From AI idea to AI in production
I ship AI products end-to-end. EverAd, my own platform, is a full-stack SaaS for AI-powered ad creative. Google Imagen and Veo for image and video generation, a proprietary scoring system, Stripe billing, campaign sharing, and embeddable infrastructure. IntakeKit is a conversational intake widget in production across PPC landing pages with journey flow routing and CRM integration.
When I integrate AI for clients, I bring the practical lessons from running it myself: token budgets, retry and fallback strategy, structured output validation, eval suites, prompt versioning, and the unsexy plumbing: caching, logging, redaction, and rate limiting. That's what turns a demo into a product.
What I build
Conversational interfaces
Intake widgets, support copilots, internal tools backed by Claude or Gemini. Streamed responses, tool use, structured data extraction, and graceful fallbacks.
AI content generation
Image and video generation via Google Imagen and Veo. Text generation with Claude and OpenAI. Templating, brand guardrails, human-in-the-loop review.
Embedded workflows
Classification, scoring, extraction, summarization wired into existing apps. JSON-mode outputs with Zod validation, retry logic, and observable cost.
RAG and search
Retrieval over your knowledge base using vector search (pgvector, Pinecone, Turbopuffer) plus a rerank step. Citations, freshness controls, and eval-driven iteration.
How I ship it
- Working prototype within 2 weeks to validate the user experience before commitment
- Structured outputs with schema validation. No "the model usually returns JSON"
- Eval suite for the critical user journeys, run in CI
- Cost dashboard from day one. You'll know per-feature spend, not just a Stripe invoice
- Prompt versioning and A/B testing on prompts treated like code
- PII redaction, audit logs, and content moderation where applicable
- Fallback to deterministic logic when the model is wrong, slow, or unavailable
- Documentation your team can ship without me
The AI stack I work with
Frequently asked
Which AI provider should I use?
For text reasoning and tool use, Claude is hard to beat right now. For multimodal and image generation, Google's Gemini and Imagen are the strongest options I've shipped to production. For video, Veo. For lowest cost on simple classification, often a smaller open-weights model. I'll usually recommend pairing Claude with a smaller model for fast inference paths.
How do you keep AI costs predictable?
Token accounting per feature, model routing (small model first, larger model on escalation), aggressive caching of deterministic prompts, and a cost ceiling that throttles or queues when exceeded. Most production systems I've shipped run at 1/3 the cost of the obvious "send everything to the biggest model" approach.
Can you integrate AI into our existing app?
Yes. That's most of the work, actually. I integrate into React/Next.js, Vue/Nuxt, Node, and serverless backends. New AI features become real components in your design system, not bolt-on chat bubbles.
What about data privacy and PII?
Provider choice matters here. Anthropic and OpenAI's enterprise tiers have explicit "no training on your data" terms, and Google's Vertex AI has similar guarantees. I help configure the right deployment (e.g. Bedrock for AWS, Vertex for GCP) and implement PII redaction before requests go to any external API.
Do you build agentic systems?
Yes, but with a strong bias toward making each step verifiable. Tool-using agents with structured tool schemas, deterministic state machines for critical flows, and human-in-the-loop checkpoints for irreversible actions. "Spawn an autonomous agent and pray" isn't on the menu.