QA has always been a pain. AI coding just made it 100x worse.
The bottleneck isn't engineering anymore.
The Problem
Write tests faster.
Run them reliably.
Self-healing.
The new breed of QA tools promise to close the gap.
The Problem
Write tests faster.
Run them reliably.
Self-healing.
Your QA budget doubled. Your confidence didn't.
They sell software. You still do the work.
The Solution
Pie
Product Intelligence Engine
Point us at your product. We tell you what's broken.
We build the test plans.
Full coverage across every flow, every platform. No scripting.
We run them continuously.
Every commit, every release. Vision-based—adapts to UI changes automatically.
You fix verified issues.
Prioritized bugs with screenshots, videos, and suggested fixes. Go or no-go in seconds.
That's it. That's QA now.
Product
From code to confidence. 30 minutes.
1. Connect
2 min
Point Pie at your app. No SDK, no code changes. Works with any stack—web, iOS, Android.
2. Autonomous Analysis
25 min
AI agents explore your app, generate comprehensive tests, and run them across configurations. No scripting required.
3. Ship Decision
Instant
Check the issues page. Prioritized bugs with screenshots, videos, and suggested fixes. Go or no-go in seconds.
The issues page customers rely on:
O 142 tests passed
? 3 tests adapted (UI changed, logic didn't)
X 1 real bug found: "Checkout fails when cart >10 items"
- Screenshot + video attached
- Priority: High
- Suggested fix: pagination overflow
False positives: 0 | Decision: Ready to ship (with 1 fix)
Proof
Our customers don't just test faster. They ship faster.
$200K
ARR
$90K
Largest ACV
0%
False Positives (90 days)
F500
In Pilot
Tilt (Fintech) - $90K ACV
Before: 4-6 hrs manual regression/release
After: Zero manual intervention to App Store
Continuous deployment unlocked
Expanding: 1 app → 4 apps
Fi (Consumer HW) - $36K ACV
Before: 2-3 days QA, 12+ engineers
After: Hours, not days. 3 people.
Same-day releases now possible
Expanding: Mobile → e-commerce web
DICK'S Sporting Goods - Fortune 500 Pilot
Market Cap
$16.8B
Status
Through Security
Test Cases
300+ importing
Impact
Kills concentration
Timing
Three forces converging. One window.
1. AI coding hit the enterprise
1.8M+ Copilot subscribers
Cursor: Fastest-growing IDE ever
40%+ of commits AI-assisted
That wall is us.
2. Legacy QA is broken
2000-2020
Selenium
Worked
2020-2024
Low-code
Struggling
2025+
???
Broken
Can't be patched. Needs new architecture.
3. Money moved to applications
Foundation models → Application layer → High-value workflows.
Pie is the application layer for the most broken workflow in software.
The window is now.
Market
$50B market. Actively breaking.
Software Testing & QA
$51B→$107B
11.3% CAGR
2025 → 2032 (Markets and Markets)
Test Automation
$35B→$77B
16.8% CAGR
2025 → 2030 (Coherent Market Insights)
$15B
Enterprise QA Tools
Primary Target
$5B
AI DevTools
Emerging
$20B
QA Services
Replacement
Competition
The only autonomous QA platform.
Capability
Pie
BrowserStack
Mabl
Rainforest
Setup Time
30 min
Instant
Weeks
Days
Cross-Stack
All stacks
Infra only
Web only
Limited
Vision-Based
O
X
X
X
AI-Refactor Resilient
O
N/A
X
Partial
The flywheel they can't replicate
1
Data Flywheel: Every test run trains our models
2
Cross-Platform Memory: Stateful understanding. Not just vision.
3
Production Gate Lock-In: Ripping us out is politically expensive
By the time they rebuild, we own the data and the relationships.
Business Model
Revenue scales with delivery velocity.
Starter
$500
/month
Up to 1,000 test runs
Growth
$2,000
/month
Up to 10,000 test runs
Enterprise
Custom
Avg $60-100K ACV
Unlimited + SLA + Support
The Velocity Flywheel
Customer adopts AI coding →
Ships more features →
Runs more tests →Pie revenue grows
Every feature shipped generates verification demand
Expansion Evidence
Tilt: 1 app → 4 apps (4x potential)
Fi: Mobile → Web (cross-platform)
Both expanding within 6 months
Team
Built by engineers who lived this problem.
Dhaval Shreyas
CEO
Square → Instacart → Meta. CTO/Co-Founder @ Cue. M.S. CS, CU Boulder. Built mobile at Square. Saw QA become the bottleneck. Had to fix it.
Jinoo Jain
CPO
OpenSpace → Nightingale (drones). NASA Ames; UC Berkeley Robotics. Shipped AI that sees the world. Pie applies vision to testing.
Adithya Aggarwal
CTO
M.S. CS (ML), ASU. ACM published. Built face recognition & visual ML. Career in visual recognition. Testing should work like humans see. Built the engine.
~18 people. Heavy R&D = building the moat.
Engineering (14)
Product (2)
GTM (2)
Proprietary vision engine. Built from scratch. Not a GPT-4V wrapper.
Enterprise DNA. Closed $90K against cheaper incumbent. We know how to sell.
The Ask
$3.5M to own autonomous testing.
Round Details
Raising
$3.5M - $4M
Valuation
$18-22M Pre
Instrument
Priced / SAFE
Timeline
Q1 2026
Why this valuation: AI DevTools median $18-25M pre. Proprietary vision engine, F500 in pilot, both customers expanding.
Use of Funds
Sales & Marketing50%
R&D35%
G&A15%
18-month milestones → Series A ready
$200K →
$1.5-2M
ARR
5 →
15-20
Customers
45% →
<20%
Concentration
18 →
30-35
Team
18 months runway . Seeking lead with Agentic AI / DevTools thesis
QA is Dead. 💀
Meet
We don't sell a QA tool. We make the problem disappear.
