About | AI Adoption Agent

1. What this is

The AI Adoption Agent is a working prototype that automates one specific piece of B2B sales intelligence: finding companies, estimating their AI readiness from public signals, and producing a draft first-touch email calibrated to that readiness.

It is built as an application artifact — a proof of concept demonstrating autonomous agent architecture in a context directly relevant to GO MO Group's existing work with AI-led B2B marketing.

2. Pipeline

1
Search — Serper.dev queries Google for companies matching the ICP description. Social media, news aggregators, and marketplaces are filtered out.
2
Scrape — Homepage and About page content is fetched via cURL. Text is extracted (scripts, nav, footer stripped). Truncated to 5,000 characters per page.
3
Classify — Claude Haiku assigns a 1–5 AI maturity score with rationale and explicit signals. It never invents signals — if the text doesn't support a higher score, it doesn't get one.
4
Outreach — Claude Sonnet drafts a segment-calibrated email. Score 1–2 gets an education angle. Score 3 gets a pilot angle. Score 4 gets an optimization angle. Score 5 gets a skip note.

3. AI Maturity Rubric

1

Dormant No public AI signals at all.

2

Curious Leadership mentions AI in blog or interviews. No implementation.

3

Experimenting Pilots, individual AI tools mentioned. No integrated systems.

4

Implementing Dedicated AI roles, integrated workflows visible publicly.

5

Native AI is core to the product or operations, not just mentioned.

4. Why this was built

GO MO Group has published detailed work on AI-led B2B marketing — including a deployed GenAI Content Supply Chain for a global manufacturer and a productised Generative Engine Optimisation (GEO) service. The ASBX process and Pull Marketing framework show a systematic approach to AI-augmented sales.

This prototype demonstrates the specific capability gap that comes before that framework kicks in: automatically identifying which prospects are ready for what conversation. An AI maturity classifier at the top of the funnel makes the downstream content personalisation (which GO MO already does well) dramatically more precise.

It is built to live under gomogroup.com, not as a standalone product. This is an input into an existing system, not a replacement for it.

5. Known Limitations

▪Public signals only. Classifications are based on what companies publish on their website. A company doing significant internal AI work without publicising it will score low. This is a known bias.
▪Phase 1 sample. The Heatmap covers 27 hand-seeded Västsverige companies, not a statistically representative sample of the region.
▪Text only. The scraper reads HTML text. It does not process video, PDFs, LinkedIn, press releases, or job postings — all of which would improve signal quality.
▪One classification pass. Scores are not averaged across multiple runs. A single scrape on a day when a page is cached or down could affect the result.
▪No send functionality. Outreach drafts are copy-to-clipboard only. This is intentional — review and personalisation by a human is required before any contact.
▪Shared hosting constraints. The live agent is limited to ~90 seconds and ~8 companies per run due to Oderland's execution limits.

6. Tech stack

Server

PHP 8.2 on Oderland shared hosting

Database

SQLite 3 (single-file, zero config)

Classification

Claude Haiku (claude-haiku-4-5-20251001)

Outreach

Claude Sonnet (claude-sonnet-4-6)

Search

Serper.dev (Google SERP API)

Map

Leaflet.js + OpenStreetMap

CSS

Tailwind CDN (no build pipeline)

JS

Vanilla — no framework

7. Contact

Built by Andreas Persson.
Questions about this prototype: andreas@perssonofsweden.com