AI assistants and search decision

Lateral

A small team can reproduce the core searchable research workspace and sharing features, but the full product value (community, funding/connectivity, and any proprietary integrations) is lost and the original offering has been sunset.

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You pay

Not priced

No pricing page we fetched carried a figure, so there is nothing to compare against. The build side is still real.

You’d pay instead

$100one-off80 h to build

$100/mo8 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 Lateral alternatives, with the arithmetic →

What a replacement has to do

  • Index research artifacts, provide searchable/semantic lookup, create and view researcher profiles/projects, share links or notes between users.

What it still won’t have

  • Hosted managed service (accounts, uptime, backups)
  • Any user base, network effects, or community built on the original product
  • Undisclosed proprietary integrations or dataset access the company may have provided

What remains hard

  • Product polish and ongoing maintenance
Read the build prompt

First-year cost

No published price

Lateral 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

Keep paying
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Subscription price × seats × 12

Build it
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AI build —APIs + hosting —

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

Not run yet
Build a minimal open-source research collaboration web app using Postgres, Next.js, and a vector store (PGVector). Core features: ingest PDFs/URLs with metadata, extract text, create embeddings via a configurable embeddings API, store embeddings in PGVector, implement a semantic search endpoint with snippet extraction, basic user auth and project/item CRUD, and a simple React UI for search and project pages. Out of scope: payments, advanced funding workflows, large-scale ingestion pipelines. Include error handling, retries for API calls, and unit/integration tests for ingestion, search, and APIs.
How we checked3 sources · 2/3 runs agreed · evidence score 60

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.

Integrity checks

What held up, and what did not.

! 2 of 3 runs agreed; the verdict is the majority✓ Citations limited to fetched pages! 1 moat recorded