New product concept
You know the athlete or coach outcome you want, but need to determine whether existing footage and sensors can support it.
Know if your sports AI idea is worth building.
A focused technical sprint for teams considering a sports AI, video-intelligence, or biomechanics product. We test the riskiest assumptions against your real footage and constraints, then turn the evidence into a buildable plan.
- Typical duration
- 1–2 weeks
- Indicative investment
- from €6,500 excl. VAT
- Primary outcome
- Evidence for a go / change / stop decision
- Working format
- Fixed, focused technical sprint
Will your footage support the product?
A convincing demo can still fail when the camera moves, the ball becomes tiny, players overlap, lighting changes, or the model has to run within a real latency and cost budget. Sports video adds geometry, time, domain rules, and filming behaviour on top of the usual machine-learning uncertainty.
The sprint is designed to surface those constraints early. Instead of producing a broad strategy deck, I work directly with representative data, isolate the riskiest technical assumption, and generate enough evidence to make the next investment decision confidently.
When this is useful
Uncertain prototype
A proof of concept works on selected examples, but nobody has tested filming variation, edge cases, or product-scale constraints.
Build-or-buy decision
You need an independent view of available models, licensing, infrastructure, data requirements, and what truly needs custom development.
What you receive
Data and filming review
- Representative footage assessment
- Signal, label, and edge-case analysis
- Filming guidance and collection gaps
Targeted technical spike
- Test of the riskiest component
- Baseline metrics and failure examples
- Model and system trade-offs
Architecture and roadmap
- Recommended system boundaries
- Risk register and validation gates
- Phased delivery and evaluation plan
How the engagement runs
- 01
Frame the decision
Define the user outcome, current assumptions, available evidence, and the decision the sprint must support.
- 02
Inspect reality
Review representative footage, labels, product constraints, hardware, privacy, and acceptable failure modes.
- 03
Test the risk
Build or benchmark the smallest technical slice that can validate or invalidate the central assumption.
- 04
Make the call
Present findings, failure cases, architecture, risks, and a practical recommendation for what happens next.
Questions we’ll work through
- Can the target events be observed from the intended camera angle?
- Which outputs need detection, pose, tracking, geometry, or temporal models?
- How much annotation is actually required for a credible first version?
- What filming instructions must users follow for reliable analysis?
- Which failure modes can be detected automatically before scoring?
- Can inference meet the target latency, hardware, and cost envelope?
- Which open-source licences are safe for the commercial product?
- What should be proven before committing to an end-to-end build?
From phone video to coach-ready intelligence.
For AIM, the production problem extended far beyond choosing an object detector. Reliable coaching required filming validation, player and ball perception, cone geometry, temporal phase and repetition models, biomechanics, scoring, optimized inference, and a visualization users could understand. That joined-up product perspective is what I bring to feasibility work.
Before we start
Do we need an existing dataset?
No polished dataset is required, but representative examples help. Raw clips, user recordings, prototype outputs, or even a precise filming concept can be enough to determine what evidence needs to be collected first.
Will the sprint produce production code?
The goal is a decision, not a disguised full build. Any code is a focused spike or benchmark used to test risk. If the evidence supports moving forward, the architecture and roadmap define how that work becomes production software.
What if the original idea is not feasible?
That is still a successful outcome if discovered early. The recommendation may narrow the promise, change the filming setup, introduce a human review step, require more data, or stop the build before it consumes a larger budget.
Can the sprint lead into implementation?
Yes. It can stand alone, support another team, or become the first stage of an end-to-end product build. You retain the findings and roadmap regardless of who implements them.
Other ways to work together
What decision is your team trying to make?
Send me the product idea, the data you have, and the part you trust least. I’ll reply personally within one business day with an honest view of whether a sprint is the right next step.
Work directly with Jude Mingay, a freelance AI product engineer based in Freiburg, Germany. Remote projects worldwide; English, German, or Portuguese.