AI assistants and search decision
Seashore AI
A competent technical user can build a useful narrow replacement (chat+RAG) in about a week, but with no product details available it's unclear what full features or proprietary data the paid product contains, so a complete replacement may not be realistic.
Visit website↗Not priced
No pricing page we fetched carried a figure, so there is nothing to compare against. The build side is still real.
$100one-off38 h to build
$100/mo4 h/mo upkeep
No published price to break even against.
Open-source builds that already do this
Every project below is open source and already does this job today. Fork one, self-host it, or take the parts you need - the build prompt further down assumes an empty file, and this is the shortcut past that. Licences differ; check the one on each card before you ship. All Seashore AI alternatives, with the arithmetic →
What a replacement has to do
- User submits text or file -> backend indexes or forwards to model -> model returns answer -> UI displays and logs the interaction.
What it still won’t have
- Brand, existing users, and reputation
- Any proprietary models, data, or integrations the vendor offers
- SLA-backed support, polished UX, and ongoing product roadmaps
What remains hard
- Product polish and ongoing maintenance
First-year cost
No published price
Seashore AI does not publish a price we could read, so there is nothing to compare against. What building costs is below.
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 self-hosted AI chat assistant using Next.js for the frontend, Node.js + Express for the backend, Postgres for metadata, and a hosted vector DB (e.g., Pinecone) for retrieval. Core features: user signup (email/password), chat UI with message history and file upload, ingestion pipeline that chunks uploads and stores embeddings, request routing that queries the vector DB and sends context plus user prompt to an LLM via OpenAI-compatible API, and session/transcript storage. Out of scope: multi-tenant billing, advanced analytics dashboard, custom model training. Include error handling, input validation, retries for transient API failures, and automated tests for the ingestion pipeline and API endpoints.
How we checked
How the score was reached
- Partly verdict base52
- An open-source build was found+5
- 3 cited sources+3
- 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 · 3
Every page the run actually retrieved.
- official productSeashore AI
- open sourcearc53/DocsGPT
- open sourceChatGPTNextWeb/NextChat
Integrity checks
What held up, and what did not.
