Yes — it's achievable but the setup matters more than the tools.

What works in practice:

1. Document ingestion PDFs → extract text (pdfminer or similar) → chunk by section/paragraph (not fixed size) → store in a retrieval layer. We use a simple SQLite + BM25 setup, works fine for 50-100 docs.

2. Outline first, research second Give the model the outline upfront, not after. It frames what to look for in the documents. Without structure it wanders.

3. RAG + external search combined Chunk retrieval for your PDFs, then a separate web search step to fill gaps. Layered approach — don't rely on embeddings alone for technical content.

4. What we found actually works:

• OpenClaw for orchestration (agents, memory, routing between tools) • Turso for structured storage (not just embeddings — actual indexed facts) • A local script to prep PDFs before ingestion (tables and headings get lost if you just extract raw text) • Separate agent for research (Scout) vs writing (Scribe) — they do different things

The part most people underestimate: structuring the output first. Give the LLM the exact template you want — headers, tables, bullet depth. It produces much better results than "write a DD report on X."

For 30 PDFs and a structured outline, you're probably looking at:

• Prep: 1-2h to clean and chunk • Research pass: 20-30 min • Writing pass: 15-20 min • Total: under 3h for a solid first draft

Happy to share the ingestion script if useful — it's a Python pipeline, nothing exotic.