Fractional CTO for AI & Machine Learning

From classic ML to RAG and agentic workflows, I help SaaS startups ship AI that actually reaches production.

20+

Years Exp.

30+

Companies

USA

& LATAM

Book a Free Consultation

Why Hire a Fractional CTO for AI/ML?

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

CTO · MateBio

GraphRAG for a TechBio Startup

I led the transformation of raw biomedical knowledge graph data into a scalable, defensible, commercially viable platform. MateBio provides AI-powered tools that enable wet lab researchers at biotech and pharma companies to explore complex biological relationships through natural language queries and interactive visualizations.

Architected the core GraphRAG pipeline — a multi-tool LLM agent that turns natural-language biomedical questions into validated Cypher over a Neo4j knowledge graph spanning 80+ integrated data sources, with entity recognition, provenance tracking, and confidence scoring.
Built a hybrid entity-resolution engine that maps imprecise researcher terms ("p53", "breast cancer") to exact graph identifiers, fusing heuristic biomedical NER, type-scoped full-text and vector search, and small-model disambiguation.
Added a systems-biology analytics layer — Graph Data Science centrality (PageRank and personalized PageRank over pre-computed projections) plus a synthetic-EHR cohort service producing "Spoke signatures" that rank genes and pathways from clinical cohorts.
Built multi-modal data ingestion so researchers could bring their own data alongside the graph — PDF and URL document RAG, plus CSV and omics analysis with auto-generated mermaid diagrams.
Stood up the platform across two clouds — AWS and GCP, each running Neo4j and PostgreSQL — with Terraform-driven CI/CD, automated database migrations, and a tested cross-cloud migration path.

Lead & Architect · Xogito

Generative AI for FinOps Automation

I led the development of an AI-powered chatbot for a FinOps startup in Seattle. The product helps enterprise teams attribute, budget, monitor, and optimize generative AI spend across providers.

Architected a multi-provider abstraction over OpenAI, Anthropic, and AWS Bedrock, routing each request to the best-fit model—reasoning, RAG, image generation.
Integrated FinOps-domain tools that let the chatbot act on real cost and usage data—attribution, budget checks, spend forecasts—turning conversation into operational answers instead of generic advice.
Implemented token streaming, tool-call progress indicators, and persistent chat history to keep long, multi-step queries responsive and transparent.
Introduced workspace isolation and role-based access controls to support multi-tenant enterprise deployments from day one.
Delivered the MVP in one month by reusing existing front-end components and infrastructure—securing early customers and validating product-market fit without a full system rebuild.

Explore other case studies as a Fractional CTO...

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 Consultation

Engagement Options

From AI readiness to a bounded MVP proof to full AI engineering leadership—transparent pricing that scales with your ambition.

AI Assessment

$4K one-time

Know what is worth building. Review your AI opportunity, data, architecture, risks, and path to production before committing to a retainer.

AI readiness and architecture review
POC-to-production risk map
Prioritized implementation plan

Advisory + AI MVP

$5K-8K/month

I help you decide and prove it works. Strategy, vendor calls, architecture reviews, cost optimization, plus one bounded AI MVP or POC slice per month.

Weekly AI strategy sessions
Vendor & model selection guidance
Architecture & design reviews
Bounded MVP or POC implementation

Embedded AI CTO

$10K-15K/month

I run your AI engineering function. I own the roadmap, lead your engineers, ship production AI features, and carry technical accountability.

Everything in Advisory + AI MVP, plus...
Hands-on RAG/agent development
Production deployment & monitoring
Lead your AI engineers (or hire them)

All engagements start with a free consultation to evaluate fit and scope your AI needs.

Want to start smaller? Begin with a Technical Assessment ($4K) — covers AI readiness alongside the rest of your stack.

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.

Book a Consultation Connect on LinkedIn

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What is a Fractional CTO?

Part-time tech leadership without full-time cost. Learn when you need one.