I Built an AI That Automates Literature Reviews — Here's How It Works Under the Hood

Source: Dev.to

If you’ve ever had to do a systematic literature review — the kind where you manually search databases, download dozens of PDFs, read each one, and paste findings into a spreadsheet — you know it’s one of the most brutal parts of academic research. It can take weeks or even months.

I built Research Room AI (https://researchroomai.com) to eliminate that pain. You type in a research topic, and the platform finds relevant papers, downloads the full‑text PDFs (open‑access only), reads them cover‑to‑cover with an LLM, and spits out a structured, exportable table of methodology, findings, and limitations.

What It Actually Does

The core user flow consists of four steps:

1. Define your topic – Enter your research subject and any constraints.
2. Secure full texts – The system identifies and downloads legal open‑access PDFs.
3. AI synthesis – An LLM reads each paper and extracts structured data.
4. Export & analyze – Results appear in a clean dashboard; you can download them as CSV.

The hard part isn’t any single step — it’s making all four work together reliably at scale.

The Tech Stack

• Frontend: Next.js 15 (App Router) + Tailwind CSS
• Worker / AI processing: Node.js workers, BullMQ queues, Redis, Groq LPU inference
• Database: PostgreSQL (flexible JSON blobs for per‑paper data)
• Billing: Paddle (handles global VAT/tax compliance)

The Hardest Problem: Finding and Downloading PDFs Reliably

Academic papers are scattered across hundreds of publishers, repositories, and paywalls. My approach:

1. Search APIs – Use OpenAlex and Semantic Scholar to retrieve papers matching the topic. These APIs return rich metadata, including DOIs and open‑access PDF URLs.
2. Multi‑source resolution – If the primary URL fails, fall back to Unpaywall, arXiv, PubMed Central, and institutional repositories.
3. Compliance guardrails – Only download PDFs explicitly flagged as open‑access; paywalled content is never fetched.

The PDF resolver service (worker/src/services/pdf-resolver.ts) handles retry logic, redirect chains, and content‑type validation. Many “PDF links” actually serve HTML error pages, so you must check MIME types after download, not before.

The Worker Architecture

The Next.js app and the AI processing worker are separate services:

• The frontend stays fast and responsive, merely enqueuing jobs.
• The worker can be scaled independently and redeployed without touching the frontend.
• Long‑running AI tasks (e.g., reading a 40‑page paper) don’t block HTTP request cycles.

Jobs flow through BullMQ queues backed by Redis. The worker picks up a job, downloads the PDF, sends the text to Groq for extraction, and writes structured results back to PostgreSQL.

Simplified processor flow

// worker/src/processor.ts
async function processJob(job) {
  // 1. Download PDF
  // 2. Extract text
  // 3. Send to Groq LPU for inference
  // 4. Store structured result in Postgres
}

Groq’s LPU inference is fast enough that users see results streaming in within a reasonable time, rather than waiting 20 minutes.

The Database Schema Challenge

Every literature review has a different set of columns. One researcher may need sample_size, study_design, country; another may want model_accuracy, dataset, limitations.

Solution: Store extracted fields as a flexible JSON blob alongside a set of review‑level column definitions that the user can configure. This provides relational integrity for project‑level data while keeping per‑paper results flexible.

The Subscription Model

Paddle handles global VAT/tax compliance out of the box, which would otherwise be a nightmare for a solo founder selling to universities worldwide.

Lessons Learned

• Decouple your AI work from your web server immediately.
• Academic APIs are inconsistent — build defensive parsers.
• Rate limiting is not optional.
• Full‑text extraction yields far higher quality than abstract‑only.
• For research/academic niches, Paddle often beats Stripe on compliance.

Try It

If you do any kind of research — academic, market, scientific — give Research Room AI a shot:

https://researchroomai.com

The free tier gives you three full literature reviews with no credit card required.

Feel free to ask questions about any part of the architecture in the comments. Building AI tooling for academia is an under‑explored niche with real pain to solve — the manual review process genuinely hasn’t changed since the 1990s.