- AI Quick Start
A working AI prototype in 14 days — built on your real business data.
Bring us a real business problem. We’ll test it against your workflows, build a working AI prototype, and tell you clearly whether it’s worth taking further. No vague AI strategy decks. No endless pilot phase. Just a fast, structured way to prove what’s real before you commit bigger budget or time.
Less Guesswork. More Working AI.
A working prototype
See your AI idea in action through a functional prototype built around real workflows, real business context, and real data — not a throwaway demo.
- Functional prototype, not a mockup
- Built around your real data
- Ready for team feedback
A use-case audit
We identify the highest-value opportunities, pressure-test the workflow, and help prioritise what is genuinely worth building first.
- High-impact use cases identified
- Impact vs. effort evaluation
- Clear priorities for what comes next
A scope-to-production plan
If the prototype proves value, you leave with a practical next-step plan covering scope, timeline, delivery model, technical direction, and likely investment.
- Clear technical architecture
- Defined timeline and milestones
- Fixed-scope project estimate
An honest verdict
If AI is not the right answer yet, we will say so. The goal is clarity, not dragging you into a bigger build that should never have started.
- Practical feasibility assessment
- No unnecessary AI adoption
- Clear recommendation on next steps
14 days. One working AI prototype. A clear decision at the end.
This is a focused sprint designed to move quickly without becoming careless. Every step exists to reduce uncertainty and get you to something real.
01
Day 1 - Kickoff
Define the problem. Align on the outcome.
We identify the highest-value use case, understand the workflow, review the available data, and align on what success should look like by Day 14.
02
Days 2–3 - Data
Prepare what powers the prototype.
We connect the relevant sources, clean what is necessary, and structure the data needed to make the prototype useful in a real business context.
03
Days 4–7 - Build
Build the first working version.
A senior engineer leads the prototype build using your real data and use case. You see progress as it happens. No hidden weeks. No “trust us, it’s coming.”
04
Day 8 - Demo 1
See it working with your own context.
We put the first version in front of your team, gather feedback, and identify what needs tightening before the final round.
05
Days 9–12 - Iterate
Refine it into something more useful.
We test against real scenarios, improve the flow, tighten the output quality, and make the prototype more aligned to how your team would actually use it.
06
Day 13 - Audit
Identify what else AI could improve.
We map additional AI opportunities across your workflows and prioritise the strongest next use cases by impact, complexity, and business fit.
07
Day 14 - Verdict
Get the prototype, the plan, and the truth.
You leave with the working prototype, a practical path to production, and a clear recommendation on whether the use case should move forward.
- AI Transformation
Start With Proof, Not Promises
Here's the path from here
The Quick Start is not designed to trap you in a bigger engagement. It exists so you can make the next move with more confidence.
Sprint
Starts from $000.00
4–6 weeks • fixed scope • fixed price
If the prototype proves value, we can take it into a more structured sprint to turn it into a production-ready internal tool, feature, or workflow system.
AI Embed
Starts from $000.00
Monthly ongoing
If you need continuous AI development, experimentation, and improvement, we can embed a senior AI engineer and supporting team into your roadmap.
- Most Popular
Walk away
$0.00
No forced next step
If the Quick Start shows that AI is not the right move yet, that is still a successful outcome. You keep the learning, the prototype, and the clarity.
Why teams trust PedalsUp with early AI decisions
This is not just about building quickly. It is about building something useful enough to make a confident decision.
We focus on real business problems
The best AI quick wins come from practical workflows, not abstract innovation ideas. We start there.
We build on your real data
The value of AI usually depends on context. That is why the prototype is built around your business inputs, not generic sample data.
We are upfront about what is worth building
PedalsUp’s AI positioning is not “AI everywhere.” It is “AI where it does a real job.” That honesty is part of what makes the offer trustworthy.
We are engineers first
This is not a consulting-only engagement. It is an execution-led validation sprint designed to produce something working, not just something well-presented.
We think beyond the prototype
From the start, we are already considering what production would require: data, workflow fit, architecture, reliability, ownership, and cost.
We keep the process clear
A fast AI offer only works if the buyer always knows what is happening, what is being tested, and what the outcome means.
Question you might have,
Answers we definitely got
AI Quick Start is a 14-day sprint designed to validate an AI use case using your real business data, resulting in a working prototype, a use-case audit, and a practical next-step recommendation.
It is ideal for teams that want to test whether AI can solve a real operational, product, support, research, or workflow problem before committing to a larger build.
Not necessarily. The current offer is already positioned around testing what is possible with the data you actually have and being honest if the condition of the data becomes a blocker early.
Both, but weighted toward action. The sprint includes strategic validation, but the outcome is a working prototype — not just a recommendation document.
That is still a useful result. You leave with better clarity, a more informed decision, and no pressure to force the wrong solution into production.
Yes. If the use case proves valuable, the Quick Start can move into a sprint, AI Embed, or broader AI product development path.
Internal copilots, document-heavy workflows, support assistants, knowledge retrieval, workflow automation, research tools, and AI-enabled operational systems are usually strong candidates.
Because many AI projects fail on weak assumptions. Quick Start helps reduce that risk before more money and time are committed.
- It's your time to grow
Start small. Prove value. Scale what works.
If you are exploring AI but want a faster, lower-risk way to find out what is actually worth building, start here. We’ll help you test the use case, build the prototype, and make the next decision with more confidence.
