Turn an AI opportunity into a costed plan for production.
In one week, we help you move from AI confusion, a stalled pilot, or a promising idea to a clear implementation plan: what should be built, what metric it should move, what it may cost, and whether it is worth doing now.
Fixed price. One week. No generic strategy deck.
From AI idea to production plan
AI creates value when it changes how work gets done - and when the system actually ships.
The Discovery Sprint is designed to answer one practical question: Is this AI opportunity worth building, and what would it take to make it real?
Painful process
What it usually meansWork is slow, manual, inconsistent, or hard to controlWhat Discovery clarifiesWhere AI can change the workflow and which quick wins matter first
Specific AI idea
What it usually meansYou already have an automation, internal tool, or product ideaWhat Discovery clarifiesFeasibility, scope, data needs, MVP path, and delivery risk
Stalled initiative
What it usually meansA previous AI effort produced a demo, slides, or a prototype but did not shipWhat Discovery clarifiesWhat blocked it and how to reset toward production
Leadership needs a business case
What it usually meansThe opportunity may be real, but budget approval needs numbersWhat Discovery clarifiesROI model, roadmap, delivery plan, and executive-ready recommendation
The goal is not to create another AI concept. The goal is to know what should change, what should be built, what it should return, and how to move it into production.
The problem
AI interest is not the same as AI impact
Many companies already have AI activity: tools, experiments, pilots, workshops, and internal champions. But the business impact often remains unclear. Sound familiar?
You know AI matters, but not where it will actually move a metric.
There are many possible use cases, but no clear ranking by impact, effort, feasibility, or ROI.
You have a promising idea, but not a buildable plan.
The concept sounds valuable, but the MVP scope, data requirements, system design, and delivery path are still unclear.
A previous pilot did not scale.
A prototype worked in a demo, but it never became a reliable workflow, internal tool, automation system, or production product.
Leadership needs a business case.
The team needs more than enthusiasm. They need a costed plan, ROI logic, risks, and a practical path to implementation.
The process
Five days. Three phases. Zero wasted time.
Days 1-2
Discovery & Mapping
We map the business reality behind the AI opportunity.
- Stakeholder interviews with leadership and role owners
- Workflow and process mapping
- Pain point and bottleneck catalogue
- Tool and data landscape review
- Metrics baseline: cycle time, volume, cost, quality, capacity, or rework
- Existing AI activity and stalled-pilot review, if relevant
Days 3-4
Analysis & Scoring
We turn observations into ranked opportunities.
- AI opportunity identification
- Impact, effort, confidence, and risk scoring
- Feasibility review: data, integrations, workflow complexity, security, and adoption
- ROI model per priority initiative
- MVP scope and delivery path
- Build / buy / automate / wait recommendation
Day 5
Delivery & Handoff
We deliver a decision-ready plan.
- Executive presentation
- Prioritized roadmap
- MVP recommendation
- Working prototype walkthrough
- ROI and cost model
- Delivery risk register
- Live Q&A and next-step alignment
What you get
Seven deliverables. Yours to keep.
Everything you need to make decisions and take action.
Pain Points & Process Reality Map
A practical map of where work slows down, where decisions get stuck, where manual effort repeats, and where AI could realistically change the workflow.
AI Opportunity Portfolio
A ranked set of AI opportunities across workflows, internal tools, automation systems, or AI-enabled product ideas - scored by impact, effort, feasibility, and confidence.
Top Quick Wins + ROI Logic
The highest-leverage opportunities, with clear assumptions behind the expected return: cycle time, team capacity, quality / consistency, cost, throughput, or rework reduction.
MVP Scope & Production Path
A clear definition of what should be built first, what should stay out of scope, what data and integrations are required, and what it would take to move from MVP to production.
Working Prototype
A functional prototype of the priority initiative - enough to validate the core workflow, demonstrate feasibility with your data and context, and de-risk the production build. Not a production system, but a tangible artifact that proves the concept works and gives stakeholders something real to react to, not just slides.
90-Day Implementation Roadmap
A practical sequence of work: first experiments, build milestones, ownership, risks, dependencies, and decision gates.
Executive Report
A board-ready summary with the business case, recommended path, cost logic, risks, and go / no-go recommendation.
What changes after Discovery
Before Discovery, AI may feel like a list of ideas, tools, or experiments. After Discovery, you know:
Which AI opportunities are worth pursuing first
Which ideas are not worth building yet
What metric the priority initiative should move
What MVP should be built
What the prototype reveals about feasibility and risk
What data, tools, and integrations are required
What risks could block delivery
Whether to build internally, work with us, or pause
You leave with a plan you can act on - not just a strategy document.
Why this is different
Most AI strategy work stops at recommendations. We connect strategy to delivery. That means we do not only ask, "Where could AI help?" We ask:
What workflow should change?
What system needs to exist?
What metric should move?
What would make this fail in production?
What should be built first?
What should not be built yet?
If the opportunity is real, the Discovery Sprint prepares it for production AI delivery: workflows, internal tools, automation systems, or AI-enabled products that can run inside the business.
Investment
Flat fee. One week. Fixed scope. No hidden costs. No scope creep.
By comparison
Traditional consulting often stretches diagnosis across multi-month assessment phases. We compress the decision work into one focused sprint: enough depth to make a serious decision, without turning discovery into a long engagement.
You will leave the sprint with a decision-ready AI opportunity map.
By the end of the Discovery Sprint, you will receive a prioritized list of AI opportunities, clear feasibility notes, expected business impact, implementation risks, and recommended next steps.
The goal is simple: give your leadership team enough clarity to decide what is worth building, what should wait, and what should be avoided.
After Discovery
Three paths. All yours to choose.
Execute internally
Take the roadmap and run with it. The deliverables are designed to be usable with your own team.
Move into production AI delivery
If the opportunity is strong and you want us to build it, we can continue from the Discovery context into implementation: AI workflows, internal tools, automation systems, or AI-enabled products that run in the business.
Pause or wait
Sometimes the right answer is to clarify ownership, improve data access, define metrics, or wait until the timing is better. The plan is yours regardless.
Is this right for you?
Good fit
- You have AI interest, budget, or pressure, but no clear implementation plan.
- You have a painful workflow and want to know whether AI can materially improve it.
- You already have an AI product, automation, or internal tool idea and need to validate if it is buildable.
- You tried an AI pilot that did not scale into production.
- Leadership needs a business case before approving investment.
- You want a roadmap you can execute with or without us.
- You completed the free Snapshot and now want the full picture.
Not a fit
- You only need a generic AI workshop or training session.
- You want staff augmentation without strategic or product discovery.
- You want someone to code a quick proof of concept without validating the business case.
- You expect a full production system to be built inside a one-week Discovery Sprint.
- You do not have a business process, pain point, or AI idea to examine.
Which engagement is right for you?
| AI Opportunity Snapshot | Discovery Sprint | Production AI Delivery | |
|---|---|---|---|
| Price | Free | $5,000 | From $10,000/month |
| Duration | 30 minutes | 1 week | 3-6 months |
| Best for | Unsure where to start or validating if there is a real opportunity | Ready to define the opportunity properly | Ready to ship a system that runs in the business |
| What you get | Opportunity read + named quick wins + go / no-go | Validated opportunity, MVP scope, ROI model, prototype, roadmap, and delivery plan | Production AI workflow, internal tool, automation system, or AI-enabled product running in your business |
| What changes | You know what is worth exploring next | You know what to build, what it may cost, and what it should return | A business metric moves: cycle time, team capacity, or quality / consistency |
| Depth | Directional | Strategic and technical | Implementation and operationalization |
| Next step | Go alone, Discovery, MVP path, or pause | Build internally or with us | Scale, optimize, or hand off |
FAQ
Answers to what clients ask before getting started, or before deciding to continue.
Ready to move from AI uncertainty to a production plan?
$5,000 flat. One week. Decision-ready roadmap, MVP scope, working prototype, ROI logic, and implementation path.