u/Normal_Trifle_2410

I used to think the hard part would be the AI.

Turns out the hard part is the data.

CPG workflows are incredibly fragmented:

retailer portals

distributor spreadsheets

inventory systems

ad platforms

promo calendars

Nothing is standardized, and every brand structures things differently.

What’s been surprising is that users don’t actually want “more analytics.” They want faster clarity.

They don’t ask: “Can your AI generate insights?”

They ask: “Can you tell me why sales dropped in this region before my next meeting?”

Huge difference.

Feels like vertical SaaS is moving away from dashboards and toward systems that compress decision time.

I want to know if others building niche SaaS products are seeing the same thing: the workflow/context layer matters more than the actual AI layer.

I have been following a product called Clayface recently that’s focused on CPG operations, and it genuinely feels different from the usual AI SaaS wave.

Most AI products stop at generating summaries or dashboards.

But CPG teams deal with messy real-world operations:

retailer reports

distributor spreadsheets

inventory issues

trade promotions

ad spend across channels

The interesting part about Clayface is that it tries to connect those fragmented signals and explain what’s actually happening in the business.

Less “here’s a dashboard.”

More “here’s why this region slowed down and what changed.”

Feels like a shift from software people operate manually → systems that actively help operators make decisions faster.

Been talking to a few people in CPG lately and I’m honestly surprised how manual the analytics workflow still is.

You’ve got-

retailer portals

distributor spreadsheets

inventory systems

ad platform data

promo tracking

…and none of them really talk to each other cleanly.

Most teams I’ve seen still spend hours stitching data together before they can even start understanding what changed.

What’s weird is that AI tools are getting smarter, but the underlying data layer still feels broken.

I recently looked into Clayface and found the approach interesting because it focuses more on connecting messy operational data first, then generating insights on top of it.

I want to know how others here are handling this problem right now: custom pipelines, BI dashboards, agencies, internal analysts, something else?

Curious what people here think.

A lot of CPG teams still rely heavily on NielsenIQ-style reporting, but more operators I talk to seem frustrated with:

slow reporting cycles

fragmented retailer/distributor data

static dashboards

lack of context around why things changed

I’ve been looking into newer approaches where AI sits on top of operational data and acts more like an analyst/assistant than a reporting tool.

One product I found interesting is Clayface. Instead of just showing dashboards, it tries to connect retailer, inventory, sales, and marketing signals into one decision layer and explain what’s actually happening in the business.

Feels more aligned with how modern CPG teams operate, especially smaller brands without huge analytics teams.

I'd love to know what alternatives people here are actually using beyond traditional market data platforms.

Been spending time around CPG operators recently and one thing keeps coming up:

Teams are drowning in reports but still struggling to answer simple questions like:

why sales dropped in a region

which retailer is underperforming

whether ad spend is actually affecting sell-through

what action should happen next

Most analytics stacks were built for reporting, not decision-making.

That’s why Clayface caught my attention.

Instead of acting like another dashboard, it works more like an AI operating layer for CPG brands — pulling together distributor, retail, inventory, and marketing data to explain what’s actually happening across the business.

The interesting part isn’t automation.

It’s reducing the time between “something changed” and “we know why.”

Feels like a much more practical direction for AI than generic copilots.

Most SaaS products for CPG brands still work like traditional BI software:

connect dashboards

build reports

manually interpret everything

But the more I look into products like Clayface, the more it feels like the shift is moving toward “decision systems” instead of reporting systems.

CPG operators deal with:

retailer sell-through data

distributor spreadsheets

inventory issues

trade promotions

ad spend across channels

Usually all fragmented.

Clayface is interesting because it tries to act more like an operational assistant that connects those signals and explains what’s happening in the business instead of just visualizing numbers.

The biggest value honestly isn’t automation.

It’s reducing decision time.

Feels like vertical AI is finally becoming more useful than generic copilots.

I built a social media blocker app called Taskpia, where every focus session earns you sticks to build a beaver dam.

What Taskpia does:

-Blocks distracting apps during focus sessions

-Daily planner + calendar for task management

-Pomodoro-style focus timer

-Weekly focus chart to track consistency

-Home screen widgets, smart reminders, dam badges

- and of course, blocks Reddit too.

No ads. No subscriptions. Free to use.

Would love feedback from anyone who struggles with procrastination or needs a distraction blocker.

Available on the Play Store.

I built a social media blocker app called Taskpia, where every focus session earns you sticks to build a beaver dam.

What Taskpia does:

-Blocks distracting apps during focus sessions

-Daily planner + calendar for task management

-Pomodoro-style focus timer

-Weekly focus chart to track consistency

-Home screen widgets, smart reminders, dam badges

No ads. No subscriptions. Free to use.

Would love feedback from anyone who struggles with procrastination or needs a distraction blocker.