Work

AI systems already doing the work

Agents, voice assistants and automation built for real businesses, with the numbers behind each one. Live is in daily use, Pilot is with a first client, Tested has passed a written test plan, and Delivered is a finished analysis or plan. Client names are withheld.

  • Live / Shipped
  • Pilot
  • Tested
  • Delivered

Case studies

Eight systems, each with how it works, what it took and the measured result.

LiveAI agents · Marketing agency · 2026

Multi-agent client intelligence platform

One structured, sourced memory of every client, shared by 33 AI agents and reachable from Claude through an MCP server.

Websitecrawled pagesReviews & listingsGoogle, Yelp, BBBPublic recordslicenses, WHOISClient Mind26-section profile33 AI agentscontent, ads, SEOMCP serverClaude reads it
Public sources feed one 26-section client profile, which 33 agents and an MCP server read.
33specialized AI agents
39client profiles in production
8 toolsin the MCP server
~$32–42per month to operate

The problem

Every AI output at the agency started from a blank prompt. Client facts lived in people's heads and drifted, so content came back generic or wrong.

Results

Commercial alternatives run $49–249 per user per month; this runs the whole agency for about $32–42. Its QA engine has generated 1,504 field-level suggestions for human review.

What I built

  • A 26-section client schema with field-level provenance
  • 33 agents reading from that shared memory
  • A prompt library with versioning: 92 prompts, 94 tracked versions
  • A document layer with embeddings, and a CRM integration that has cataloged 2,590 assets
  • An MCP server with token auth and audit logging

Stack

  • TypeScript
  • Supabase / Postgres
  • Claude API (tiered model routing)
  • MCP
TestedAI agents · CRM · Marketing agency · 2026

Agents that build: CRM construction from a spec with Claude & Codex

AI coding agents built a marketing agency's CRM systems directly inside the platform from written specifications, verified at every step.

Written specevery objectClaude / Codexbuild in the appCRM test locationworkflows, fields, KBAPI readbackverifies each objectGap reportwhat still needs work
A written spec is built by Claude or Codex inside a CRM test location, checked by API readback, and gaps loop back to the spec.
3.5 hrsunattended Codex build run
351custom values built in two days
10workflows built by Claude from spec
<1 minfirst lead response in testing

The problem

Each CRM template (pipelines, workflows, fields, emails, knowledge base, pages) took contract builders weeks at about $500 per template, and quality varied.

Results

A two-day build produced 351 custom values, 20 tags, 21 fields, a 12-stage pipeline, 2 calendars, a 52-entry knowledge base, 11 workflow shells, 28 SMS snippets and 4 pages. The agency put contractor hiring on hold.

What I learned

The platform's own AI created data objects but not workflows, so the agents worked in the workflow builder instead. The specification and the gap report are the real product.

What I built

  • A specification format that defines every CRM object
  • A build method where Claude or Codex constructs those objects in the live platform
  • API readback and a gap report after each phase
  • Hard boundaries: test locations only, repeated location checks, no live SMS until a compliance-safe number exists
  • A QA skill that enrolls test contacts across workflows (23 enrollments across 16 workflows)

Stack

  • Claude Code
  • OpenAI Codex
  • GoHighLevel API v2
  • Python
TestedVoice AI · Multi-location retailer · 2026

Inbound voice AI agent family

Phone agents that answer, qualify, book and hand off, tested against 50+ cases before any client demo.

Inbound callVoice agent6 service branchesBook showroom visitTransfer with summaryTake a messageFollow-up text
An inbound call reaches the voice agent, which books, transfers, takes a message or sends a follow-up text.
11 / 11first test block passed
50+test cases plus a stop-ship regression
6service branches with matched follow-up texts
5phone agents and a website voice pilot

The problem

A retailer with several showrooms was missing calls and wanted every caller qualified and routed, without an AI that quotes prices or gives risky advice.

Results

Showroom booking verified on a live call. Defects found in testing, including an emergency response that could be interrupted, were fixed before the client heard the agent. Pricing proposed as a launch fee, a prepaid build and monthly maintenance, with usage at cost.

What I built

  • An agent that books appointments, takes messages and transfers with a spoken summary
  • A prequalifying flow: full questions for shoppers, two at most for service calls, immediate transfer on request
  • Consent-gated follow-up texts sent two minutes after the call, matched to the caller's need
  • Safety rules: no prices, chemical advice or promised dates; the 24-hour number on safety issues

Stack

  • GoHighLevel Voice AI
  • Conversation AI
  • CRM workflows
LiveReporting · Marketing agency · 2026

Account health dashboard & alert bridge

Every client CRM account checked nightly; problems become tasks in the tool account managers already use.

Every clientCRM accountNightly engine18 health flagsTasks in the CRMHealth dashboardMonday digest
Every client CRM account is read nightly by a read-only engine with 18 flags, producing CRM tasks, a dashboard and a Monday digest.
18health flags per account
Nightlyread-only check of every account
9,600open deals analyzed across 27 accounts
~1 weekfrom start to live

The problem

Account managers checked client accounts by hand. Silent forms, waiting leads and stalled deals surfaced only when a client complained.

Results

The first portfolio read found 81% of 9,600 open deals idle for over 30 days. The fix was three habits (closing deals out, marking losses, clearing dead deals), not a pipeline redesign.

What I built

  • A Python engine that reads every account through read-only tokens into Supabase
  • Flags compared against each account's own baseline and against peers
  • A severity-sorted view by account manager with a one-click weekly client update
  • An alert bridge that creates CRM tasks and emails for silent forms, dropped lead sources and stalled pipelines
  • A Monday digest: every open issue per client in one email

Stack

  • Python
  • Supabase
  • GoHighLevel API
  • HTML email
PilotCRM automation · AI agents · 2026

Research-then-verify client onboarding

AI researches a new client before kickoff, and the client corrects a pre-filled profile instead of filling out a long questionnaire.

New clientin the CRMWebhookfires the agentClaude researchsite, records, reviewsReport in the CRMevery field labeledClient verifiesand e-signsAccount buildsautomatically
A new client triggers a webhook, a Claude agent researches the business, the report returns to the CRM, the client verifies it, and the account builds itself.
26sections in each AI research profile
<20 minunattended research run
74fields covered by an automated save test
7tabs in the client onboarding app

The problem

Onboarding asked clients for hundreds of fields across long forms. Clients stalled, data arrived incomplete and launches slipped.

Results

In pilot with the agency team. Three client pilots must pass a written checklist before any new features are added.

What I built

  • A CRM workflow that fires a webhook; a Claude agent researches the business and posts a report back into the CRM
  • Research that works without a website, using license records, permits and directories
  • Stop-and-notify when a listed site is broken or belongs to another company
  • A confidence label on every field (verified, derived or needs input), and a client review and e-sign flow
  • Automatic sub-account build when onboarding completes

Stack

  • Claude Routines
  • Firecrawl
  • GoHighLevel API & webhooks
  • Supabase Edge Functions
LiveAI agents · Enablement · 2026

104-skill AI library & orchestration pipeline

An agency's AI work packaged as versioned, searchable skills, plus a one-prompt pipeline that runs nine of them in a gated sequence.

Buildsource docValidatego / no-goFreezechecksummedSkillSkillSkill17 deliverables
One source document is built, validated and frozen, then parallel skills read it to produce 17 deliverables.
104packaged skills across 12 categories
9skills in one gated pipeline
17deliverables per pipeline run
~$4–8measured cost per run

The problem

Good prompts lived in individual chats. When one early run scaled 43 outputs from an unchecked source, 22 of them had to be regenerated.

Results

The library is published as a searchable site with team and priority filters, so each team starts from an adopt-first list.

What I built

  • A versioned skill plugin with a manifest for team rollout
  • A pipeline that builds a source document, validates and freezes it, then runs parallel steps from it
  • Gates before scale: a validator, a graded voice sample and a capacity check
  • A quarterly library audit for duplicates and retirement

Stack

  • Claude skills
  • Claude Code
  • Firecrawl
  • Netlify
ShippedWeb & search · Healthcare IT consultancy · 2026

30-day website launch for a healthcare IT consultancy

Strategy to launch in 30 days as a solo engagement, with accessibility and AI-search groundwork built in.

Design & buildJun 5–25Content & QAJun 25–Jul 3LaunchJul 4–5, one evening1,000+ organic clicks / 28 dayswithin four weeks of launch
Design and build, content and QA, and a one-evening launch led to 1,000 organic clicks per 28 days within four weeks.
30 daysconcept to live
20+pages designed and built
WCAG AAaudited before launch
1,000+organic clicks per 28 days within 4 weeks of launch

The problem

The firm needed a site that matched its reputation, on a fixed budget of about 60 hours, without losing the search traffic of the old site.

Results

Live in one evening via DNS cutover. Organic clicks rose 33% from the launch month to the following month.

What I built

  • A hand-coded WordPress theme with no page builders and sub-second page loads
  • A redirect map, launch runbook and parallel build with instant rollback
  • Forms with file uploads, consent gating and invisible anti-spam
  • llms.txt, FAQ patterns and citable claims for AI search
  • An AI automation roadmap for the firm's leadership

Stack

  • WordPress (PHP)
  • HTML/CSS/JS
  • Cloudflare Turnstile
  • Search Console

More work

Supporting builds, products and analyses.

TestedCRM automation

Speed-to-lead engine

Ten workflows that catch every lead, check business hours and compliance, text first with email fallback and watch response times.

First clean test: one text and one email in under a minute

LiveVoice & chat AI

Three-bot chat system with shared knowledge bases

Chat assistants for three brands drawing on six modular knowledge bases with clean scope separation.

163 FAQs · all bots validated above 85% accuracy

LiveWeb & search

AI visibility audit products

Four audits, including an LLM visibility audit that queries ChatGPT, Gemini and Claude with web grounding and scores visibility 0–100.

Branded HTML reports from one shared standard

TestedReporting & analytics

Automated monthly client report

A Looker Studio dashboard goes in; an internal report, a client-facing report and a pre-filled meeting doc come out.

Google Ads, Meta, Analytics and Business Profile in one run

TestedCRM automation

Website asset loader

Pulls a client's logo, colors, fonts and photos into an organized folder, then bulk-loads them into the CRM media library.

About 350 images loaded in 8 minutes

LiveAI agents

Fact-locked AI content

Diagnosed why in-tool AI copy drifted from verified client facts, then built a context block that every research run now produces.

Unverified items excluded by rule

DeliveredAI agents

AI automation roadmap

A phased automation roadmap and a CRM and HR-system integration readout for a healthcare IT consultancy's leadership and finance teams.

Paired with the 30-day website launch

ShippedProducts

SkillEngine

An AI productivity platform that saves your best prompts as reusable skills, with Claude, Gemini and OpenAI behind one interface.

277 skills · 48 pages · built in 31 days · Open the app

Media mix model executive dashboard showing ROI and channel contribution
ShippedProducts

Media mix model platform

Bayesian media mix modeling with data connectors, data-quality checks and budget scenario planning.

Adstock and saturation curves · scenario comparison

Lab

Smaller builds that show range.

  • Client-side security scanner15 security checks, 12 PII types detected, scored A–F
  • AI governance platform55 pages built in under a week
  • B2B CRM with prospect portalsMulti-contact pipeline, AI research assistant, ROI calculator

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