SERVICES

How I work.
Named outcomes, no theater.

Mostly hired in-house. Occasionally available through Toptal for engagements that need a Marketing Engineer — the hybrid between AI, web, and the marketing/product surface.

CORE

AI tooling that survives the prompt-to-production gap

Most AI projects die on the way from notebook to running system. I build the in-between — agent pipelines, internal tools, AEO-ready surfaces — in the same stack the rest of the team can run.

Agent Pipelines

Ingest → classify → summarize → brief. Durable storage, observable cost, no vendor lock-in.

Model Routing

OpenRouter + Helicone. Right model per task, real cost tracking, no model loyalty.

Answer Engine Optimization

Structured data, question-first content, entity authority. Get cited, not just ranked.

Internal Tools

Dashboards, briefs, content-ops loops that marketing/product own without engineering tickets.

Workflow Automation with AI

n8n, HubSpot, Power Automate, Chrome extensions — with LLM steps where they earn their keep. Versioned flows, not duct tape.

Architecture Reviews with AI

AI-accelerated audit, ranked next steps. No 60-page deck — a doc your team will actually open.

LLM Pipelines & Agent Prototypes

The hiring artifact, built as a service: ingestion → classify → summarize → ship.

Production agent pipelines: local-first or cloud, with durable storage and observable cost
Model routing via OpenRouter; cost and latency tracked via Helicone
Storage on Turso, Postgres, or pgvector; cron on Railway or your existing infra
Prompt management with versioning, evals, and team-shareable libraries
Same shape I'm shipping in CIA — production-grade, not a demo notebook

Answer Engine Optimization

Beyond SEO. Get cited by ChatGPT, Perplexity, and Gemini — not just ranked by Google.

JSON-LD schema (ProfessionalService, FAQPage, Article) across templates
Question-focused headings and answer-paragraph patterns that AI parses cleanly
Entity and authority establishment — the bits AI uses to decide whether to cite you
AEO audits with concrete next steps, ranked by effort and impact
Implementation directly in HubSpot CMS, WordPress, or your custom Next.js stack

Marketing Engineering

The hybrid role most companies are trying to hire for and can't quite name.

HubSpot CMS templates, modules, HubL, HubDB — at the level a real HubSpot dev ships
WordPress full-stack: themes, blocks, ACF, performance, accessibility
Internal dashboards and tools that marketing/product own without an eng ticket
Integrations: HubSpot ↔ n8n ↔ APIs ↔ your custom systems
Conversion-focused web work without the consultant theater

Embedded Engineering with AI

Drop in for a sprint or a quarter. Ship like a team member — with AI velocity baked in, not bolted on.

Joins your standups, code review, and Slack — no account-manager middle layer
Production code review, security, and accessibility defaults from day one
Documentation that survives team turnover (and is actually written down)
AI-assisted velocity with hardening — prototype fast, ship to production-grade
Best when the role is new, the title is fuzzy, or the team needs an AI-native voice

How I work by default

Not add-ons. Just the defaults.

Direct, no middle layer

You work with me — not an account manager. Decisions in hours, not next-week's-call.

Enterprise defaults

Strict code review, security-first, WCAG, semantic HTML, type-safety. Same standards Cognigy and Regiondo expected.

Documentation that ships

Handover docs your team will actually open. Built so I'm not the single point of failure.

LET'S TALK

Hiring? Or stuck mid-build?

Open to senior in-house roles with conversational AI / voice agent / AI-native DXP teams. Available for select Toptal engagements. I reply within 24 hours with something concrete — not a calendar link to a discovery deck.

→ Hiring conversations: tell me about the role and the stack

→ Project work: tell me the bottleneck, not the requirements doc

→ Either way, I'll come back with a take, not a pitch