Start with a business metric
Cycle time, skilled hours, error rate, backlog, or response quality—not “number of AI features.”
10-working-day Workflow Proof Sprint
Prove one AI workflow before you fund the build.
PitchAI turns one costly, repeatable process into a controlled working slice—with a baseline, edge-case tests, human review, and a clear go/no-go decision.
No production access required for the first pass. Sanitized representative data is welcome. If AI is the wrong tool, the answer can be “do not build.”
One accountable workflow owner
Human approval by default
Visible failures and limitations
Belgium + Netherlands
Why this exists
Many AI pilots prove possibility. They do not prove usefulness.
The hard questions arrive after the demo: Who owns it? What happens on bad data? What stays human? Does it save enough time to justify production? The sprint answers those questions before a larger commitment.
Test the riskiest assumption
Data quality, integration, output quality, or user review is tested early enough to stop responsibly.
Leave with a decision
Build, redesign, or stop—with evidence, constraints, and a costed next phase your team can review.
The ten-day path
Fast enough to create urgency. Structured enough to trust.
The sprint uses a representative workflow and data sample. Full rollout starts only after the evidence gate passes.
Baseline
Map the current process, owner, inputs, failure modes, time cost, and one measurable target.
Working slice
Build the smallest end-to-end loop on representative data, including the human review step.
Evaluation
Run normal, edge, and failure cases. Record quality, exceptions, latency, cost, and manual effort.
Decision
Demonstrate the evidence, document limits, and deliver the go/no-go production plan.
What you keep
A proof ledger your business and technical owners can challenge.
Every output is designed to survive the handoff from an enthusiastic demo to a real operating decision.
- Baseline sheet: current effort, delay, rework, error classes, and target metric.
- Workflow and ownership map: sources, handoffs, permissions, decisions, and owner.
- Controlled working slice: a complete representative loop, not a disconnected mock-up.
- Evaluation set and results: normal cases, edge cases, expected failures, and observed limits.
- Human-control design: approval, correction, escalation, and audit boundaries.
- Go/no-go roadmap: integration path, rollout phases, operating model, risks, and estimate.
QuestionEvidenceDecision rule
Does it save meaningful work?
Before/after handling time on representative cases.
Target improvement agreed before build.
Can users trust it?
Evaluation results, evidence links, corrections, and visible limitations.
High-impact outputs stay behind human approval.
Can it survive operations?
Failure handling, ownership, access path, cost, and monitoring design.
No silent fallback and no undefined owner.
Good fit / wrong fit
Use AI where the workflow earns it.
We target knowledge-heavy processes in operations, reporting, assessments, retrieval, document work, and data analysis. We do not force an agent into every problem.
A strong sprint candidate
- Consumes at least five skilled staff-hours each week
- Repeats often enough to measure before and after
- Uses documents, messages, reports, or operational data
- Has an accountable business owner
- Can keep a human at the important decision boundary
- Has representative data or realistic sanitized samples
We should pause or say no
- The problem is only “we need an AI strategy”
- No one owns the current process or target metric
- There is no accessible data and no credible sample
- The goal is a high-impact autonomous decision without review
- The business case depends on unsupported certainty or certification claims
- A simple rule, form, or process change solves it more safely
Private readiness self-check
What needs evidence before a proof sprint?
This indicative checklist surfaces readiness gaps; it does not decide eligibility or scope. Your answers stay in this browser. Only the broad result band is counted for campaign measurement.
Commercial shape
Fixed scope. No open-ended discovery meter.
The first workflow, evidence target, data boundary, and decision owner are agreed before kickoff. Full production rollout is a separate decision after the proof gate.
Request a fit check
Proof sprints start at
€5,000
excluding VAT. Final fixed price and scope are confirmed in writing before kickoff.
Questions
The useful objections, answered plainly.
Does this guarantee a production system in two weeks?
No. The engagement covers a fixed-scope evidence process and the deliverables agreed in writing. The working slice may run in a controlled environment on representative data. Production integration and rollout are scoped after the proof gate unless explicitly included.
What if AI is not the right tool?
That is a valid outcome. The sprint should stop weak ideas before they become expensive implementations. We document whether a deterministic rule, process change, existing product, or no build is better.
Do we need to share production data?
Not for the first pass. Representative, minimized, or sanitized data is often enough to test the riskiest assumptions. Any personal or sensitive data requires a defined processing and access path.
Which platforms do you use?
We design around the workflow and existing stack. Depending on constraints, that can include Microsoft 365, internal databases, APIs, document stores, rules, and multiple model providers. We do not market third-party certification unless it is documented.
Who is the sprint for?
Operations leaders, managing partners, IT/data leads, and workflow owners in knowledge-heavy Benelux organizations—especially teams dealing with recurring reporting, assessment, retrieval, document, or analysis work.
Start with the current cost
Your weekly report may cost more than the software.
Use the private calculator to estimate the annual Copy/Paste Tax, then decide whether the workflow deserves a proof sprint.