Fractional CTO for Generative AI
From traditional ML to RAG pipelines and agentic workflows, I help startups and SMEs craft GenAI solutions that are scalable and tailored to their unique needs.
Why Hire a Fractional CTO for GenAI?
Ship AI Features in Weeks, Not Quarters
Stop wasting months on POCs that never launch. I help you ship production-ready RAG pipelines and AI agents fast.
Cut AI Costs by 60% Without Sacrificing Quality
Optimize LLM inference, reduce token usage, and right-size your AI infrastructure. Typical savings: $10K-$30K/month.
Avoid the $200K AI Implementation Mistakes
Navigate hallucinations, data privacy, compliance, and scaling challenges with 25+ years of production system experience.
Why GenAI for Your Business?
GenAI is revolutionizing industries by automating processes, enhancing decision-making, and creating innovative customer experiences. Here’s how I can help you leverage it:
- Integrate AI-driven solutions to enhance your product offerings and reduce operational costs.
- Develop scalable architectures to handle AI workloads and adapt to evolving needs.
- Strategize and implement AI applications that align with your business goals.
With decades of experience in app development and cloud solutions, I offer the expertise to turn your vision into reality.
Case Studies in Generative AI Architecture
Generative AI for FinOps Automation
As a Technical Lead at Xogito Group, I led the development of a state-of-the-art chatbot for a FinOps startup in Seattle. The system integrated cost attribution, budgeting, monitoring, and optimization for generative AI services—showcasing the potential of combining finance operations with intelligent automation.
- Delivered an MVP in one month by leveraging pre-existing components and containerized backend services.
- Enabled RAG, tool calling, image generation and cost attribution using models from OpenAI, Anthropic and AWS Bedrock.
- Accelerated customer acquisition by demonstrating early product-market fit with a working prototype.
- Built a scalable foundation for integrating AI cost insights across multiple cloud providers.
GraphRAG for Precision Medicine
As CTO at MateBio, I led the architecture and development of a next-generation biomedical chat that combined AI-powered natural language querying with interactive knowledge graph visualization. The assistant enabled wet lab researchers to explore complex biological relationships in real time, grounded on a knowledge graph powered by Neo4j.
- Created a chat-based interface that translates biomedical questions into Cypher queries against Neo4j.
- Integrated entity recognition, provenance tracking, and confidence scoring for explainable results.
- Implemented real-time streaming and progress indicators to enhance transparency during query processing.
- Developed interactive 2D and 3D graph visualizations to help researchers navigate biological pathways and relationships.
GenAI Services
RAG Pipeline Implementation
Launch production-ready retrieval systems in 8-12 weeks. Handle 10K+ queries/day with 95%+ accuracy using vector databases and hybrid search.
Agentic Workflow Development
Build AI agents that actually work. From customer support to research assistants—deploy autonomous agents that handle complex multi-step tasks.
LLM Cost Optimization
Reduce AI inference costs by 60%+. Optimize prompts, implement caching, right-size models, and switch providers strategically.
AI POC → Production Pipeline
Turn abandoned POCs into revenue-generating features. Add guardrails, monitoring, compliance, and scaling to get AI live in production.
Fine-Tuning & Model Customization
When base models aren't enough, fine-tune for your domain. Optimize for quality, cost, and latency with custom training pipelines.
AI Integration Strategy
Navigate vendor selection (OpenAI, Anthropic, AWS Bedrock, Azure), architecture decisions, and compliance requirements with confidence.
AI Strategy: From ML to Foundational Models
Navigating the rapidly evolving AI landscape can be daunting. I guide my clients in choosing the right AI approach for their needs, starting with the easiest and most cost-effective options to validate before scaling further.
- Traditional Machine Learning: Best for structured problems with clearly defined datasets.
- Foundational Models: Suitable for scenarios requiring robust natural language understanding and generation.
- Retrieval-Augmented Generation (RAG): Ideal for integrating large-scale knowledge bases with generative capabilities.
- Agentic Workflows: Automating complex decision-making tasks using AI agents.
- Fine-Tuning and Distilling: Optimizing models for specific business needs while reducing infrastructure costs.
My goal is to ensure every project starts lean, validates quickly, and scales intelligently, saving costs and delivering value faster.
Is This You?
If any of these AI challenges sound familiar, let's talk:
Spent $50K+ on AI POCs that never made it to production?
Your LLM costs are spiraling from $5K to $30K/month with no clear ROI?
Hallucinations and accuracy issues making your AI features unreliable?
Competitors are shipping AI features while you're stuck evaluating vendors?
Your data science team can't get models into production?
Need to navigate data privacy, compliance, and security for AI systems?
If you checked even one box, I can help.
Book Your Free ConsultationThe Role of the AI Engineer
The emergence of foundation models has transformed the landscape of AI development, shifting the focus from model creation to application development. AI engineers are now at the forefront of adapting and integrating these powerful models into innovative products that drive business outcomes.
Key Differentiators from ML Engineering:
- Model Adaptation vs. Development: AI engineers focus on fine-tuning and integrating existing models rather than creating them from scratch.
- Unstructured Data Mastery: Work involves deduplication, tokenization, context retrieval, and ensuring data quality, unlike traditional ML's emphasis on feature engineering with tabular data.
- Differentiation Through Applications: Success is achieved by innovating in application interfaces and workflows rather than solely relying on proprietary model quality.
- Closer to Full-Stack Development: AI engineers often come from web or full-stack backgrounds, bringing a product-first mindset and rapid prototyping skills.
- Product-First Approach: Foundation models enable teams to focus on building the product first and only investing in custom data and models once the product shows promise.
As an AI engineer, I bring expertise in navigating this evolving landscape, ensuring a lean, iterative approach to validate ideas and scale intelligently. Whether you're starting with traditional ML or exploring cutting-edge applications with foundation models, I guide you every step of the way.
GenAI Investment Levels
From POC validation to production-ready AI systems—transparent pricing that scales with your AI ambitions.
AI Readiness Assessment
$3,500
Evaluate your AI opportunities and create a concrete implementation roadmap.
- Use case identification & prioritization
- Data readiness evaluation
- ROI projection & cost modeling
- 90-day AI implementation plan
AI Advisory
$6K-10K/month
Strategic guidance for AI product development and optimization.
- Weekly AI strategy sessions
- Vendor & model selection guidance
- Architecture & design reviews
- Cost optimization recommendations
Hands-On AI Implementation
$12K-18K/month
Full AI product development from POC to production.
- Everything in Advisory, plus...
- Hands-on RAG/agent development
- Production deployment & monitoring
- Team training & knowledge transfer
All engagements start with a free consultation to evaluate fit and scope your AI needs.
GenAI Frequently Asked Questions
How do you prevent AI hallucinations in production systems?
I implement multiple layers of protection: retrieval-augmented generation (RAG) with verified sources, confidence scoring, output validation, and human-in-the-loop workflows for critical decisions. For high-stakes applications, I add guardrails and fallback mechanisms.
Should we use OpenAI, Anthropic, or AWS Bedrock for our AI features?
It depends on your use case, data privacy requirements, and budget. I help you evaluate options based on: model quality for your task, latency requirements, data residency needs, cost at scale, and vendor lock-in risk. Often the answer is "start with one, prepare to switch."
What's the difference between fine-tuning and RAG?
RAG retrieves relevant information and adds it to prompts—great for knowledge that changes frequently. Fine-tuning trains models on your data—better for specialized tasks or domain-specific language. I usually recommend starting with RAG (faster, cheaper) and fine-tuning only when RAG isn't sufficient.
How much training data do we actually need for our AI project?
For RAG systems: as little as your documented knowledge base. For fine-tuning: typically 500-5,000 high-quality examples. For training from scratch: millions (don't do this). Most startups overestimate data needs—foundation models are powerful out-of-the-box.
Can you help us get from POC to production?
Yes—this is one of my core services. I add production-grade infrastructure: monitoring, error handling, rate limiting, cost controls, compliance guardrails, and scalability. Typical timeline: 6-10 weeks from working POC to production-ready system handling real users.
How do you handle data privacy and compliance for AI systems?
I implement data anonymization, on-premise/private cloud deployment options, audit logging, and ensure compliance with GDPR, HIPAA, or SOC 2 as needed. For sensitive data, I can architect AI systems that never send raw data to third-party LLMs.
What if our AI costs spiral out of control?
I build cost controls from day one: prompt optimization, caching strategies, rate limiting, model right-sizing, and monitoring dashboards. I also implement automatic alerts when costs exceed thresholds. Typical cost reductions: 40-70% without quality loss.
Let’s Build Your GenAI Strategy
From traditional ML to RAG pipelines and agentic workflows, I'll guide you through designing the best, most cost-effective way to validate and implement your vision.