SEO and marketing decision
AIRIX - AEO / AI Visibility Platform
A technical user can build a useful weekly-scanning workflow, but reproducing the hosted product’s ongoing engine coverage, scale, and continuous maintenance against changing AI platforms is operationally heavy, so full parity is unlikely without dedicated ops and monitoring.
Visit website↗$39.99/mo
$480/yr
Read off the official pricing page.
$100one-off80 h to build
$50/mo6 h/mo upkeep
On cash alone, building overtakes the subscription at 2 seats.
No open-source build does this yet
Nothing published replaces this one, so a replacement starts from an empty file. Here is what it would have to cover.
What a replacement has to do
- Run automated queries across multiple AI chat engines for a given business, collect & parse answers, compute per-engine visibility scores, generate remedial assets (llms.txt, schema, answer-shaped copy), and deliver a weekly brief/email.
What it still won’t have
- Scale, reliability and SLAs for weekly scans
- Continuous maintenance for new/changed AI engine UIs and proprietary APIs
- Proprietary monitoring, model-updated optimisations, and marketing/brand of the hosted product
- Multi-tenant billing, referral programs, and turnkey onboarding
What remains hard
- Execution quality
We build and maintain everything. You do nothing.
First-year cost
Keep paying
Paying is—cheaper in year one.
On cash alone, building overtakes the subscription at 2 seats.
Money you would actually spend
Time you would spend
—
What you would spend
What we assumed
The verdict above measures whether you could build it. This one is only about money.
Runnable build prompt
Build a self-hosted AIRIX-lite using Node.js + Playwright, Postgres, Next.js, and a hosted SMTP (SendGrid). Core features in scope: 1) scheduled weekly scan job that runs two prompts per configured AI engine (implement drives via Playwright to capture answers for engines without public APIs), 2) normalize and store raw answers and per-engine visibility results in Postgres, 3) extractor microservice (Node.js) that identifies company mentions and competitor names and computes a 0–100 weekly visibility score, 4) generator that produces llms.txt and answer-shaped copy using an LLM (OpenAI/other) for templating, 5) generate a weekly brief HTML and send via email, 6) a minimal Next.js dashboard to view last scan and trigger on-demand scans. Out of scope: multi-tenant billing, ads tracker, integrated competitor scanning across proprietary data sources, analytics beyond basic scoring. Require error handling, retries for scraping, tests for the extractor/score logic, and CI that deploys to a small VPS (DigitalOcean) with managed Postgres and a scheduled worker (e.g., systemd or cron).
How we checked
How the score was reached
- Partly verdict base52
- 2 cited sources+1
- Price verified on pricing page+3
- 3/3 assessment runs agreed+4
- Evidence score60
The base comes from the verdict. Everything under it is a check that either happened or did not, and each one is a fact frozen in this record rather than a judgement made at render time - so the same evidence always produces the same number.
How scoring works →Cited sources · 2
Every page the run actually retrieved.
- official productAIRIX home
- official pricingAIRIX pricing
Integrity checks
What held up, and what did not.
