Areopagus AI Engineering

Highlighting the work of Adam Higdon

Enterprise-grade AI, mid-market speed.

For $10M–$250M companies who need AI deployed to production — not slide decks promised. I build enterprise-grade applications at roughly 25% of the cost of a traditional agency engagement, shipped in weeks instead of quarters.

Book a 20-min call See live work

Systems shipped

Lines of production code

Projects you can click

Avg. to production

01 — Why Me

Operator. Enterprise practitioner. Builder.

Most solo AI people have one or two of these. This work wants all three.

Act 01 · The Operator

From the Dot-Com Boom to the AI Boom.

My career has spanned both — I started and ran my own digital agency in the early commercial-web era, doing SEO, SEM, and web development, carrying the P&L through the dot-com cycle. Same operator instincts now applied to AI.

Act 02 · The Enterprise Practitioner

Fifteen years in Fortune 500 rooms.

Spent the last decade and a half implementing, integrating, and selling enterprise software at Fortune 500 scale. That quality bar is what I carry into every build.

Act 03 · The Builder

100k+ lines of AI code, in 2026 alone.

Voice, SaaS, iOS, enterprise dashboards, multi-model deliberation, call intelligence. Velocity comes from operator instincts plus modern AI tooling — not a five-person team coordinating through a PM.

Cloudflare Workers·D1·Claude·GPT-5·Twilio·OpenAI Realtime·Stripe·Netlify·CallRail·Fly.io·Resend·Perplexity·Grok·Cloudflare Workers·D1·Claude·GPT-5·Twilio·OpenAI Realtime·Stripe·Netlify·CallRail·Fly.io·Resend·Perplexity·Grok·

02 — Case Study

One system. One client. Real numbers.

Legal MarketingProductionActive ClientCustomer Data Platform

Identity resolution and AI-powered intake across 230 law firms.

The Problem

The agency manages intake for 230 personal-injury and consumer law firms. Calls come in across 13 CallRail accounts. Contact forms from 230 firm websites piped into a single inbox. Their operations team was manually triaging thousands of submissions a week. Real leads were slipping through.

What I Built

A unified data platform stitching calls and forms to canonical firm identities across multiple source systems. Real-time webhooks from 13 CallRail accounts feed an identity resolution graph that maps numeric IDs, website domains, vendor slugs, and sender emails to a single canonical identifier per firm. AI enriches every record with paralegal-voice summaries, lead classification, and urgency scoring.

A deterministic routing engine auto-approves ~99% of submissions with zero manual triage. A multi-tenant denylist structurally prevents shared notification senders from misrouting leads across firms.

Calls analyzed

Contact forms processed

~99%

Auto-approved via identity graph

Firms served

Contract to production

95%

AI cost reduction

Cloudflare WorkersD1KVIdentity GraphWebhooksFly.ioGemini FlashCallRail APINetlify

Read the full engineering deep dive →

03 — Case Study

One platform. One engineer. Eight weeks.

GEO PlatformProductionSaaS

Generative Engine Optimization across 5 AI platforms.

Faneros AI Visibility dashboard — visibility scores across ChatGPT, Claude, Perplexity, Gemini, and Grok with GEO Readiness Score and competitor mention chart

Faneros Competitive Landscape Map — geocoded competitor pins on a Chicago downtown map with AI-generated competitor descriptions

The Problem

Generative Engine Optimization is a real category most agencies can't speak to. Buyer-intent queries increasingly get answered inside ChatGPT, Perplexity, and Gemini — users never click through. GA4 labels nearly all AI-referred traffic as "direct." There's no established playbook, and the existing tools each solve only one piece.

What I Built

A SaaS platform combining three previously-separate capabilities: AI visibility scanning across 5 platforms in parallel, AI traffic attribution that recovers ~90% of dark AI traffic, and a Bayesian marketing mix model treating AI as its own channel.

13 deliverable types generate per scan, routed deterministically across three frontier models and gated by a packageFacts validation contract before download.

Build time, idea to production

~40,000

Lines of production code

AI platforms scanned in parallel

Deliverables per scan, validated

~90%

AI traffic detection accuracy

~$1K/mo

Total infrastructure cost

Cloudflare WorkersKVR2Claude OpusGPT-5.4Gemini 3.1 ProSerpAPIBayesian MMMStripeNetlify

04 — Case Study

3,600 accounts. 38 sellers. One Monday morning.

Sales IntelligenceEnterprise SaaSActive Deployment

Account intelligence and territory planning for a Fortune 500 field sales organization.

Warroom command center showing 3,627 accounts on a US density map with filters and AI-powered command center panel

The Problem

A North American field sales team of 38 account executives, each managing roughly 80 accounts in their patch. Every Monday, every seller faces the same question: which five accounts to call this week, which to deprioritize, and what to actually say when they get on the phone. Salesforce can produce a list and a CSV. It can't answer those questions. Sellers were guessing.

What I Built

A single-operator platform that turns Monday-morning planning into minutes instead of hours. Three capability surfaces under one filter bar: territory hunting (drop a 15–50 mile radius around any city, see every account fully hydrated), one-click intelligence (executive briefings and internal sales plans generated in ~3 minutes from internal data plus live 10-K and 10-Q filings), and a manager-level coverage heat map for partner gaps and white-space planning.

Accounts under management

Field sellers using daily

~80

Accounts per AE

~3 min

Click to executive briefing

Core functionality stood up

National

US field coverage

Cloudflare WorkersKVAnthropic APIClaude Opus 4.6LeafletJavaScriptGitHub PagesWeb Search

05 — Proof You Can Touch

Six live projects. Click through and try them.

These are the actual products — running right now. Open a new tab, poke around.

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Faneros

SaaS that scans how five AI assistants see your business and generates visibility fixes. Stripe billing.

faneros.ai →

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Areopagus

Three frontier AI models debate your question through multiple rounds. Sequential juror chain, TTS, consensus scoring. On the App Store.

areopagus.ai →

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LunaPhone

Real-time voice AI receptionist. Healthcare and legal agents. Google Calendar booking, sub-500ms latency.

lunaphone.ai →

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Intake IQ

Call intelligence dashboard. 50 firms, 1,742 calls analyzed, AI-categorized lead types, conversion analytics.

intakeiq-ai.netlify.app →

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Warroom

Partner sales intelligence. 3,600+ accounts, interactive US map, AI Command Center, RAG over enterprise PDFs.

projectwarroom.netlify.app →

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AI Mix Modeler

Bayesian MMM with Hill curves, adstock, halo detection, efficient frontier. Runs entirely in-browser.

mix.faneros.ai →

One person. Six verticals. All live.

06 — Product Spotlights

Three products, shipped solo. Worth a closer look.

Spotlight 01 · Marketing Science

A Bayesian Marketing Mix Model for the generative engine era.

The Faneros AI Mix Modeler is a self-serve Bayesian MMM with Hill saturation curves, adstock decay, halo detection, an efficient-frontier optimizer, and a scenario planner.

Channels modeled

Minutes

Upload to model

~1%

Cost of traditional MMM

Open mix.faneros.ai →

Spotlight 02 · AI Visibility · Paying Customers

How the world's major AI assistants see your brand.

Faneros GEO scans five AI systems and produces thirteen ready-to-deploy deliverables. Two-pass knowledge-base architecture keeps contamination at zero.

AI systems scanned

Deliverables per scan

24/7

Scheduled scans

Open faneros.ai →

Spotlight 03 · Real-Time Voice AI

A voice receptionist that sounds like a person.

LunaPhone — Twilio → Cloudflare Workers → OpenAI Realtime API. Sub-500ms latency for healthcare and legal intake.

<500ms

Voice-to-voice latency

Verticals live

24/7

Replaces answering service

Open lunaphone.ai →

07 — What I Build

Six categories of systems. All in production.

AI intake & triage

Classify, score, and route submissions with dual-pass verification and learning trust lists.

Call intelligence

Transcription, enrichment, lead scoring, and conversion attribution across your phone system.

Custom dashboards

Role-based views, real-time metrics, drill-downs, AI-assisted search. Your domain, your brand.

Voice AI agents

Real-time conversational agents for inbound calls, booking, and qualification. Sub-500ms.

Classifier pipelines

Dual-pass LLM classification with verification, confidence scoring, and human-in-the-loop fallback.

AI visibility (GEO)

How do today's major AI assistants see your brand? Scan all five and get ready-to-deploy fixes.

08 — How I Work

No slide decks. Just working software.

Step 01

Discovery call

Twenty minutes. You describe the problem. I tell you on the call whether I can ship in two weeks.

20 minutes · no cost · no slides

Step 02

Two-week pilot

Fixed fee. Fixed scope. Working prototype in your team's hands by day ten.

Fixed fee · 2 weeks · no strings

Step 03

Production deploy

Harden the prototype, deploy to production. Billing starts the day your team logs in.

1 week · bundled into retainer

Step 04

Monthly retainer

Hosting, AI costs, monitoring, improvements. Full source access. Thirty-day exit.

Month-to-month · the code is yours

If you don't see a working prototype by the end of the pilot, you don't pay a cent beyond the setup fee.

09 — Pricing

Transparent terms.

One-Time

Setup & Pilot

Fixed fee · scoped on the discovery call

Discovery & scope definition
Two-week build to working prototype
Production deployment & handoff
Infrastructure & domain setup
30-day post-launch monitoring

Recurring

Monthly Retainer

Month-to-month · 30-day exit · code is yours

Hosting & infrastructure
AI model costs (LLM inference, embeddings)
Uptime monitoring & incident response
Continuous improvements from your usage
Direct line for feature requests

Enterprise

Scoped Individually

Annual engagements · multi-system builds

Multi-system architecture
Custom SLA & uptime guarantees
On-prem or customer-cloud deployment
Dedicated capacity (not shared)
Quarterly business reviews

10 — Questions I Actually Get Asked

The things you're probably thinking.

Why one person instead of a team?

Because the work that matters — deciding what to build, building it well, shipping it fast — is better done by one experienced operator with modern AI tooling than by five people coordinating through a project manager. Most software failures are coordination failures.

What happens if you get hit by a bus?

Every engagement includes source-code escrow: all code in a private repo you have read access to, infrastructure credentials documented in a handoff runbook. Any competent AI engineer can pick it up.

How is this different from an agency?

Agencies bill by headcount and staff with whoever is on the bench. They take a quarter to ship what one person can ship in a fortnight. I don't have that overhead. Neither do you.

Do you work with my stack?

Probably. Preferred: Cloudflare Workers + D1 + frontier LLM. Also shipped on AWS, Azure, GCP. I integrate with Salesforce, HubSpot, Twilio, CallRail, Slack, Gmail, and most major APIs.

What about data & security?

Data stays in your region. No model training on your data. SOC 2 Type II compliant upstream providers. On-prem available at Enterprise tier. BAAs for healthcare.

Can I talk to a current client?

Yes. Live reference at the legal marketing agency featured in the case study. They'll answer honest questions. Book a call and I'll make the intro under mutual NDA.

Enterprise-grade AI, mid-market speed.

Operator. Enterprise practitioner. Builder.

From the Dot-Com Boom to the AI Boom.

Fifteen years in Fortune 500 rooms.

100k+ lines of AI code, in 2026 alone.

One system. One client. Real numbers.

Identity resolution and AI-powered intake across 230 law firms.

The Problem

What I Built

One platform. One engineer. Eight weeks.

Generative Engine Optimization across 5 AI platforms.

The Problem

What I Built

3,600 accounts. 38 sellers. One Monday morning.

Account intelligence and territory planning for a Fortune 500 field sales organization.

The Problem

What I Built

Six live projects. Click through and try them.

Faneros

Areopagus

LunaPhone

Intake IQ

Warroom

AI Mix Modeler

Three products, shipped solo. Worth a closer look.

A Bayesian Marketing Mix Model for the generative engine era.

How the world's major AI assistants see your brand.

A voice receptionist that sounds like a person.

Six categories of systems. All in production.

AI intake & triage

Call intelligence

Custom dashboards

Voice AI agents

Classifier pipelines

AI visibility (GEO)

No slide decks. Just working software.

Discovery call

Two-week pilot

Production deploy

Monthly retainer

Transparent terms.

Setup & Pilot

Monthly Retainer

Scoped Individually

The things you're probably thinking.

Find one person who has built this diversity of work — and I'll pay them.

Let's talk for twenty minutes.