Launched this week
FlowGrid
Private, AI-powered CRM that builds itself from your data
52 followers
Private, AI-powered CRM that builds itself from your data
52 followers
The privacy-first CRM that adapts to you. FlowGrid is built around three principles: privacy-first by default, minimalism over bloat, and AI features that actually do work instead of demo tricks. You bring your data, FlowGrid adapts to it, and then gets out of your way. Key features: Import spreadsheets → AI scaffolds your workspace instantly Natural language queries (Nexus AI) Field-level encryption & privacy controls Visual pipelines Voice-to-text transcription for hands-free prompting
FlowGrid
Hey Product Hunt 👏🏿 I’m Sam, the builder behind FlowGrid.
I built FlowGrid because most CRMs feel like they were designed for a world before AI, and they’re starting to show it. They’re powerful, but bloated. Too many clicks, too much setup, and systems that are hard to evolve without breaking everything else.
FlowGrid started from a different premise:
What if your CRM worked with natural language and adapted to you instead of the other way around?
The focus is minimalism, privacy, and speed. One or two clicks to do most things. A global search that actually helps. AI that understands your data when you import it, so you’re not spending hours configuring schemas and fields. If you want to change something, you type it, or say it, and move on.
Security was the other non-negotiable. After watching high-profile CRM breaches, I became pretty obsessive about doing this right. FlowGrid encrypts sensitive data by default with tenant scoped encryption keys, isolates tenants properly, and treats security as a baseline, not an enterprise add-on with an enterprise price tag.
The long-term vision is simple: FlowGrid as a stable, secure backend for your business, or even your personal systems, something flexible enough to keep up with how fast tools and workflows are changing.
I’m excited to share this with you, and I’d genuinely love your feedback. Thanks for checking it out and for supporting indie builders 🙏🏿.
CRM adoption usually fails not because of features, but because half the team never properly migrates their data. If the AI scaffolds the workspace from an import — does it handle messy, inconsistent spreadsheets, or does it need clean data to work well?
FlowGrid
@klara_minarikova 👏🏿Good morning from across the pond!
Yes, this is an issue that I am very aware of. If the import process feels fragile, teams disengage fast.
What we ship today covers the essentials. We normalize headers, dedupe keys, trim and skip empty values, prevent cross-sheet object mixing, support fuzzy matching, detect core fields like email and phone, parse multi-selects, and offer optional dedup/update logic. I’d consider these the baseline tools needed to make mapping smooth instead of painful.
In this current version, my primary focus has been reducing friction during setup. The AI helps scaffold structure, but most of the heavy lifting during import is actually deterministic heuristics — intentionally. That’s a privacy decision. I’m very careful about not sending raw spreadsheet data to LLMs, especially when it may contain sensitive information.
Cleaning spreadsheets deeply — anomaly detection, value standardization, inference across rows — can easily become its own standalone product. That’s not something I’m blind to. It’s on the roadmap, and I’ll prioritize it based on real usage demand.
For now, the system is designed so that even imperfect data can scaffold a clean workspace quickly, without requiring full raw-data exposure to AI. As we see how teams use it in the wild, normalization depth will evolve from there.
Of course, if a spreadsheet is truly cursed (i.e. it has been passed around for ten years with merged cells, hidden columns, and mystery formulas) there’s a limit to what any system can automatically rescue with confidence.
That being said, If you’ve seen particular failure patterns during past migrations that are of concern, I’d be curious to know what they were.