Where these 3 reports land
Build it0
Scope it1
Keep it2
Category report
AI Search
3 products ranked by how much of their useful core an AI coding agent can reproduce: 0 strong builds, 1 scoped build, and 2 weak replacements.
How the score breaks down here
- Interface
- 31
- Core workflow
- 26
- Data access
- 6
- Operations
- 14
- Trust & safety
- 26
26/100 average buildability
Why people keep paying here
- proprietary data3 of 3 reports
The useful dataset is owned, accumulated, or expensive to reproduce.
- scale infra2 of 3 reports
Reliability at the vendor's scale is an operations problem, not a prompt.
- execution polish1 of 3 reports
The last 20 percent is sync, migration fidelity, speed, and edge cases.
The typical achievable core in this category: ground answers in sources you select and cite them visibly.
Scope it
Perplexity
A retrieval-plus-LLM answer engine is buildable, but Perplexity's search stack, source ranking, UX, mobile/browser surfaces, and model access make full parity hard.
Keep it
One Place
You can build a scheduled personal property monitor, but not One Place: the value is the maintained real-estate corpus, crawler fleet, AI extraction, deduplication, image/semantic search, geo enrichment, and constant source upkeep across millions of listings.
Keep it
Scite
The AI classification of a citation as supporting, contrasting or mentioning is a solvable NLP task, but the product's real value is the licensed full-text corpus across 40+ publishers plus preprint servers, kept current and cross-referenced at scale. No individual or small team can replicate that data-access moat; a DIY build only works on open-access papers, which is a small fraction of the literature that matters for a lot of fields.