Clarity AI

Last year I was running growth at my own startup. Every Monday I opened three tabs: Stripe, HubSpot, and a Google Sheet I called "the source of truth." It was not the source of truth. It was a mess. I'd spend two hours reconciling numbers that should have taken two minutes. Then I'd make a decision based on whatever I could remember from the spreadsheet I closed twenty minutes ago.

I talked to other founders. Same story. Stripe doesn't talk to HubSpot. HubSpot doesn't know about Postgres. Nobody knows what's actually happening in the business right now.

The tools that solve this cost $85,000–$120,000 a year — that's a full-time data analyst. Or you buy Looker/Tableau, which takes an engineering sprint to set up and a second sprint every time your schema changes.

So I built something else.

What I built

Clarity AI gives you an AI analyst called Atlas. You connect your data sources — Stripe, HubSpot, PostgreSQL, BigQuery, whatever you're using — and you're up in 60 seconds. No SQL. No dashboards to configure. You just ask Atlas what you want to know.

"Why did MRR drop in March?"
"Which cohort has the highest 90-day retention?"
"How many customers upgraded in Q1?"

Atlas answers in plain English. Not charts you have to interpret. Actual answers with the logic explained.

It also watches your data while you're not looking. Anomaly detected at 2am? You get an alert before your Monday panic.

Pricing is $149/mo for the Starter plan (5 data sources, 3 seats, 2,000 query credits). That's against $85–120K for a full-time analyst. We're not pretending to replace a senior data scientist. We're replacing the person founders wish they could afford.

We have 8 integrations live today: PostgreSQL, Snowflake, BigQuery, Redshift, MySQL, Stripe, HubSpot, and Google Sheets. Zapier and REST API for anything else.

The product is live at clarity-ai.polsia.app. 14-day free trial, no card required.

What I want feedback on

Three things I'm genuinely uncertain about:

1. Positioning. Right now I'm leaning into "replaces the analyst you can't afford." But a few early users said they think of it more like "always-on business intelligence." Are those the same buyers or different ones?

2. Pricing. $149/mo feels right for solo founders, but teams keep asking about $499. Is that a "team" tier or should I just let usage drive it?

3. ICP. My gut says non-technical founders of B2B SaaS doing $5K–$150K MRR. But I'm seeing operators at $500K+ MRR come in too — they just don't want to deal with their data team for every question. Worth chasing?

If you've drowned in dashboards, reply and tell me what tool broke first.

Most "AI analytics" tools launching in 2026 are just Tableau with a chat interface slapped on top.

I've been tracking the space closely as we build Atlas. Every week brings another "revolutionary AI analyst" that requires you to know your table schemas, understand joins, and basically think like a data engineer. They took the same complex foundations and added conversational UI as lipstick on a pig.

Real AI analytics should make the complexity invisible. When a hotel owner asks "why did revenue drop last month" they shouldn't need to know that bookings live in one table, cancellations in another, and room rates in a third. The AI should figure that out.

We're seeing this clearly with our beta customers. The ones coming from traditional BI tools spend the first week trying to recreate their old dashboards in Atlas. Then something clicks. They start asking business questions in plain English instead of translating their thoughts into database queries.

The winners in this space won't be the ones with the flashiest demos. They'll be the ones that actually eliminate the need to think like a machine.