AceSense is built by Akshay Sarode and a small engineering team based in Europe. NVIDIA Inception Member, Almi Backed, with 50 beta players across the EU since the first release in 2025.

Because half the amateur tennis world runs on Android, and the dominant tennis AI app (SwingVision) is iOS-only. The biggest unmet need in the category was an app that takes Android players seriously — so we built that, and added iOS in parallel from the same codebase.

Where is the team based?

Europe. Data is hosted in europe-west1 (Belgium). EU-native pricing, EU data residency, GDPR-compliant by default.

About AceSense — Founder Story and Why Android-First

AceSense is an AI tennis video analysis app built in Europe by Akshay Sarode. NVIDIA Inception Member, Almi Backed, 50 beta players. The story behind it.

The product

AceSense is an AI tennis video analysis app for amateur players (NTRP 3.0-4.5). You film your match on a phone, upload the video, and get an automated coaching report back in minutes — shot detection, ball tracking, court heatmap, stroke-quality scores, and the top three things to work on next session.

It's built mobile-first for both iOS and Android, runs on europe-west1 infrastructure, and publishes its accuracy methodology — three things no other AI tennis app does together.

The founder

Akshay Sarode built the first version of AceSense in 2024-2025 after spending too many evenings filming his own tennis matches on a phone propped against a backpack and getting nothing useful out of the footage. He's an engineer, currently building AceSense full-time from Europe.

The founding observation was simple: amateur tennis players are paying €50-€100/hour for human coaching they only get one or two hours of per week, and the rest of the time they have no feedback loop. Recreational golf has a dozen video-feedback apps; recreational tennis has SwingVision (iOS-only) and a small handful of competitors with weaker accuracy and no Android support. There was a real product-shaped hole.

The first three months of the project were spent figuring out whether the AI could actually be made accurate enough on phone-recorded footage. Not "demo accurate" — honestly accurate, on real-world videos shot at fence-clip height with normal lighting. The answer turned out to be: yes, but only by chaining the right open-source models in the right way (TrackNet for ball tracking, MediaPipe for pose, CatBoost for the temporal classification on top), and only by being relentless about the test set.

The second six months were spent building the test set, the acesense-annotate labelling tool that produces the ground-truth data, the regression suite, and the marketing claims that the accuracy page makes today.

Why Android-first

The standard advice for a small consumer app is: ship iOS first, defer Android. iOS users pay more, the App Store ecosystem is tighter, and the engineering effort is smaller.

We did the opposite. AceSense was built mobile-first for both iOS and Android from a shared Flutter codebase, and we leaned into Android in our early marketing. Three reasons:

The biggest single unmet demand in the category is Android tennis AI. SwingVision has been iOS-only since 2019. The Reddit thread literally titled "Genuinely thinking of getting a iPhone just for the swing[vision]" has been live for two and a half years with no resolution. That demand has nowhere to go.
Half the EU is on Android, and the EU is our primary market. Defaulting to iOS-only would be defaulting to a US-shaped strategy.
It forces clean architecture. Building on Flutter from day one meant we couldn't ship iOS-specific hacks; everything has to work on both platforms identically. That's harder upfront and pays off forever.

This isn't an "Android also" decision. It's an "Android first" decision, with iOS as the equal-quality companion.

What we are, and what we're not

What we are. A small focused team building an honest AI tennis tool for amateur players. We publish accuracy numbers. We charge transparent EU prices. We don't gate the AI behind a paywall — the free tier produces the full report. We treat the coach-handoff workflow as a first-class product feature, not an afterthought.

What we're not. A paddle-sport app. A pro-tour officiating system. A real-time line-calling Apple Watch product. A facility-hardware company. A multi-sport video tool. We chose tennis, chose amateurs, chose phones, and went deep on those.

The numbers (real)

NVIDIA Inception Member. GPU credits and engineering support that made the early model training tractable.
Almi Backed. Early-stage funding from the Swedish state-backed Almi programme.
50 Beta Players. Across the EU, contributing the videos that built the test set and the bug reports that shaped the iOS and Android apps.
Data hosted in europe-west1. EU residency by default. No data leaves the EU.

These four numbers are on the homepage hero for a reason — they're the fastest way to convey "this is real, in operation, with traction." If you want more detail on any of them, the contact page is the right place to ask.

The pipeline (technical heritage)

For readers who want the engineering story: AceSense's pipeline is built on five named, public-lineage models:

TrackNet for ball detection (originally a 2019 paper for badminton, adapted to tennis).
A custom court keypoint detector trained on EU and US amateur footage.
FasterRCNN for player bounding-box detection.
MediaPipe Pose for 33-point body skeletons.
CatBoost for the temporal classification of bounces and shots.

Five models chained, running on RunPod's serverless GPU infrastructure in the EU region, orchestrated by a Firebase Functions backend. The full pipeline is described in how AceSense works; the architecture is documented internally in acesense-docs/architecture/FULL_ARCHITECTURE.md.

What's next

Roadmap (from public commitments on the changelog and current development):

2026 H2 — junior court support (78 ft scaled-down lines, currently a known limitation).
2026 H2 — public benchmark dataset (a 5-hour subset of our test set, with redistribution licences from beta players, so external researchers can reproduce our numbers).
2026 H2 — doubles per-player attribution moving from beta to GA after the next training cycle.
2027 — possibly other racquet sports, but only if we can be confident the model transfers; we won't ship a half-trained model into a different sport.

Things we're explicitly not doing:

Apple Watch real-time line calling. Not in the next 12 months. SwingVision wins that battle.
A coach-marketplace platform.
An AR overlay product.

Stay on the things we're good at, ship the things we said we would, and publish numbers when they move.

How to get in touch

General questions: [email protected]
Press / partnerships: [email protected] — we'll route appropriately
Beta-player programme: apply through the homepage form
Coaching / academy enquiries: contact page

If you want to test the product on your own video, the free tier gives you 3 analyses a month with the full report — no credit card. That's the fastest way to understand what AceSense is.

Read next: How AceSense works · Accuracy methodology · Pricing · Examples · Changelog · FAQ

Frequently asked questions

Who builds AceSense?: AceSense is built by Akshay Sarode and a small engineering team based in Europe. NVIDIA Inception Member, Almi Backed, with 50 beta players across the EU since the first release in 2025.
Why Android-first?: Because half the amateur tennis world runs on Android, and the dominant tennis AI app (SwingVision) is iOS-only. The biggest unmet need in the category was an app that takes Android players seriously — so we built that, and added iOS in parallel from the same codebase.
Where is the team based?: Europe. Data is hosted in europe-west1 (Belgium). EU-native pricing, EU data residency, GDPR-compliant by default.