Hey everyone! 👋 I've been deep in the weeds with research tools for my sales forecasting and competitor analysis projects. For the last year, I was a pretty happy Mem.ai user for collating notes and sources. Recently, I decided to give Google's NotebookLM a serious trial run for a few weeks, thinking its AI-native approach would be a game-changer.
I really wanted to love it. The ability to instantly create study guides and summaries from my uploaded source PDFs (like market reports and sales call transcripts) felt magical at first. It's incredibly fast at answering specific questions based on the provided materials. For a focused, single-topic deep dive, it's powerful.
But here's why I ultimately switched back to my old workflow for now:
* **The "Notebook" constraint became a blocker.** In Mem, everything feels connected. In NotebookLM, I found myself creating a sprawling number of separate notebooks just to keep different client projects or research topics logically organized. It started to feel fragmented, and cross-referencing between notebooks wasn't as fluid.
* **Lack of true "workflow" integration.** My process involves live CRM data (Salesforce), dashboards (Tableau), and my notes. NotebookLM feels like a brilliant but isolated research chamber. I couldn't easily weave its outputs back into my active sales pipelines or growth documents without a lot of manual copying and pasting.
* **It's a fantastic *answer engine*, not a great *thinking space*.** For me, the initial synthesis phase is messy. I need to freely mix my own ideas, half-baked hypotheses, and sourced quotes. NotebookLM's focus on clean Q&A from sources inadvertently made my own creative process feel more rigid.
My takeaway? NotebookLM is an exceptional tool for **consuming and interrogating a defined set of source materials**. If your research is contained and you need rapid summaries, it's a 10/10. But for my dynamic, cross-functional work in revenue intelligence—where insights need to live and connect across CRM, analytics, and ongoing strategy—the walled-garden notebook model broke my flow.
Has anyone else run into this? Have you found clever ways to stitch NotebookLM into a broader sales or research workflow? I'd love to hear your hacks!
—Amy
I'm a data team lead at a mid-sized e-commerce company (around 150 people), and our core research and knowledge base stack currently runs on Obsidian paired with a handful of specialized AI tools for different tasks.
Your points about fragmentation really resonate. Based on your workflow, here's a breakdown of what I've seen and tested:
1. **Target Audience:** NotebookLM is an AI-first, individual researcher's tool. It's built for someone who works in a single thread of inquiry with a limited corpus of primary sources. Mem.ai is a knowledge network tool aimed at small teams who need to connect disparate ideas and have that information resurface. If your team is >1 person sharing research, Mem is already the better fit.
2. **Real Pricing:** NotebookLM is currently free, which is a huge advantage for a trial. The real cost is the lock-in and migration effort if you build a library there. Mem.ai is $10/user/month for the team plan, with a 30% discount on annual billing. The hidden cost for Mem is the optional AI add-on, which is an extra $8/user/month and needed for the most useful automatic note-linking features.
3. **Integration & Deployment:** Neither tool is strong here, but NotebookLM is a walled garden. Its only inputs are manual uploads or Google Docs. Mem.ai has more live hooks (Slack, email, and a decent API) which let you pipe in CRM notifications or meeting notes automatically. Getting data into NotebookLM remains a manual, per-notebook chore.
4. **Where It Breaks:** NotebookLM's limitation is exactly what you hit - it's a collection of isolated silos, not a connected brain. Cross-notebook search isn't a feature. For sales forecasting, you can't have it analyze relationships between a market report in one notebook and a transcript in another without manually merging sources. Mem's weakness is its AI can feel less "smart" on specific source Q&A; it's better at associative linking than precise citation.
For your case of sales forecasting and competitor analysis, which sounds like an ongoing process with many connected sources, I'd stick with Mem.ai for the core knowledge layer. The deciding factors would be your team size and how much you rely on automated data ingestion. If you're solo and do purely manual, project-based research, NotebookLM could work. Tell us if you work alone or with a team, and what percentage of your source material comes from automated feeds (like CRM exports) versus manual uploads.
Stay grounded, stay skeptical.