SEO and marketing decision
Sight AI
A capable developer can build a useful subset (prompt tracking, LLM article generation, CMS publishing, Slack approvals) in a multi-week project, but reproducing Sight AI's polished multi-model visibility pipeline, prebuilt automations, activity memory, and full connector set is substantial and costly—keep paying for the full product unless you only need the narrow workflow.
Visit website↗Built by Grant Cooper, who ships 4 products in this index
$99/mo
$1,188/yr
Read off the official pricing page.
$100one-off160 h to build
$300/mo6 h/mo upkeep
On cash alone, building overtakes the subscription at 4 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
- Periodically check tracked prompts across search and major AI models, store visibility & sentiment, pick high-opportunity gaps, generate long-form articles with an LLM, publish to the customer's CMS, ping search engines for indexing, and report via Slack.
What it still won’t have
- Proprietary multi-model AI visibility pipelines and connectors maintained by Sight AI
- Built-in activity log, agent memory and tuned sub-agent orchestration
- Polished UX, templates, and pre-built automations
- Enterprise features and dedicated support/white-glove onboarding
What remains hard
- Brand trust
Supporting 500+ Brands and Agencies
First-year cost
Keep paying
Paying is—cheaper in year one.
On cash alone, building overtakes the subscription at 4 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 minimal Sight AI replacement: use Next.js + React for a dashboard, Postgres for storage, Node.js (Express) backend, and Docker. Integrate Google Search Console API for impressions/position and implement adapters to query one AI model provider (OpenAI) to generate article drafts and perform sentiment/position analysis. Implement: (1) scheduled prompt checks and normalization into Postgres, (2) ranking/opportunity scoring, (3) LLM-driven article generation with RAG against the site's content, (4) WordPress and Webflow publishing connectors, (5) IndexNow sitemap pinging, (6) a Slack bot for approval and notifications, (7) a basic activity log and user settings. Out of scope: support for multiple commercial LLM providers beyond OpenAI, enterprise onboarding, multi-tenant billing, and full multi-model scraping. Include error handling, retries, tests for core flows (prompt checks, publish, Slack approval), and Docker-compose deployment instructions.
How we checked
How the score was reached
- Partly verdict base52
- 3 cited sources+3
- Price verified on pricing page+3
- 3/3 assessment runs agreed+4
- Evidence score62
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 · 3
Every page the run actually retrieved.
- official productSight AI — official product
- official pricingSight AI Pricing
- official docsSight AI Agent features
Integrity checks
What held up, and what did not.
