# How to Vibe Code Your Own Lateral (and Stop Paying for It)

> AI-powered research workspace and semantic document analysis platform

- Site: https://lateral.io
- Category: Research & Knowledge Management SaaS
- Verdict: **Solid side project** (75/100 vibecodeable)
- Estimated effort: 2-3 weekends of part-time work

## Verdict

Build a personal research workspace in 2 to 3 weekends using modern AI coding tools, Next.js, and pgvector.

Lateral sunset in 2025 because commodity LLMs and modern vector databases made proprietary custom embeddings obsolete for a solo builder. Today, you can assemble an identical personal literature review workspace by combining Next.js, a serverless Postgres database with pgvector, and the Anthropic API. The hardest part is not the AI or embeddings—it is writing robust PDF text extraction logic that handles multi-column academic layouts without scrambling paragraphs.

### What you can't replicate

- The original proprietary hyperbolic space word embeddings
- The historical user network and academic institutional trust

## What it does

Lateral allowed researchers, academics, and analysts to ingest collections of PDFs, read and annotate them, and conduct cross-document semantic searches to synthesize literature reviews and extract insights quickly.

### Core features

- PDF ingestion and text/layout extraction
- Document chunking and local vector embedding generation
- Cross-document semantic vector search ('Super Search')
- Interactive split-pane reader and PDF viewer
- Matrix-style synthesis board for organizing snippets side-by-side
- LLM-powered literature review summarization and Q&A

## The business

### Pricing

- Free Tier: Free — Basic document ingestion and limited semantic search.
- Pro Research: $29/mo — Advanced cross-document Super Search and synthesis.

Founded 2013.
Team size: 10-50.

## The hard parts

- Extracting clean text and layout hierarchies from multi-column academic PDFs without scrambling reading order
- Building a reactive split-pane interface for cross-document snippet comparison and tagging
- Managing robust vector chunk indexing and similarity retrieval performance across large PDF libraries

## How to vibe code Lateral

### Prerequisites

- Node.js (free): Required for running the Next.js development environment.
- GitHub (free): Source control and deployment pipeline integration.
- Anthropic API Key (Pay-as-you-go (~$5-10/mo)): Powers document summarization and Super Search reasoning.

### Recommended AI tools

- Claude Code: Best-in-class terminal agent for scaffolding the full-stack Next.js research workspace end-to-end.
- Cursor: Ideal for iterative UI work on the split-pane reader and matrix synthesis view.

### Stack

- Frontend: Next.js with Tailwind CSS and shadcn/ui
- Backend: Next.js App Router API routes
- Database: Neon (Serverless Postgres with pgvector)
- Auth: better-auth
- Payments: none
- Other: Vercel AI SDK, pdf-parse or unpdf for document extraction

### Hosting

- Vercel (Hosting the Next.js frontend and serverless API routes on the Hobby tier.): $0/mo
- Neon (Storing relational data, projects, chunks, and vector embeddings using pgvector.): $0/mo

### Build guide

1. **Project Scaffolding & Database Schema** — Initialize the Next.js project with Tailwind CSS, shadcn/ui, and configure the Neon database connection with pgvector extension.

```
Scaffold a new Next.js App Router project configured with TypeScript, Tailwind CSS, and standard directory structure. Set up Drizzle ORM connecting to a Neon PostgreSQL database with the pgvector extension enabled. Create database schemas for users, projects, documents (storing title, author, raw text, and file path), and document_chunks (storing chunk text, page number, and vector embedding column using 1536 dimensions). Configure better-auth for local session management. Verify the database migration runs cleanly against Neon.
```

2. **PDF Ingestion & Text Chunking Pipeline** — Implement server-side PDF upload handling, text extraction, and vector embedding generation using OpenAI or Anthropic embedding models.

```
Create a server action and API endpoint in Next.js that accepts multi-page academic PDF uploads. Use an extraction library like 'unpdf' or 'pdf-parse' to extract clean text while preserving paragraph structures and page numbers. Implement a chunking utility that splits document text into 500-token overlapping chunks. For each chunk, generate embeddings via an API call and insert them into the document_chunks table alongside the document ID and page metadata. Handle parsing errors gracefully and return processing status to the frontend.
```

3. **Split-Pane PDF Reader & Annotation UI** — Build an interactive document view with a split-pane layout for reading PDFs and capturing highlighted snippets.

```
Build a responsive split-pane research dashboard using React and Tailwind CSS. The left pane should display a list of ingested PDFs and project collections, while the right pane renders an interactive PDF viewer with text selection capabilities. Implement user annotation features allowing researchers to highlight text snippets, tag them with custom labels, and save them to a side-panel scratchpad associated with the active project. Store annotations in a dedicated database table linked to document chunk references.
```

4. **Cross-Document 'Super Search' & RAG** — Implement semantic vector search across all ingested documents within a project to surface relevant paragraphs instantly.

```
Implement the 'Super Search' feature using Neon's pgvector similarity search. Create an API endpoint that converts a natural language research query into an embedding, queries the document_chunks table using cosine distance (`<=>`), and returns the top matching snippets with source document titles and page numbers. Build a dedicated search modal in the Next.js frontend that displays these semantic matches with highlighted keywords and allows users to inject retrieved snippets directly into their synthesis notes.
```

5. **Matrix Synthesis Board & LLM Summarization** — Build a matrix-style comparison board where users can view extracted snippets side-by-side and generate synthesized summaries.

```
Develop a matrix-style synthesis board component in React where researchers can compare saved snippets from multiple PDFs side-by-side across custom thematic columns. Integrate the Vercel AI SDK to add an 'AI Synthesize' button that sends the selected row or column snippets to Claude Sonnet via API, generating a coherent literature review summary paragraph with inline citations pointing back to the original documents. Ensure the UI supports smooth drag-and-drop or grid reordering for smooth desktop usage.
```

### Cost vs paying

**Starting costs (one-time):**

- Domain name (optional): $12/yr
- Total: ~$12 one-time

**Ongoing costs (monthly):**

- Neon Database & Vercel Hosting (Hobby Tiers): $0/mo
- Anthropic API Usage for Embeddings & Super Search: ~$5/mo
- Total: ~$5/mo

- Paying for the SaaS instead: $29/mo (Historical)
- Build time: 16-24 hours
- AI tool credits: $20 (Claude Pro)
- Break-even: N/A (Product is defunct/sunset; built for personal use and learning)

## Sources

- [Lateral Official Website & Sunset Notice](https://lateral.io)
- [Tracxn Company Profile - Lateral GmbH](https://tracxn.com)
- [YourAIFinder - Lateral Platform Overview](https://youraifinder.com)