standard application monitoring misses AI-specific signals like model drift, inference latency, and token consumption. Teams discover issues through user complaints rather than proactive alerts.
Security Blindspots
AI systems introduce unique attack vectors: prompt injection, data exfiltration through model outputs, and API key exposure. Traditional security reviews may not cover these AI-specific risks.
Runaway Costs
Token-based pricing and variable inference costs can surprise teams without proper budgeting controls. Usage spikes from unexpected traffic patterns or recursive calls can exceed projections.
HA/DR Oversights
AI systems have dependencies on external APIs and models that require different resilience patterns than traditional applications. Failover strategies for AI components often need special consideration.
Compliance Complexity
AI systems processing sensitive data face evolving regulatory requirements. Data residency, model transparency, and audit logging requirements differ from traditional application compliance.
These challenges are common across organizations deploying AI for the first time. The assessment and deployment packages below are designed to identify and address these issues systematically.
Choose Your AI Production Path
Three proven packages designed for different stages of your AI production journey
Assessment
- Detailed technical assessment report
- Production readiness gap analysis
- Cost estimates and implementation roadmap
What's Included
- Comprehensive assessment report with findings
- Production readiness gap analysis
- Cost estimates for deployment
- Implementation roadmap with priorities
- 30-minute consultation to discuss findings
What's Not Included
- AWS infrastructure costs (you pay AWS directly)
- Third-party software licenses or API costs
- Implementation of recommended changes (see Deployment Sprint)
- Ongoing support after delivery (available separately)
Timeline starts when codebase access and initial materials are provided. Assumes timely client responsiveness (48-hour feedback turnaround). Complex systems may require additional time.
What affects timeline?
- Codebase size and complexity
- Infrastructure access provisioning
- Client responsiveness to questions
- Documentation availability
Deployment Sprint
- Complete production infrastructure setup
- CI/CD pipelines for AI model deployment
- Security controls and compliance implementation
- Monitoring, logging, and handover documentation
What's Included
- Complete production infrastructure setup
- CI/CD pipelines configured and tested
- Security controls and access management
- Monitoring, logging, and alerting
- Handover documentation and training
What's Not Included
- AWS infrastructure costs (you pay AWS directly)
- Third-party software licenses (e.g., monitoring tools, security scanners)
- Client team availability required for daily standups and approvals
- Ongoing support after deployment (available separately)
Assumes infrastructure access provisioned, approval processes documented, and client team available for daily standups. Timeline may extend for complex multi-cloud environments.
What affects timeline?
- Infrastructure access provisioning
- Approval process complexity
- Client team availability for standups
- Third-party dependencies
- Multi-cloud environment complexity
Optimization
- Performance optimization and auto-scaling
- Advanced monitoring and observability
- Compliance frameworks (SOC 2, ISO 27001)
- Cost optimization and team training
What's Included
- Performance optimization and auto-scaling configuration
- Advanced monitoring and observability dashboards
- Compliance framework implementation (SOC 2, ISO 27001)
- Cost optimization analysis and recommendations
- Team training and knowledge transfer
What's Not Included
- AWS infrastructure costs (you pay AWS directly)
- Third-party monitoring/security tool licenses
- Formal compliance certification audits (we prepare you for audit)
- Client team availability required for training and approvals
Assumes baseline monitoring in place, access to production metrics, and stakeholder availability for weekly reviews. Large-scale systems may require additional time.
What affects timeline?
- Baseline monitoring maturity
- Production metrics access
- Stakeholder availability for reviews
- System scale and complexity
- Compliance requirements
If we fail to deliver infrastructure-ready deployment artifacts (containerized services, IaC templates, CI/CD pipelines) within the agreed timeline, you are not obligated to pay additional fees beyond the assessment fee. Timeline extensions may apply for client-side delays (credential access, approval processes, or scope changes).
AI Production Success Stories
Real results from transforming AI prototypes into production systems
Results Methodology: Metrics based on client-reported outcomes from post-deployment surveys and engagement retrospectives. Comparisons reflect guided implementation vs. client-estimated DIY approaches. Results vary significantly based on starting maturity, technical debt, team responsiveness, and implementation scope. These outcomes are not typical or guaranteed.
Frequently Asked Questions
Answers to common questions about AI production readiness
What makes AI production different from regular software deployment?
AI systems require specialized infrastructure for model serving, specialized monitoring for model performance drift, unique security considerations for data protection, and compliance frameworks specific to automated decision-making systems.
How long does AI production readiness typically take?
Assessment typically takes 7-10 business days, basic production deployment typically takes 4-6 weeks, and enterprise optimization generally takes 8-12 weeks. Timelines may vary based on your current AI maturity, infrastructure complexity, and responsiveness to feedback.
What's the ROI of investing in AI production readiness?
Many clients achieve significant ROI through AI production readiness, with typical benefits including substantial infrastructure cost reductions through optimization and rightsizing, improved system reliability, faster time-to-market for new features, and avoided costly production failures. Results vary based on specific implementation and market conditions.
Using Claude or ChatGPT generated code? See the AI Code Security service for guardrails, logging, and compliance evidence.
Ready to Transform Your AI Prototype?
Start with a no-cost 30-minute discovery call to discuss your AI production needs. Receive initial guidance and a recommended approach—no obligation. The comprehensive $5,000 assessment package (detailed above) is available if it's the right fit.